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Translations will automatically update with the README. --\u003e\n  \u003cp align=\"center\"\u003e\n    \u003ca href=\"https://zdoc.app/de/ai-boost/awesome-prompts\"\u003eDeutsch\u003c/a\u003e |\n    \u003ca href=\"https://zdoc.app/en/ai-boost/awesome-prompts\"\u003eEnglish\u003c/a\u003e |\n    \u003ca href=\"https://zdoc.app/es/ai-boost/awesome-prompts\"\u003eEspañol\u003c/a\u003e |\n    \u003ca href=\"https://zdoc.app/fr/ai-boost/awesome-prompts\"\u003efrançais\u003c/a\u003e |\n    \u003ca href=\"https://zdoc.app/ja/ai-boost/awesome-prompts\"\u003e日本語\u003c/a\u003e |\n    \u003ca href=\"https://zdoc.app/ko/ai-boost/awesome-prompts\"\u003e한국어\u003c/a\u003e |\n    \u003ca href=\"https://zdoc.app/pt/ai-boost/awesome-prompts\"\u003ePortuguês\u003c/a\u003e |\n    \u003ca href=\"https://zdoc.app/ru/ai-boost/awesome-prompts\"\u003eРусский\u003c/a\u003e |\n    \u003ca href=\"https://zdoc.app/zh/ai-boost/awesome-prompts\"\u003e中文\u003c/a\u003e\n  \u003c/p\u003e\n  \u003cp align=\"center\"\u003e\n    \u003ca href=\"https://awesome.re\"\u003e\u003cimg src=\"https://awesome.re/badge.svg\" alt=\"Awesome\" /\u003e\u003c/a\u003e\n    \u003ca href=\"http://makeapullrequest.com\"\u003e\u003cimg src=\"https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square\" alt=\"PRs Welcome\" /\u003e\u003c/a\u003e\n  \u003c/p\u003e\n\u003c/div\u003e\n\n---\n\nThe prompt engineering world has split into two camps:\n\n- **Camp 1 — Prompt templates**: collect system prompts, share copy-paste recipes, curate persona prompts. Useful, but limited.\n- **Camp 2 — Prompt as engineering**: compile LM programs (DSPy), test and regress prompts (promptfoo), control generation structurally (Guidance), optimize prompts automatically (TextGrad, GEPA). This is where the long-term value is.\n\nThis repo covers both. The engineering camp gets more space.\n\n---\n\n## Table of Contents\n\n- [📋 Prompts](#prompts) — copy-paste ready\n  - [Coding \u0026 Development](#coding--development)\n  - [DevOps \u0026 SRE](#devops--sre)\n  - [Data Engineering](#data-engineering)\n  - [AI \u0026 ML](#ai--ml)\n  - [Product \u0026 Strategy](#product--strategy)\n  - [Project Management](#project-management)\n  - [Healthcare \u0026 Clinical](#healthcare--clinical)\n  - [Industrial \u0026 Automotive](#industrial--automotive)\n  - [Legal \u0026 Compliance](#legal--compliance)\n  - [Knowledge \u0026 Documentation](#knowledge--documentation)\n  - [Writing \u0026 Academic](#writing--academic)\n  - [Learning \u0026 Education](#learning--education)\n  - [Research \u0026 Analysis](#research--analysis)\n  - [Productivity \u0026 Tasks](#productivity--tasks)\n  - [Safety \u0026 Compliance](#safety--compliance)\n  - [Meta \u0026 Prompt Engineering](#meta--prompt-engineering)\n  - [Image, Video \u0026 Audio Generation](#image--video--audio-generation)\n  - [Creative \u0026 Role-play](#creative--role-play)\n  - [Game Development](#game-development)\n  - [Translation](#translation)\n  - [Legacy (2023 era)](#legacy-2023-era--kept-for-reference)\n- [🔬 Frameworks](#frameworks) — the engineering camp\n  - [Prompt Programming](#prompt-programming)\n  - [Automatic Prompt Optimization](#automatic-prompt-optimization)\n  - [Tool Use \u0026 Reliability](#tool-use--reliability)\n  - [Eval \u0026 Testing](#eval--testing)\n  - [Red Team \u0026 Security](#red-team--security)\n  - [Eval \u0026 Observability](#eval--observability)\n  - [Low-Code \u0026 Workflow Platforms](#low-code--workflow-platforms)\n- [🕵️ System Prompt Leaks](#system-prompt-leaks) — learn from production\n- [🧠 Prompt Engineering](#prompt-engineering) — techniques \u0026 defense\n- [🔭 Context Engineering](#context-engineering)\n- [🤖 Agent Ecosystem](#agent-ecosystem) — MCP, Skills, Harness\n- [📖 Official Guides](#official-guides)\n- [📄 Papers](#papers) — Foundations, Optimization, Reasoning, RAG, Agents, Multi-Agent, Safety, Self-Improving Agents, Tool Use, Evaluation, Memory, Multimodal\n- [🛠 Tools \u0026 Libraries](#tools--libraries)\n\n---\n\n## Prompts\n\nAll prompts are open — click, copy, use directly.\n\n### Coding \u0026 Development\n\n| Name | Description | Prompt |\n|------|-------------|--------|\n| 🤖 Agentic Coder | Plan-first coding agent — security checklist, test discipline, PR summary format (2025) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/agentic_coder.txt) |\n| 📋 Improve Audit Planner | Codebase audit → self-contained plans → cheap-executor dispatch — nine-dimension audit with `file:line` evidence, machine-checkable verification gates, isolated worktree execution, and backlog reconciliation; based on shadcn/improve (MIT, 8.6k+ stars, June 2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/shadcn_improve_audit_planner.txt) |\n| 🔔 Proactive Coding Agent Architect | Design coding agents that notice what matters before being asked — reactive / scheduled / situation-aware levels, insight policy (monitor → evaluate → decide → ground → adapt), emission gates, developer context model, and feedback-driven learning; based on \"Agentic Coding Needs Proactivity, Not Just Autonomy\" (arXiv 2605.06717, 2026) and Google's Jules evaluation work (June 2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/proactive_coding_agent_architect.txt) |\n| 🪿 Goose AI Engineering Agent Operator | Vendor-neutral open-source AI engineering agent operator — MCP-native extension discipline, plan-then-execute loops, multi-provider awareness, least-privilege permission model; based on block/goose → aaif-goose/goose under the Linux Foundation Agentic AI Foundation (Apache-2.0, ~50k stars, June 2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/goose_ai_engineering_agent_operator.txt) |\n| ♊ Gemini CLI Prompt Architect | Gemini-CLI-optimized prompt engineer — four-element task prompts (goal/context/constraints/done-when), GEMINI.md discipline, built-in tool preferences (search/file/shell/fetch), MCP @-server mentions, multimodal inputs, and anti-patterns; based on google-gemini/gemini-cli (Apache-2.0, 105k+ stars, 2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/gemini_cli_prompt_architect.txt) |\n| 🛠 OpenAI Codex CLI Prompt Architect | Codex-optimized prompt engineer — four-element task prompts (goal/context/constraints/done-when), AGENTS.md discipline, tool preferences, and anti-patterns; based on OpenAI's official Codex Prompting Guide (Feb 2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/openai_codex_cli_prompt_architect.txt) |\n| 🖥 Cline Prompt Architect | Cline-optimized prompt engineer — four-element task prompts (goal/context/constraints/done-when), Plan/Act mode discipline, `.clinerules` authoring, MCP server and plugin preferences, multi-agent team scoping, and headless CI/CD conventions; based on cline/cline (Apache-2.0, 64k+ stars, 2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/cline_prompt_architect.txt) |\n| 🔱 Grok Build Prompt Architect | Grok-Build-optimized prompt engineer — four-element task prompts (goal/context/constraints/done-when), AGENTS.md / CLAUDE.md project-rule discipline, `.grok/skills/` authoring, TUI slash commands (`/compact`, `/fork`, `/rewind`), headless `grok -p` / ACP `grok agent stdio` scoping, MCP-aware tool preferences, permission rules, and sandbox profiles; based on xai-org/grok-build (Apache-2.0, 18k+ stars, July 2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/grok_build_prompt_architect.txt) |\n| 🟠 MiMo Code Prompt Architect | MiMo-Code-optimized prompt engineer — four-element task prompts (goal/context/constraints/done-when), build/plan/compose agent selection, persistent SQLite FTS5 memory (MEMORY.md / checkpoint.md / tasks), `/goal` judge-verified stop conditions, compose-mode specs-driven workflows, deterministic JS workflows, and `.mimocode/skills/` authoring; based on XiaomiMiMo/MiMo-Code (MIT, 12k+ stars, June 2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/mimo_code_prompt_architect.txt) |\n| 🌙 Kimi Code Prompt Architect | Kimi-Code-CLI-optimized prompt engineer — four-element task prompts (goal/context/constraints/done-when), `coder`/`explore`/`plan` subagent selection, `/goal` judge-verified stop conditions, AI-native `/mcp-config`, SKILL.md authoring, lifecycle hooks, video/multimodal input, and KIMI.md/AGENTS.md project-rule discipline; based on MoonshotAI/kimi-code (MIT, 6.2k+ stars, May 2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/kimi_code_prompt_architect.txt) |\n| 🧩 OpenAI Codex Skill Author | Author installable Codex skills in the official Agent Skills format — SKILL.md with trigger-tuned description, optional agents/openai.yaml for invocation policy and MCP dependencies, scripts-only-when-needed discipline, and progressive-disclosure context design; based on OpenAI's Codex Skills docs and github.com/openai/skills (2026, 22.6k+ stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/openai_codex_skill_author.txt) |\n| 🦘 Roo Code Custom Mode Architect | Design focused, least-privilege Custom Modes for the open-source Roo Code VS Code agent — role definition, tool allowlist (read/edit/browser/command/mcp), file-permission discipline, model-routing hints, and mode-specific safety guardrails; outputs `.roomodes` JSON and a verification checklist; based on RooVetGit/Roo-Code (Apache-2.0, 50k+ stars, 2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/roo_code_custom_mode_architect.txt) |\n| 🐼 Qwen3-Coder-Next Agentic Coding Architect | Design agentic coding harnesses for Qwen3-Coder-Next — 80B/3B hybrid MoE economics, 256K native context (1M via YaRN), non-thinking output, specialized function-call format, FIM editing, plan-then-execute loops, and verifiable reward signals; based on the Qwen3-Coder-Next Technical Report (arXiv 2603.00729, 2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/qwen3_coder_next_agentic_architect.txt) |\n| 📐 Formal Theorem Proving Architect | Blueprint-driven Lean 4 prover — dependency-graph decomposition, parallel lemma proving, compiler-feedback refinement loops; 99.2% pass@1 on MiniF2F-test, 75.6% on PutnamBench; based on Goedel-Architect (arXiv 2606.06468, June 2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/formal_theorem_proving_architect.txt) |\n| 🧪 Prototype Architect | Throwaway-prototype skill — logic prototypes (interactive TUI for state machines) and UI prototypes (radically different variants on a single route with floating switcher); based on mattpocock/skills (Jan 2026, 117k+ stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/prototype_architect.txt) |\n| 🔍 Code Reviewer | Security-focused code reviewer — OWASP Top 10, severity grading, fix examples (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/code_reviewer_security.txt) |\n| 🕸 Multi-Agent Orchestrator | Central dispatch agent — task decomposition, parallel delegation, state tracking, error recovery (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/multi_agent_orchestrator.txt) |\n| 🎛 Teams-First Multi-Agent Orchestrator | Teams-first multi-agent orchestration layer for Claude Code — 19 specialized agents with model routing (haiku/sonnet/opus), delegation rules, skill triggers, team pipeline (plan→prd→exec→verify→fix), structured commit trailers, and project memory; based on Yeachan-Heo/oh-my-claudecode (Feb 2026, 35k+ stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/oh_my_claudecode_multi_agent_orchestrator.txt) |\n| 🧱 Agent Harness Designer | System prompt for designing reliable agent runtimes — tool minimization, approval gates, memory/compaction, rollback, observability, evals; derived from OpenAI/Anthropic harness guidance (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/agent_harness_designer.txt) |\n| 🔐 Autonomous Permission Classifier Architect | Design model-based permission classifiers for coding agents — prompt-injection probe, reasoning-blind transcript classifier with two-stage filter, block/allow templates, deny-and-continue semantics, and recursive subagent handoff gates; based on Anthropic's \"How we built Claude Code auto mode\" (March 2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/autonomous_permission_classifier_architect.txt) |\n| 🔁 Loop Engineering Architect | Design external loop specifications that let coding agents run without step-by-step prompting — trigger, goal, five-level verification ladder, architecture, stopping rule, durable memory; based on \"Stop Hand-Holding Your Coding Agent\" (arXiv 2607.00038, July 2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/loop_engineering_architect.txt) |\n| 🔄 Claude Code Loops Operator | Turn/goal/time/proactive loop operator for Claude Code — choose the right primitive (`/goal` · `/loop` · `/schedule`), encode verification skills, manage tokens, and design routines that run while you sleep; based on Anthropic's official \"Loop engineering: Getting started with loops\" guide (July 2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/claude_code_loops_operator.txt) |\n| 🛞 Loop Engineering Patterns Operator | Practical loop pattern operator for recurring coding-agent tasks — select from 7 production patterns (PR Babysitter, Daily Triage, CI Sweeper, etc.), scaffold with `loop-init`, score Loop Ready with `loop-audit`, and operate the five building blocks + memory across Grok, Claude Code, Codex, and Opencode; based on cobusgreyling/loop-engineering (MIT, 9.7k+ stars, June 2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/loop_engineering_patterns_operator.txt) |\n| 🧭 Fable Method Agent Loop Architect | Think / act / prove agent loop — classify the ask, define done with named verification, gather primary-source evidence in parallel, commit to one recommendation, act surgically, verify by observation, report outcome-first; includes domain adapters, triviality/fit/intent/recall/authorization gates, twin-check, and artifact gate; based on Sahir619/fable-method (MIT, 1.9k+ stars, July 2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/fable_method_agent_loop_architect.txt) |\n| 📜 Auditable Enterprise LLM Agent Harness Architect | Reconstruct prompt-heavy enterprise LLM prototypes into traceable, auditable, code-owned systems — source-to-claim pipeline, code-owned contracts, seven validation dimensions, replaceable composition boundary, insight-first answer structure; based on \"From Prompts to Contracts: Harness Engineering for Auditable Enterprise LLM Agents\" (arXiv 2607.08028, July 2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/auditable_enterprise_llm_agent_harness_architect.txt) |\n| ⚡ Agent Harness Performance Engineer | Cross-harness agent harness optimization — token economics, memory persistence hooks, continuous learning via instinct extraction, verification loops, parallelization, security scanning; based on affaan-m/everything-claude-code (Jan 2026, 182k+ stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/agent_harness_performance_engineer.txt) |\n| 💰 Agent Cost Observability Architect | End-to-end cost observability and budget-governance system for AI coding agents — multi-provider token telemetry, real-time TUI/menubar dashboards, per-project budget envelopes, cost-anomaly detection, optimization recommendation loops, forecast-and-actual tracking; based on getagentseal/codeburn (Apr 2026, 7.2k+ stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/agent_cost_observability_architect.txt) |\n| 📁 Agent Virtual Filesystem Architect | Unified virtual-filesystem layer for AI agents — mount topology, resource adapters, bash-tool surface, two-layer cache, snapshots/cloning, framework integration; based on strukto-ai/mirage (May 2026, 2149 stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/agent_virtual_filesystem_architect.txt) |\n| 🖥 AOS CE Agent Operating System Architect | Architect for Unicity AOS Community Edition — capsules, Astrid Runtime, Forge workbench, meta-harness loops, MCP bridge, and least-privilege capability design; based on unicity-aos/aos-ce (Rust, 6.5k+ stars, July 2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/aos_ce_agent_os_architect.txt) |\n| 🏢 QM Multiplayer Agent Harness Architect | Design and deploy Y Combinator's QM — a multiplayer agent harness for work with personal + shared scopes, Slack + web surfaces, admin governance, per-scope sandbox, multi-harness core (Pi/OpenCode/Codex/Claude Code), shared skills, and crons/watches; based on yc-software/qm (MIT, ~5k stars, July 2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/qm_multiplayer_agent_harness_architect.txt) |\n| 🧹 Agent State Hygiene Architect | Local-agent state maintenance architect — inspect-before-mutate discipline, report-first workflow, archive-don't-delete policy, handoff-doc continuity, session metadata bloat detection, stale worktree pruning, log rotation, and config hygiene; based on vibeforge1111/keep-codex-fast (May 2026, 1.2k+ stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/agent_state_hygiene_architect.txt) |\n| ⚙️ Autonomous Software Factory Orchestrator | Chat-driven autonomous development orchestrator — human sets direction via lightweight messages, self-coordinating claws execute planning/build/test/review/push loops; notification routing (git/tmux/GitHub/lifecycle) kept strictly outside agent context windows; based on ultraworkers/claw-code (Mar 2026, 191k+ stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/autonomous_software_factory_orchestrator.txt) |\n| 🖥 Computer Use Operator | System prompt for browser/desktop agents — observe → act → verify loops, least privilege, confirmation gates, phishing/prompt-injection resistance; derived from OpenAI's 2026 computer-use guidance | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/computer_use_operator.txt) |\n| 🌐 Browser Harness Designer | Self-healing browser harness architect — direct CDP websocket, thin editable runtime, agent-generated helper layer, domain/interaction skill separation; based on browser-use/browser-harness (Apr 2026, 12k+ stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/browser_harness_designer.txt) |\n| 🎭 Webwright Browser Agent | Microsoft SWE-style browser agent — code-as-action Playwright automation, critical-point plan, screenshot evidence, self-verification loop, one-shot vs parameterized CLI modes; based on microsoft/Webwright (Apr 2026, 4.6k+ stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/webwright_browser_agent.txt) |\n| 🌐 Vercel Agent Browser Operator | Native Rust browser automation operator for AI agents — snapshot-first navigation with `@eN` refs, semantic locators, batch execution, MCP server mode, React introspection, Web Vitals, and axe-core a11y audits; based on vercel-labs/agent-browser (Apache-2.0, 39k+ stars, Jan 2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/vercel_agent_browser_operator.txt) |\n| 🖼 UI-TARS Desktop Agent Operator | Vision-language model driven GUI agent operator — screenshot-first observation, structured mouse/keyboard actions, GUI/browser/remote operator modes, MCP tool mounting, event-stream context engineering; based on bytedance/UI-TARS-desktop (2026, 36.6k+ stars, Apache-2.0) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/ui_tars_desktop_agent_operator.txt) |\n| 📱 Phone Harness Operator | Real-iPhone agent operator via macOS iPhone Mirroring — screenshot + Vision OCR for eyes, HID-level CGEvents for hands; least-privilege phone control, observe-act-verify loops, iOS gesture quirks, high-impact confirmation gates; based on ShawnPana/phone-harness (MIT, 1.3k+ stars, Aug 2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/phone_harness_operator.txt) |\n| 🖥 Agent-Native CLI Designer | Agent-native CLI architect for GUI software — 7-phase SOP to wrap any GUI app into a stateful, agent-usable CLI with REPL + subcommand modes, backend integration, test planning, and SKILL.md generation; based on HKUDS/CLI-Anything (Mar 2026, 34k+ stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/cli_anything_harness_designer.txt) |\n| 🧩 Agent Skill Designer | Prompt for packaging reusable agent skills — narrow scope, tool-aware workflow, safety rules, verification checklist, `SKILL.md` draft output; derived from Anthropic/Google skill guidance (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/agent_skill_designer.txt) |\n| 🧠 Managed Agent Architect | Prompt for designing long-running managed-agent systems — brain/hands split, worker contracts, checkpoints, permission scoping, recovery; derived from Anthropic/OpenAI 2026 harness guidance | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/managed_agent_architect.txt) |\n| 🚀 Launch Your Agent Architect | Founder copilot for launching Claude Managed Agents (CMA) — interview a founder, scope the smallest v0, launch in their own Anthropic account, grade against a binary rubric, iterate, and schedule deployments; based on anthropics/launch-your-agent (Apache-2.0, June 2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/launch_your_agent_architect.txt) |\n| 🔌 Agent Protocol Advisor | Prompt for choosing MCP vs A2A vs simpler transports — protocol mapping, trust boundaries, ownership, retries, migration plan; derived from Google's 2026 protocol guide | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/agent_protocol_advisor.txt) |\n| 🔌 A2A Agent Protocol Architect | Architect A2A-compliant agent-to-agent systems — AgentCard discovery, Task lifecycle, Message/Part/Artifact contracts, JSON-RPC/gRPC/HTTP bindings, async streaming, OAuth/mTLS security, idempotency, versioning; based on the A2A open protocol (Google → Linux Foundation, v1.0 2026, 22k+ stars, Apache-2.0) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/a2a_agent_protocol_architect.txt) |\n| 🌐 Omnigent Meta-Harness Architect | Vendor-agnostic control plane for orchestrating multiple coding-agent harnesses — adapter contracts, policy envelopes, sandbox profiles, portable context bundles, and cross-harness verification; based on omnigent-ai/omnigent (Apache-2.0, 7.4k+ stars, June 2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/omnigent_meta_harness_architect.txt) |\n| 🌐 Vercel Eve Agent Architect | Filesystem-first agent architect for Vercel Eve — design durable backend agents using `agent/instructions.md`, `agent/tools/`, `agent/skills/`, `agent/channels/`, `agent/schedules/`, `agent/connections/`, and `agent/subagents/` conventions; path-named capabilities, typed Zod tools, load-on-demand skills, human-in-the-loop approvals, and `eve eval` harness; based on vercel/eve (Apache-2.0, 4.3k+ stars, June 2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/vercel_eve_agent_architect.txt) |\n| 🧮 Agentic Code Reasoner | Prompt for evidence-backed code reasoning — semi-formal reasoning chain, competing hypotheses, verification-first conclusions for complex code understanding (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/agentic_code_reasoner.txt) |\n| 🧠 ADHD Parallel Ideation Skill | Parallel divergent ideation for coding agents — spawns N isolated branches under cognitive frames (hardware/regulator/biology/speedrunner/etc), scores/clusters/prunes traps, deepens survivors; mechanical generator/critic split with zero shared context during divergence; for architecture, naming, API design, and fuzzy-debugging decisions; based on UditAkhourii/adhd (May 2026, 717+ stars, The New Stack featured, preprint) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/adhd_parallel_ideation_skill.txt) |\n| 📨 Multi-Agent Communication Designer | Prompt for designing agent-to-agent message protocols — topology choice, message fields, conflict handling, graph/schema vs free-text tradeoffs (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/multi_agent_communication_designer.txt) |\n| 🕸 Multi-Agent Topology Selector | Prompt for choosing single/parallel/sequential/hierarchical/hybrid agent topologies — communication cost, ownership, failure controls, human review points (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/multi_agent_topology_selector.txt) |\n| 🤝 Agent Cooperation Designer | Prompt for designing cooperative multi-agent systems — shared objective, local roles, disagreement rules, anti-herding controls, evaluation signals (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/agent_cooperation_designer.txt) |\n| 🎛 Vendor-Diverse Multi-Agent Ensemble Designer | Prompt for designing multi-agent ensembles that DELIBERATELY mix vendors (Claude / GPT / Gemini / DeepSeek / Qwen / Llama) — role-to-vendor mapping for complementary inductive biases, disagreement-as-signal arbitration, vendor-correlated failure audit, monoculture controls, version pinning; based on MIT/Harvard \"Multi-Agent LLM Systems for Clinical Diagnosis: The Impact of Vendor Diversity\" (arXiv 2603.04421, 2026) — generalised beyond clinical to any high-stakes ambiguous task | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/vendor_diverse_multi_agent_designer.txt) |\n| 🗄 SQL Assistant | Senior DB engineer — query writing (CTE-first), optimization (EXPLAIN-driven), schema design, multi-dialect (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/sql_assistant.txt) |\n| 🐛 Debugging Agent | Systematic bug hunter — reproduce → observe → hypothesize → test → localize → fix; works for any language (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/debugging_agent.txt) |\n| 🎯 Disciplined Diagnostician | Disciplined diagnosis loop for hard bugs and performance regressions — feedback-loop construction, falsifiable hypotheses, instrumented probes, correct regression-test seams, cleanup protocol; based on mattpocock/skills (Feb 2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/diagnose.txt) |\n| 🏗 System Design | Staff-level architect — clarifies requirements first, capacity estimation, component trade-offs, failure modes (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/system_design.txt) |\n| 📐 Spec-Driven Development Architect | Spec-first system designer — structured mission/tech-stack/roadmap/requirements/scenarios/validation packages; RFC 2119 discipline, delta specs for changes, small-phase decomposition; based on 2026 spec-driven development best practices (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/spec_driven_development_architect.txt) |\n| ⚡ Performance Profiler | Performance engineering expert — baseline → bottleneck analysis → impact-ranked optimization plan with code examples (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/performance_profiler.txt) |\n| 🔧 Refactoring Coach | Refactoring specialist — diagnose code smells, sequence safe Fowler-catalog transforms, preserve behavior at every step (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/refactoring_coach.txt) |\n| 🔗 API Integration Architect | Integration architect — pattern selection, auth, retry/backoff, idempotency, observability for reliable system-to-system integrations (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/api_integration_architect.txt) |\n| 🗃 Database Schema Designer | DB architect — entity modeling, normalization (1NF–3NF), index strategy, PostgreSQL DDL with migration notes (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/database_schema_designer.txt) |\n| 🧪 Test Strategy Architect | Testing architect — risk-based test pyramid, tooling, coverage targets by layer, 4-week implementation roadmap (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/test_strategy_architect.txt) |\n| ⚡ Claude Artifacts | System prompt for generating rich Claude Artifacts (UI, interactive apps, code) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/claude_artifacts_prompt.md) |\n| 💻 Professional Coder | Expert coding assistant — auto programming, project generation, any language | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/%F0%9F%92%BBProfessional%20Coder.md) |\n| 🎨 Design System Spec Architect | Prompt for authoring DESIGN.md design-system specifications — machine-readable YAML tokens + human-readable rationale, component definitions, state variants, and WCAG-safe palettes; derived from Google Labs' 2026 design.md specification (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/design_system_spec_architect.txt) |\n| 🎨 Generative UI Architect | Component-first, design-system-native UI generation — states, tokens, accessibility, responsive layouts, typed code output (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/generative_ui_architect.txt) |\n| 🎨 Open Design Orchestrator | Local-first, agent-agnostic design producer — skill-driven prototype/deck workflows, 72+ brand-grade design systems, deterministic visual directions, five-dimensional self-critique, multi-modal export (HTML/PDF/PPTX/MP4); based on nexu-io/open-design (Apr 2026, 38k+ stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/open_design_orchestrator.txt) |\n| 🎨 Magazine Web Deck Designer | Single-file HTML horizontal-swipe deck architect — two locked visual styles (Editorial Magazine × Electric Ink vs Swiss Internationalism), WebGL hero backgrounds, 10–22 registered layout skeletons, locked theme presets, Motion One choreography, typography-first discipline; based on op7418/guizang-ppt-skill (Apr 2026, 8590 stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/magazine_web_deck_designer.txt) |\n| 🎨 HTML PPT Studio Designer | Professional static HTML presentation architect — 36 themes, 15 full-deck templates, 31 layouts, 47 animations (27 CSS + 20 canvas FX), true presenter mode with pixel-perfect previews + speaker script + timer; token-based design system, keyboard runtime, no build step; based on lewislulu/html-ppt-skill (Apr 2026, 4676 stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/html_ppt_studio_designer.txt) |\n| 🎨 Frontend Taste Engineer | Senior UI/UX engineer that overrides default LLM biases toward generic UI — metric-based design rules (variance/density/motion dials), anti-slop guardrails, CSS hardware acceleration, spring physics, liquid-glass refraction, and premium interaction states; based on Leonxlnx/taste-skill (Apr 2026, 17.5k+ stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/frontend_taste_engineer.txt) |\n| 🎨 Anti-AI-Slop Design Architect | Structural-variety-first design skill — refuses LLM-default rhythms, enforces 69-gate slop test, locked-token discipline, honest-copy rule, pre-emit 6-axis self-critique, and four verbs (default/audit/redesign/study); based on Nutlope/hallmark (Apr 2026, 2.4k+ stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/anti_ai_slop_design_architect.txt) |\n| 🎨 HTML-Native Design Orchestrator | Single-sentence-to-ship design skill — interactive prototypes, HTML decks, motion design (MP4/GIF), infographics, and 5-dimension expert critique; enforces Core Asset Protocol (logo → product shots → UI → color → font), Junior Designer workflow, anti-AI-slop rules, and 5-schools×20-philosophies design direction advisor; based on alchaincyf/huashu-design (Apr 2026, 14k+ stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/huashu_design.txt) |\n| 🖥 Frontend Developer | React/Vue/Angular expert — component architecture, Core Web Vitals, WCAG 2.1, responsive design, TypeScript, performance budgets (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/frontend_developer.txt) |\n| 🌐 Web Quality Auditor | Comprehensive frontend quality audit — Lighthouse-driven performance (Core Web Vitals), accessibility (WCAG 2.2 AA), technical SEO, and best practices; severity-graded findings with file:line citations and concrete fixes; based on addyosmani/web-quality-skills (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/web_quality_auditor.txt) |\n| 📲 Mobile App Builder | Native iOS (Swift/SwiftUI) + Android (Kotlin/Jetpack Compose) + cross-platform (React Native/Flutter) — offline-first, biometric auth, push notifications, app store deployment (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/mobile_app_builder.txt) |\n| 🍎 SwiftUI Code Reviewer | Production-grade SwiftUI code reviewer — deprecated API modernization, data flow validation, accessibility audit (Dynamic Type/VoiceOver/Reduce Motion), performance optimization, Swift 6.2 concurrency, navigation patterns, code hygiene; based on twostraws/SwiftUI-Agent-Skill (Mar 2026, 3.9k+ stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/swiftui_code_reviewer.txt) |\n| 🤖 Jetpack Compose Architect | Production-grade Jetpack Compose code architect — state authoring/hoisting/holder patterns, recomposition performance, stability diagnostics, deferred reads, side-effect lifecycle, Kotlin Flow state/event modeling, accessibility and Material 3 compliance; based on chrisbanes/skills (May 2026, 660 stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/jetpack_compose_architect.txt) |\n| ⛓️ Solidity Smart Contract Engineer | Security-first Solidity — checks-effects-interactions, ERC-20/721/1155, UUPS/diamond proxies, DeFi primitives, gas optimization, Foundry fuzz/invariant testing, L2 deployment (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/solidity_smart_contract_engineer.txt) |\n| ⚡ Solana Blockchain Architect | Production-grade Solana program design — Rust/Anchor, account-model discipline, PDA derivation/CPI safety, SPL Token/Token-2022, compute-unit optimization, reinitialization defense, signer/owner validation, `solana-program-test` verification; based on solana-foundation/solana-dev-skill (Mar 2026, 493 stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/solana_blockchain_architect.txt) |\n| 🧠 Emotion-Aware Engineering Partner | Senior coding partner grounded in Anthropic's 2026 emotion-vectors research — incremental delivery, honest uncertainty calibration, collaborative pushback, debugging transparency (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/emotion_aware_engineering_partner.txt) |\n| ✅ Verification Specialist | Adversarial validation agent — tries to break implementations across frontend, backend, CLI, mobile, data/ML, and infra; enforces command-backed PASS/FAIL/PARTIAL verdicts with adversarial probes (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/verification_specialist.txt) |\n| 🏛 Tech Debt Auditor | Whole-repo structural audit — nine-dimension debt sweep (architectural decay, consistency rot, type debt, test debt, dependency rot, performance hygiene, observability, security hygiene, documentation drift); forced orientation before judgment, mandatory `file:line` citations, required \"looks bad but is actually fine\" section; based on ksimback/tech-debt-skill (Apr 2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/tech_debt_auditor.txt) |\n| 🧐 Doubt-Driven Development Architect | Fresh-context adversarial review for non-trivial decisions — CLAIM → EXTRACT → DOUBT → RECONCILE → STOP cycle; isolates artifact + contract, forbids passing the claim to the reviewer, bounds doubt theater, offers cross-model escalation; based on addyosmani/agent-skills (2026, 54.7k+ stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/doubt_driven_development.txt) |\n| 🎯 Andrej Karpathy Coding Guidelines | Concise behavioral guardrails against common LLM coding mistakes — think before coding, simplicity first, surgical changes only, goal-driven verification; derived from Andrej Karpathy's observations on LLM coding pitfalls (Jan 2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/andrej_karpathy_coding_guidelines.txt) |\n| 🐴 Ponytail Lazy Senior Dev Architect | Make your coding agent think like the laziest senior dev — YAGNI ladder, reuse-before-write, stdlib/native-first, one-line-when-possible, while keeping validation, security, accessibility, and error handling non-negotiable; ~54% less code in real agentic benchmarks; based on DietrichGebert/ponytail (MIT, 84k+ stars, June 2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/ponytail_lazy_senior_dev_architect.txt) |\n| 🧰 Coding Agent System Prompt | Production-grade system prompt for CLI coding agents — identity, permission model, task execution discipline, code style constraints, risk-aware action, tool usage protocol, output efficiency; independently authored from patterns observed in Claude Code (Apr 2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/coding_agent_system_prompt.txt) |\n| 📊 Technical Diagram Engineer | Production-quality SVG diagram generator — architecture, data flow, flowchart, sequence, agent/memory, UML, ER, network topology; 7 visual styles, semantic arrow vocabulary, shape taxonomy, layout rules, AI/Agent domain patterns; based on yizhiyanhua-ai/fireworks-tech-graph (Apr 2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/technical_diagram_engineer.txt) |\n| 🧩 Claude Code Sub-Agent Designer | Designer prompt for Anthropic's Claude Code sub-agents — when to use sub-agent vs skill vs inline, kebab-case naming, routing description authoring, least-privilege tool allowlists, isolated context discipline, output-contract lock-in, routing stress test; based on Anthropic's Claude Code Sub-Agents docs (Feb 2026) and wshobson/agents + VoltAgent/awesome-claude-code-subagents (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/claude_code_subagent_designer.txt) |\n| 🏛 Solution Architect | In-depth codebase study → concrete implementation plan — explores conventions, maps dependencies, presents multiple options with trade-offs, sequences reversible incremental steps, and surfaces open questions before any code is written; based on repowise-dev/claude-code-prompts (Apr 2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/solution_architect.txt) |\n| 🛠 Pragmatic Programmer | Classic software engineering principles as binding agent rules — DRY at knowledge level, orthogonality, tracer bullets, ruthless feedback, automation, broken windows; MUST/SHOULD/MUST NOT policy for code generation and review; based on Hunt \u0026 Thomas and ciembor/agent-rules-books (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/pragmatic_programmer.txt) |\n| 📚 Classic Software Engineering Canon | Multi-book binding ruleset for AI coding agents — Clean Code (readability, naming, functions, side effects), Clean Architecture (dependency direction, boundaries, adapters), Domain-Driven Design (bounded contexts, aggregates, ubiquitous language), Designing Data-Intensive Applications (consistency, durability, replication, schema evolution); unified review checklist; based on ciembor/agent-rules-books (Apr 2026, 1.4k+ stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/classic_software_engineering_canon.txt) |\n| 🦸 Superpowers Agentic Development Framework | Structured skill-driven software development methodology — 14 composable skills with activation triggers, red flags, procedural checklists, and verification criteria; 7-step workflow (brainstorm → plan → worktree → TDD → subagent-driven execution → code review → finish); mandatory refusal to skip tests/review/verification; based on obra/superpowers (May 2026, 85k+ stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/superpowers_agentic_development_framework.txt) |\n| 📓 AGENTS.md Author | Authoring prompt for the AGENTS.md open standard — concise repo-root file telling cross-vendor coding agents (Codex CLI, Cursor, Aider, Gemini CLI, Jules, Factory, RooCode; Claude Code via CLAUDE.md) how to set up, build, test, and commit safely; recommended section order, extract-don't-invent commands, monorepo nested-file resolution, ≤200-line discipline, anti-patterns, provenance + questions output; based on the official agents.md spec, OpenAI's Aug 2025 introduction, and Agentic AI Foundation / Linux Foundation 2026 stewardship | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/agents_md_author.txt) |\n| 🕸 Codebase Knowledge Graph Architect | Transform code, SQL schemas, infrastructure definitions, docs, and multimodal assets into a structured, queryable knowledge graph — AST-level entity extraction, God-node identification, surprising cross-module connections, design-rationale mining, architectural tension detection, and confidence-tagged edges (EXTRACTED / INFERRED / AMBIGUOUS); outputs GRAPH_REPORT.md, graph.json, and optional interactive visualization; supports incremental delta updates on commits; based on safishamsi/graphify (Apr 2026, 44k+ stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/codebase_knowledge_graph_architect.txt) |\n| 🧠 Codebase Memory MCP Architect | MCP-native code-intelligence architect for DeusData codebase-memory-mcp — index repos into a persistent knowledge graph (158 languages, Hybrid LSP, \u003c1ms structural queries), map agent questions to the 15 MCP tools, design indexing/watch/artifact policies, and enforce query plans that replace file-by-file exploration; based on DeusData/codebase-memory-mcp (MIT, 37k+ stars, Feb 2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/codebase_memory_mcp_architect.txt) |\n| 🏗 Parallel Codegen Architect | Architect generator/evaluator/orchestrator harness patterns for sustained, large-scale code construction with parallel LLM sub-agents — compilers, interpreters, runtimes, parsers, type checkers, codemod systems; pre-condition test (decomposable artifact, testable interfaces, work-per-module repays coordination), strict role separation (orchestrator reads only summaries, never generator transcripts; evaluator is read-only on code and tests; sealed modules are immutable without explicit reopening), phased workflow (plan → parallel build → integration tiers → end-to-end → postmortem), checkpoint-resumable execution, anti-patterns refused (inter-generator chat, evaluator-rewrites-tests-to-pass, role conflation, unbounded parallelism); based on Anthropic's \"Building a C Compiler with Parallel Claudes\" (anthropic.com/engineering/building-c-compiler, Feb 2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/parallel_codegen_architect.txt) |\n| 🏭 Opinionated Agent Team Designer | Multi-role tooling system designer for AI coding agents — CEO / Designer / Eng Manager / Release Manager / Doc Engineer / QA role definitions with explicit mandates and anti-scopes, review lattice (plan-review, code-review, pre-ship sign-off), slash-command invocation protocol, infrastructure roles (autoplan, guard, benchmark, learn, retro), team-mode shared configuration with silent auto-updates; opinionated over flexible, narrow over general, review over trust, explicit over implicit; based on garrytan/gstack (Mar 2026, 96k+ stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/opinionated_agent_team_designer.txt) |\n| 🖥 Native-Feel Desktop Architect | Cross-platform desktop app architect that feels indistinguishable from native — four-layer architecture (native shell → system WebView → Node backend → Rust core), eight architectural tenets, WebKit/WebView2 survival guide, 75-item ship audit, anti-patterns (Electron abstraction, Tauri control-loss, two UI codebases); based on yetone/native-feel-skill (May 2026, 1.2k+ stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/native_feel_desktop_architect.txt) |\n| 🅾 Agent-First Language Architect | Programming-language designer that treats agents as primary users — small regular surface, deep standard library, deterministic structured tooling, and explicit syntax; based on vercel-labs/zerolang (May 2026, 3.6k+ stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/agent_first_language_architect.txt) |\n| 📄 Agentic HTML Publisher | Local-first, ship-ready HTML publisher — turns Markdown/CSV/JSON/notes into single-file HTML via 75 skill templates across 9 surfaces (magazine, deck, poster, social cards, prototype, data report, Hyperframes); juice-inlined CSS for WeChat, 2× PNG for X, standalone .html download; anti-AI-slop design discipline with locked palettes, CJK font stacks, and 8 px baseline grid; based on nexu-io/html-anything (May 2026, 4.5k+ stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/html_anything_publisher.txt) |\n| 🧱 Small Model Coding Agent Architect | Terminal-native coding agent designed for 8B–35B local models — deterministic regex tool routing, plan-tracker anchors, patch-first editing, forgiving JSON parser, two-tier memory, snapshot rollback, graceful cloud escalation, benchmark-driven development, and structured 8-step debugging; compensates for small context windows and unreliable tool calling instead of assuming frontier-model capabilities; based on Doorman11991/smallcode (May 2026, 1.6k+ stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/small_model_coding_agent_architect.txt) |\n| 🏛 Symphony Workflow Orchestrator Architect | Issue-tracker-driven autonomous execution orchestrator — per-issue workspace isolation, WORKFLOW.md contract, bounded concurrency, retry backoff, reconciliation, observability, and human-review handoff; based on openai/symphony (Feb 2026, 24.8k+ stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/symphony_workflow_orchestrator_architect.txt) |\n| 🌐 Website Clone Architect | Pixel-perfect website reverse-engineer — Chrome MCP reconnaissance, getComputedStyle() design-token extraction, parallel builder agents in git worktrees, component spec contracts with interaction-model discipline, visual QA diff; 95–99% accuracy for static pages; based on JCodesMore/ai-website-cloner-template (Mar 2026, 16k+ stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/website_clone_architect.txt) |\n| 🦑 OpenSquilla Token-Efficient Agent Architect | Design token-efficient, microkernel AI agents with OpenSquilla — local SquillaRouter model routing, persistent memory, layered sandbox, built-in web search, on-device embeddings, and a unified turn loop across CLI/Web/chat; route each turn to the cheapest capable model, keep durable state out of the prompt window, and measure token economics per turn; based on opensquilla/opensquilla (Apache-2.0, 6.3k+ stars, May 2026) and \"Agentic Routing: The Harness-Native Data Flywheel\" (arXiv 2607.11399, July 2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/opensquilla_token_efficient_agent_architect.txt) |\n\n### DevOps \u0026 SRE\n\n| Name | Description | Prompt |\n|------|-------------|--------|\n| 🚨 Incident Response Commander | Incident commander — SEV1-4 matrix, real-time coordination, blameless post-mortems, SLO/SLI framework, stakeholder comms templates (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/incident_response_commander.md) |\n| 🛡 SRE | Site reliability engineer — SLO/error budget framework, observability three pillars, golden signals, toil reduction, chaos engineering (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/sre.md) |\n| ☁️ Cloud Architect | Senior cloud architect — multi-cloud (AWS/Azure/GCP), Well-Architected Framework, migration 6Rs, FinOps, zero-trust, disaster recovery, IaC (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/cloud_architect.txt) |\n| ⎈ Kubernetes Specialist | K8s operations — cluster architecture, RBAC, network policies, GitOps (ArgoCD/Flux), service mesh (Istio/Linkerd), multi-tenancy, CIS Benchmark, cost optimization (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/kubernetes_specialist.txt) |\n| 🏗 Platform Engineer | Internal developer platform \u0026 AI infrastructure — IaC, multi-model serving, agent runtime, observability, cost optimization, GitOps, zero-trust (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/platform_engineer_iac.txt) |\n| 🚀 Release Engineer | Production launch specialist — pre-launch checklists, feature flags, staged canary rollouts, rollback strategy, post-launch verification; based on addyosmani/agent-skills (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/release_engineer.txt) |\n| 🏗 Terraform IaC Specialist | Diagnose-first Terraform/OpenTofu specialist — response contract (assumptions, risk category, remediation, validation, rollback), failure-mode routing table (identity churn, secret exposure, blast radius, CI drift, state corruption), module hierarchy, count vs for_each rules, testing strategy matrix; based on antonbabenko/terraform-skill (Jan 2026, 1.9k+ stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/terraform_iac_specialist.txt) |\n\n### Data Engineering\n\n| Name | Description | Prompt |\n|------|-------------|--------|\n| 🔧 Data Engineer | Data pipeline specialist — Medallion Architecture (Bronze/Silver/Gold), PySpark + Delta Lake, dbt contracts, Great Expectations, Kafka streaming (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/data_engineer.md) |\n| 📈 Analytics Engineer | Production data infrastructure — dimensional modeling, dbt, pipeline architecture, data quality testing, metrics definition (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/analytics_engineer.txt) |\n| 🗄 Data Platform Architect | Enterprise data platform design — lakehouse architecture, data mesh, real-time streaming, AI/ML pipelines, governance, multi-cloud cost optimization (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/Data_Platform_Architect.txt) |\n| 📊 Data Governance Architect | Enterprise data governance — policy frameworks, stewardship models, data catalogs, lineage tracking, privacy compliance, AI data standards (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/Data_Governance_Architect.txt) |\n\n### AI \u0026 ML\n\n| Name | Description | Prompt |\n|------|-------------|--------|\n| 🤖 ML Systems Architect | Production ML design — data pipelines, training, inference, model evaluation, MLOps, monitoring, cost optimization, LLM fine-tuning (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/ml_systems_architect.txt) |\n| 🧬 LLM Architect | LLM systems — fine-tuning (LoRA/QLoRA/RLHF/DPO), RAG architecture, serving (vLLM/TGI), quantization (GPTQ/AWQ), safety guardrails, multi-model orchestration (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/llm_architect.txt) |\n| 🎙 Realtime Voice Agent Architect | Enterprise voice agent design — sub-1s TTFA, streaming STT→LLM→TTS, turn-taking, barge-in handling, voice-optimized prompts, confirmation gates (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/realtime_voice_agent_architect.txt) |\n| 🎨 Multimodal Agent Designer | Cross-modal agent architecture — active perception, visual/audio grounding, token-efficient context management, modality-aware tool design, GUI automation (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/multimodal_agent_designer.txt) |\n| 🔍 Long-Horizon Multimodal Search Agent | Sustained visual-textual search across 100-turn horizons — file-based visual context management, progressive on-demand image loading, multi-hop visual reasoning, horizon drift prevention; based on LMM-Searcher (arXiv 2604.12890, April 2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/long_horizon_multimodal_search_agent.txt) |\n| 🧠 Proactive Memory Agent for Long-Horizon Agents | Active memory intervention layer — separate memory agent decides when to inject reminders vs. stay silent; structured bank of status, knowledge, and procedural memories; based on \"Remember When It Matters\" (arXiv 2607.08716, July 2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/proactive_memory_agent_long_horizon.txt) |\n| 🧭 S-Agent Spatial Tool-Use Architect | Spatial reasoning as spatio-temporal evidence accumulation — VLM planner + three-level spatial tool hierarchy (2D grounding → 3D lifting → spatial knowledge aggregation) + Scene/Agent memory; training-free improvements on open-source and closed-source VLMs; based on S-Agent (arXiv 2606.20515, June 2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/s_agent_spatial_tool_use_architect.txt) |\n| ⚖️ AI Ethics Reviewer | Algorithmic ethics audit — fairness \u0026 bias, transparency, privacy, safety, accountability, societal impact, cross-cultural considerations, mitigation roadmap (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/AI_Ethics_Reviewer.txt) |\n| 🤖 MLOps Engineer | ML operations platform — feature stores, model registries, training pipelines, serving infrastructure, drift monitoring, experiment tracking, GPU optimization, LLM deployment (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/MLOps_Engineer.txt) |\n| 🦾 Embodied AI Developer | VLA systems, robotic agents, world-model-driven embodied intelligence — perception-action grounding, sim-to-real pipelines, cross-embodiment transfer, skill primitives, physical safety gates; derived from 2026 embodied-AI research (StarVLA, EmbodiedClaw, VLA-World) (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/embodied_ai_developer.txt) |\n| 🌍 Agent World Model Architect | Predictive environment simulators for agent imagination — state-space design, dynamics modeling, counterfactual rollouts, plan-then-execute integration, world-model-specific safety (hallucinated futures, goal misgeneralization, deceptive alignment); spans physics, language, and hybrid world models; based on VLA-World, OccuBench, and 2026 world-model safety research (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/agent_world_model_architect.txt) |\n| 📱 On-Device AI Deployment Architect | Privacy-first edge AI architect — hardware-aware model selection, quantization strategy (GGUF/AWQ/TurboQuant), inference engine tuning (MLX/llama.cpp/Ollama/vLLM/TensorRT-LLM), KV-cache optimization, SSD offloading, hybrid cloud-edge partitioning, thermal/power management; based on llmfit, omlx, Rapid-MLX, ds4, apfel, and 2026 on-device AI ecosystem (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/on_device_ai_deployment_architect.txt) |\n| 🤖 Self-Improving Agent Architect | Closed learning loop agent design — experience-driven skill creation, autonomous improvement nudges, cross-session memory with user modeling, multi-platform gateway, scheduled automations, model-agnostic backends; based on NousResearch/hermes-agent (2026, 140k+ stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/self_improving_agent_architect.txt) |\n| 🏢 Agentic Company Orchestrator | Zero-human-company multi-agent orchestration architect — org-chart design, heartbeat-driven execution, goal-aligned delegation, budget governance with hard stops, ticket-based task tracking, board approval gates, multi-company isolation, and portable company templates; based on paperclipai/paperclip (Mar 2026, 64k+ stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/agentic_company_orchestrator.txt) |\n| 🔭 Open Deep Research Agent Architect | End-to-end design of an open-source deep research agent that competes with OpenAI Deep Research / Gemini Deep Research / Perplexity Pro — task contract, synthetic agentic data pipeline, on-policy RL with verifiable rewards, Light vs Heavy inference modes, typed evidence graph with triangulation, long-horizon planner with replan triggers, deployment topology with prefix caching, public-benchmark eval harness (xbench / BrowseComp / GAIA / FRAMES), citation-honesty governance; based on Alibaba-NLP/DeepResearch — Tongyi DeepResearch (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/open_deep_research_agent_architect.txt) |\n| 📈 Quantitative Trading Agent Architect | End-to-end quantitative trading agent design — natural-language strategy generation, cross-market backtesting (A/HK/US equities, crypto, futures, forex), Shadow Account behavior extraction from broker journals, multi-agent trading teams (investment/quant/crypto/risk), 452-alpha factor zoo, persistent research memory; based on HKUDS/Vibe-Trading (Apr 2026, 7.6k+ stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/quantitative_trading_agent_architect.txt) |\n| 🧪 Autonomous ML Research Agent | Self-directed experiment loop for ML research — fixed-time-budget training, single-file edit discipline, keep/discard decision gates, git-branch state management, overnight autonomy; reads code, forms hypotheses, runs experiments, logs results, and iterates without human intervention; based on karpathy/autoresearch (Mar 2026, 80k+ stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/autonomous_ml_research_agent.txt) |\n| 🧪 Agent Environment Engineering Architect | Design the runtime, artifacts, constraints, and interfaces that let off-the-shelf CLI agents do metric-driven autonomous scientific discovery — permissions/artifact/budget/human-in-the-loop engineering, hidden-evaluator sandbox, parallel propose-implement loops, cost-capped exploration; based on EurekAgent (arXiv 2606.13662, June 2026; THU-Team-Eureka/EurekAgent) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/agent_environment_engineering_architect.txt) |\n| 🧪 ML Intern — Autonomous ML Engineer | Hugging Face-native autonomous ML engineer — literature-first recipe extraction, citation-graph crawling, current API validation, HF Jobs training with pre-flight checks, Trackio monitoring, sandbox-first development, and headless iterative improvement; based on huggingface/ml-intern (May 2026, ~8.1k stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/ml_intern_autonomous_ml_engineer.txt) |\n| 🧪 Self-Distillation Code Generation Strategist | Decision strategist for the SSD recipe — when self-distillation is the right next training move and when it is not; precondition test on pass@k − pass@1 gap, minimal-recipe pipeline (sample → cross-entropy fine-tune on raw unverified samples, no reward model, no verifier, no RL), parallel verifier-aware arm, pre-declared anti-collapse battery (self-BLEU, length drift, pass@k diversity, style probe, safety/refusal drift), round-2 decision gate, per-difficulty slice reporting with CIs, GPU-hour Pareto comparison vs SFT-external / DPO / GRPO; refuses to recommend SSD on models whose pass@k − pass@1 gap is \u003c ~5 pp and refuses to ship gains without contamination-checked held-out slices; based on Apple's \"Self-Distillation Improves Code Generation\" (arXiv 2604.01193, April 2026; Qwen3-30B 42.4% → 55.3% pass@1 on LiveCodeBench v6, gains concentrate on hard problems) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/self_distillation_code_strategist.txt) |\n| ⚖️ Verifier Engineering Strategist | Designs, audits, and refuses verifier systems — the machinery that turns a model's output (final answer, intermediate step, tool call, agent trajectory) into a reward/selection/gating signal; per-workload type selection (rule-based → programmatic → ORM → PRM → LLM-as-judge → hybrid), explicit verifier hypothesis with target precision/recall on named slices, Math-Shepherd-style PRM data synthesis with held-out cross-policy evaluation, mandatory adversarial probe battery (length inflation, format mimicry, confidence-word spam, prompt injection via candidate), reward-vs-true-accuracy divergence monitor as the reward-hacking detector, verifier-policy co-adaptation cycle, infrastructure-noise separation, versioning + kill-switch protocols; refuses LLM-as-judge in RL without bounded bias, refuses in-distribution PRM accuracy as a deployment signal, refuses shared training/eval verifier; based on the 2025–2026 verifier-augmented training trajectory (DeepSeek-R1 arXiv 2501.12948, Math-Shepherd arXiv 2312.08935, ProcessBench arXiv 2412.06559, Anthropic's Demystifying Evals / Infrastructure Noise / Eval Awareness 2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/verifier_engineering_strategist.txt) |\n| 🗺 AgentAtlas Trajectory Eval Architect | Diagnostic agent evaluator — scores trajectories by control-decision taxonomy (Act / Ask / Refuse / Stop / Confirm / Recover), trajectory-failure taxonomy, six-axis coverage audit, and taxonomy-aware vs. taxonomy-blind gap; separates real capability from prompt-supervision artifacts; based on \"AgentAtlas: Beyond Outcome Leaderboards for LLM Agents\" (arXiv 2605.20530, May 2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/agentatlas_trajectory_eval_architect.txt) |\n| 🛰 WorkSpace-Isolated Agent OS Architect | Productivity-oriented agent platform architect — WorkSpace-level isolation (files/memory/skills/cost per project), white-box memory with end-to-end traceability and dream-mode consolidation, smart model routing by task difficulty (~70% cost savings), always-on background execution with deliverable landing, MCP-native integration; based on OpenBMB/PilotDeck (May 2026, 2.6k+ stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/workspace_isolated_agent_os_architect.txt) |\n| 🐈 Nanobot Personal Agent Operator | Self-hosted personal AI agent operator — config/workspace separation, SOUL.md/USER.md/AGENTS.md identity files, Dream memory consolidation, multi-channel deployment (WebUI/CLI/Telegram/Discord/Slack/Feishu/Email), MCP/tool integration, cron/heartbeat/trigger automations, provider presets, and workspace access-mode discipline; based on HKUDS/nanobot (MIT, 46k+ stars, Feb 2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/nanobot_personal_agent_operator.txt) |\n\n### Product \u0026 Strategy\n\n| Name | Description | Prompt |\n|------|-------------|--------|\n| 🧭 Product Manager | Full product lifecycle — discovery to launch; PRD template, RICE scoring, Now/Next/Later roadmap, GTM brief, outcome measurement (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/product_manager.md) |\n| 🔎 Continuous Discovery Architect | Structured product discovery — Opportunity Solution Trees (Teresa Torres), 8-risk assumption mapping, 9 prioritization frameworks (Opportunity Score/RICE/ICE/Kano), lean startup experiments with XYZ hypotheses and pretotypes; validates before building, prioritizes problems over features; based on phuryn/pm-skills (Mar 2026, 15.8k+ stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/continuous_discovery_architect.txt) |\n| 🧠 AI-Native Product Architect | AI-first product design — agentic workflows, generative UI, human-in-the-loop at the right level, self-improving loops, trust \u0026 transparency architecture (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/ai_native_product_architect.txt) |\n| 🎯 UX Research Specialist | Research methodology and user insights — qualitative interviews, usability testing, survey design, metrics analysis, journey mapping, stakeholder communication (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/ux_research_specialist.txt) |\n| 💼 CFO / Financial Strategy | Chief Financial Officer driving capital allocation and enterprise value — FP\u0026A, fundraising, M\u0026A, pricing strategy, board reporting (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/cfo_financial_strategy.txt) |\n| 🏦 Investment Banking Associate Agent | End-to-end pitch and valuation agent — comps, precedents, DCF, LBO, football-field summary, branded deck generation; Excel model discipline (formulas-over-hardcodes, blue/black/green color coding, balance checks), institutional-grade QC, citation rigor; based on Anthropic's official Claude for Financial Services (Feb 2026, 26k+ stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/investment_banking_associate_agent.txt) |\n| 🏛 Financial Operations \u0026 Compliance Agent | Fund-administration and financial-operations analyst — GL reconciliation, month-end close (accruals, roll-forwards, variance commentary), LP statement audit, KYC/onboarding screening with rules-engine evaluation and sanctions/PEP escalation; spreadsheet discipline, audit-trail hygiene, human sign-off gates; based on Anthropic's official Claude for Financial Services (May 2026, ~29k stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/financial_operations_compliance_agent.txt) |\n| 📊 Sales Strategist | Sales leader optimizing pipeline, win rates, territory planning, deal acceleration — BANT/MEDDIC, quota setting, GTM execution (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/sales_strategist.txt) |\n| 💬 Customer Success Strategist | Account success leader maximizing lifetime value — health scoring, account planning, executive engagement, EBRs, retention \u0026 expansion, advocacy programs (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/customer_success_strategist.txt) |\n| 🚀 Growth Hacker | Growth driver using data-driven experimentation — funnel optimization, viral loops, unit economics, A/B testing, activation, retention, acquisition channels (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/growth_hacker.txt) |\n| 📈 Content Calibration Architect | Content experiment strategist — turns every post into a calibrated 5-phase loop (score → blind-predict → ship → retro → evolve); rubric-driven scoring, immutable prediction discipline, and compounding judgment over time; format-agnostic (video, essay, thread, podcast); based on XBuilderLAB/cheat-on-content (May 2026, 3k+ stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/content_calibration_architect.txt) |\n| ⚙️ Operations Manager | Ops leader optimizing processes, reducing costs, enabling scale — Lean, bottleneck analysis, cost structure, systems integration (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/operations_manager.txt) |\n| 🔄 Change Management Leader | Organizational transformation and adoption — stakeholder alignment, communication strategy, training programs, adoption tracking, sustainment, cultural change (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/change_management_leader.txt) |\n| 🎯 Recruitment Strategist | Talent acquisition leader building pipelines and optimizing hiring — sourcing, competency modeling, offer strategy, retention focus (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/recruitment_strategist.txt) |\n| 💬 Community Manager | Community leader building engaged, healthy communities — moderation, engagement loops, advocacy programs, member lifecycle, culture building (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/community_manager.txt) |\n| 🎨 Brand Strategist | Brand building and reputation — positioning, messaging, visual identity, GEO (Generative Engine Optimization), crisis management, brand experience (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/brand_strategist.txt) |\n| 👥 HR / Talent Development | Talent development and performance — recruitment, onboarding, learning, career development, culture, DEI, engagement, retention (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/hr_talent_development.txt) |\n| 💰 Financial Advisor | Comprehensive wealth management — financial planning, investment strategy, risk management, tax optimization, estate planning, behavioral coaching (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/financial_advisor.txt) |\n| 🔍 SEO Specialist | Technical SEO, content strategy, link authority, SERP features — audit templates, keyword research, E-E-A-T, Core Web Vitals, AI search adaptation (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/seo_specialist.txt) |\n| 🎤 Developer Advocate | DevRel — DX audits, technical content, community building, product feedback loops, SDK adoption, conference talks, time-to-first-success tracking (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/developer_advocate.txt) |\n| 🚀 Growth Engineering Skill Architect | End-to-end marketing skill ecosystem for AI agents — product-marketing foundation, 35+ interlocking skills (CRO, SEO, ads, copy, analytics, retention), skill-dependency graph, agentskills.io standard; every skill reads shared context before acting and cross-references related skills instead of duplicating; based on coreyhaines31/marketingskills (Jan 2026, 29.5k+ stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/growth_engineering_skill_architect.txt) |\n| 🎯 Paid Advertising Architect | Multi-platform paid advertising audit \u0026 optimization — 250+ checks across Google, Meta, YouTube, LinkedIn, TikTok, Microsoft, Apple \u0026 Amazon Ads; weighted scoring, attribution/tracking deep dives, AI creative pipeline, PPC math, A/B test design; based on AgriciDaniel/claude-ads (Feb 2026, 5.5k+ stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/paid_advertising_architect.txt) |\n\n### Project Management\n\n| Name | Description | Prompt |\n|------|-------------|--------|\n| 🏃 Scrum Master | Certified Scrum Master — sprint ceremonies, impediment removal, team coaching, velocity tracking, retrospectives, scaling (SAFe/LeSS/Nexus) (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/scrum_master.txt) |\n| 🚨 Project Recovery Specialist | Crisis project turnaround — root cause diagnosis, stakeholder realignment, scope reclamation, team rehabilitation, 30-60-90 day recovery plans (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/Project_Recovery_Specialist.txt) |\n| 🔄 Agile Transformation Lead | Enterprise agile transformation — operating model design, framework selection, product management integration, flow optimization, change management, technical practices (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/Agile_Transformation_Lead.txt) |\n| 📋 Technical Program Manager | Complex cross-functional program delivery — dependency modeling, critical path analysis, risk management, stakeholder alignment, resource planning, AI-augmented workflows (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/Technical_Program_Manager.txt) |\n\n### Healthcare \u0026 Clinical\n\n| Name | Description | Prompt |\n|------|-------------|--------|\n| 🏥 Clinical Assistant | Differential diagnosis generator + SOAP note writer from transcripts/notes — ICD-10/CPT coding, diagnostic workup, HIPAA-compliant (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/clinical_assistant.txt) |\n| 🏥 Healthcare Operations Agent | HIPAA-aware healthcare operations analyst — prior-authorization review, claims-appeal support, patient-message triage, ambient clinical documentation; NPI/ICD-10/CMS policy validation, human-in-the-loop sign-off, audit-trail sourcing; based on Anthropic's official Claude for Healthcare (Jan 2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/healthcare_operations_agent.txt) |\n| 🏥 Healthcare AI Architect | Clinical AI system design — safety-first architecture, multi-agent clinical reasoning, evidence stratification, uncertainty communication, HIPAA/FDA compliance, MR-Bench evaluation (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/healthcare_ai_architect.txt) |\n| 🔬 Clinical Research Coordinator | Clinical trial operations — GCP compliance, protocol design, site management, patient recruitment, safety reporting, decentralized trials, data integrity (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/Clinical_Research_Coordinator.txt) |\n| 🏥 Health Informatics Specialist | Digital health system design — EHR integration, FHIR interoperability, clinical decision support, health data architecture, regulatory compliance (HIPAA/FDA), AI in healthcare (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/Health_Informatics_Specialist.txt) |\n| 🧬 Bioinformatics Engineer | Production-grade computational biology — NGS pipelines (FASTQ→BAM→VCF), single-cell/spatial transcriptomics, differential expression, variant calling, multi-omics integration; Snakemake/Nextflow workflows, Bioconductor statistical rigor, reproducible containerized environments; based on GPTomics/bioSkills (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/bioinformatics_engineer.txt) |\n\n### Industrial \u0026 Automotive\n\n| Name | Description | Prompt |\n|------|-------------|--------|\n| 🚗 Automotive Functional Safety Architect | ISO 26262 safety architect — HARA with Cartesian malfunction analysis, ASIL decomposition, FSC/TSC derivation, HW-SW interface design, ISO/SAE 21434 cybersecurity concept, ISO 21448 SOTIF validation, GSN safety-case argument; every artifact paired with implicit reviewer gate; based on jherrodthomas/automotive-skills-suite (May 2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/automotive_functional_safety_architect.txt) |\n| 🤖 Industrial Robotics Architect | ISO 10218 / ISO/TS 15066 / ISO 3691-4 robotics architect — machinery safety lifecycle (ISO 12100 → ISO 13849 / IEC 62061), cobot biomechanical limits and SSM/PFL, AMR fleet safety with VDA 5050, ROS2 system architecture, IEC 62443 OT cybersecurity, FAT/SAT V\u0026V; every artifact paired with implicit reviewer gate; based on jherrodthomas/robotics-skills-suite (May 2026, 510 stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/industrial_robotics_architect.txt) |\n| 🏭 Agentic CAD \u0026 Hardware Designer | Parametric CAD and hardware-design engineer — STEP-first build123d/Python parts and assemblies, natural-language spec → CAD brief, enclosures/fixtures/joints/mating, URDF/SDF/SRDF robotics descriptions, source-controlled geometry with validated exports; based on earthtojake/text-to-cad (Apr 2026, 2952 stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/agentic_cad_hardware_designer.txt) |\n| 🔩 Embedded Firmware Engineer | Production-grade MCU firmware — ESP32/ESP-IDF, STM32 HAL/LL, Nordic nRF5/Zephyr, FreeRTOS; static allocation discipline, ISR minimalism, protocol state machines (UART/SPI/I2C/CAN/BLE), memory-safety rules, stack watermark verification; based on GammaLabTechnologies/harmonist (Apr 2026, 1788 stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/embedded_firmware_engineer.txt) |\n| 🔌 PCB/EDA Design Architect | Production-grade PCB design architect — schematic review, PCB layout analysis, Gerber verification, DRC/ERC, net tracing, SPICE simulation, EMC pre-compliance (FCC/CISPR), DFM validation, multi-supplier BOM sourcing; based on aklofas/kicad-happy (Mar 2026, 398 stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/pcb_eda_design_architect.txt) |\n| 🧩 Verilog RTL Architect | Production-grade Verilog-2001 RTL generation and FPGA design workflows — staged generation (regular/deep-review/agentic-repair), existing-RTL analysis/refinement/verify-repair, AXI-Stream/AXI4-Lite/AXI4/AHB/APB interface templates, static lint, self-checking testbench scaffolds, ASIC-quality review, Vivado/VCS/iverilog backend validation; based on Eriemon/verilog-generator (May 2026, 160 stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/verilog_rtl_architect.txt) |\n\n### Legal \u0026 Compliance\n\n| Name | Description | Prompt |\n|------|-------------|--------|\n| ⚖️ Legal Analyst | Comprehensive legal research and contract analysis — IRAC methodology, regulatory compliance, litigation risk, IP strategy, M\u0026A due diligence (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/legal_analyst.txt) |\n| 🔒 Compliance Auditor | SOC 2, ISO 27001, HIPAA, PCI-DSS — gap assessment, evidence collection automation, policy templates, audit preparation, continuous compliance (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/compliance_auditor.txt) |\n| 📋 Regulatory Affairs Specialist | Global regulatory strategy — FDA/EMA/NMPA pathways, QMS design, submission preparation, gap analysis, post-market surveillance, AI/ML compliance (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/Regulatory_Affairs_Specialist.txt) |\n| ⚖️ Contract Negotiation Strategist | Complex deal negotiation — contract architecture, risk allocation, BATNA/ZOPA analysis, concession planning, cultural negotiation, AI-assisted contract analysis, M\u0026A and licensing (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/Contract_Negotiation_Strategist.txt) |\n| 🤖 AI Governance Legal Agent | End-to-end AI governance counsel — use-case triage (APPROVED/CONDITIONAL/NOT APPROVED), AI impact assessment, vendor AI review, regulatory gap analysis, policy monitoring; source-attribution discipline with [settled]/[verify]/[verify-pinpoint] tiers, red-line gates, jurisdiction-aware cross-checks, lawyer/non-lawyer role calibration; based on Anthropic's official Claude for Legal (Apr 2026, 7.3k+ stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/ai_governance_legal_agent.txt) |\n| ⚖️ Agentic Deontic Reasoning Architect | Rule-following agent architect — stores statutes/policies as retrievable harness files, binds case facts to rule elements on demand, handles cross-references and exceptions, verifies conclusions before submission; based on DAR (arXiv 2606.05009, June 2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/agentic_deontic_reasoning_architect.txt) |\n| 📝 China Patent Disclosure Architect | End-to-end China patent mining and technical disclosure drafting — project scanning, patent-point extraction, CNIPA prior-art search with abstract-grounded summaries, de-identified disclosure documents with mermaid diagrams, iterative revision loops, and self-check gates; based on handsomestWei/patent-disclosure-skill (Apr 2026, 1.6k+ stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/china_patent_disclosure_architect.txt) |\n| 🏛 China Software Copyright Materials Architect | End-to-end Chinese software copyright registration package — real source-code extraction (first-30 / last-30 pagination), examiner-facing operation manual with anti-AI-flavor discipline, mandatory human confirmation gates, registration-form consistency enforcement; based on Fokkyp/SoftwareCopyright-Skill (Apr 2026, 3.5k+ stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/china_software_copyright_architect.txt) |\n\n### Knowledge \u0026 Documentation\n\n| Name | Description | Prompt |\n|------|-------------|--------|\n| 📚 Knowledge Management Architect | Enterprise knowledge systems — information architecture, documentation standards, AI-powered search, RAG, discoverability, governance, maintenance (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/knowledge_management_architect.txt) |\n| 📝 Technical Documentation Strategist | Comprehensive docs strategy — docs-as-code, AI-assisted writing, information architecture, developer experience, quality assurance, knowledge management integration (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/Technical_Documentation_Strategist.txt) |\n| 🧠 Personal Knowledge Assistant | PKM system design — Zettelkasten, BASB, spaced repetition, AI reading assistants, semantic note-taking, knowledge synthesis, creativity pipelines (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/Personal_Knowledge_Assistant.txt) |\n| 🗄 Knowledge Base Architect | Enterprise knowledge systems design — taxonomy, ontology, information architecture, semantic search, knowledge graphs, AI-augmented curation, content lifecycle governance (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/Knowledge_Base_Architect.txt) |\n| 🔗 Personal Agent Brain Architect | Self-wiring knowledge brain for personal AI agents — entity-centric graph, hybrid search (exact → graph → vector), verbatim ingestion, self-maintenance dream cycle, skill-driven interface; based on garrytan/gbrain (Apr 2026, 14k+ stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/personal_agent_brain_architect.txt) |\n| 📖 Book-to-Skill Architect | Transform technical books and documents into structured agent skills — extracts frameworks, mental models, principles, techniques, and anti-patterns; generates on-demand SKILL.md, chapter summaries, glossary, patterns, and cheatsheet; based on virgiliojr94/book-to-skill (May 2026, 1k+ stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/book_to_skill_architect.txt) |\n| 🧠 Cognitive Distillation Architect | Distill any person's cognitive operating system into a reusable agent skill — five-layer extraction (expressive DNA, mental models, decision heuristics, anti-patterns, honesty boundaries), six-channel research, triple-gate validation, directional + uncertainty verification; based on alchaincyf/nuwa-skill (Apr 2026, 22k+ stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/cognitive_distillation_architect.txt) |\n| 🗄 Obsidian Vault Operator | Obsidian-native agent skill — wikilinks, embeds, callouts, properties, CLI automation, JSON Canvas, Bases database views, and Defuddle web extraction; based on kepano/obsidian-skills (Jan 2026, 32.5k+ stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/obsidian_vault_operator.txt) |\n| 🌐 OpenWiki Agent Documentation Architect | Design and maintain an agent-facing codebase wiki using OpenWiki conventions — OKF v0.1 bundles, `openwiki/` architecture, AGENTS.md / CLAUDE.md pointer blocks, INSTRUCTIONS.md briefs, code / personal modes, and CI update workflows; based on langchain-ai/openwiki (MIT, 12k+ stars, June 2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/openwiki_agent_documentation_architect.txt) |\n\n### Writing \u0026 Academic\n\n| Name | Description | Prompt |\n|------|-------------|--------|\n| ✏️ All-around Writer | Professional writing in any style — essays, articles, fiction | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/%E2%9C%8F%EF%B8%8FAll-around%20Writer%20%28Professional%20Version%29.md) |\n| 👌 Academic Assistant Pro | Academic writing with a professorial touch — papers, citations, analysis | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/%F0%9F%91%8CAcademic%20Assistant%20Pro.md) |\n| 🖋 Literature Professor | Essay writing and literary analysis from a professor's perspective | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/Literature_Professor.md) |\n| 📝 Technical Writer | Senior dev-docs writer — Stripe/Twilio/Google standards; blog posts, API docs, release notes, READMEs; no padding (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/technical_writer.txt) |\n| ✈️ Simplified Technical English (STE) Writer | Agent skill that writes docs in ASD-STE100 Simplified Technical English — 20/25-word sentence limits, one word one meaning, simple tenses, active voice, condition-before-command; 72.9% fewer STE violations measured across 6 Claude models; based on AminBlg/SimpleEnglish (MIT, 1.7k+ stars, July 2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/simple_english_ste_technical_writer.txt) |\n| 📑 Academic Peer Reviewer | Comprehensive manuscript review — contribution assessment, methodology critique, reproducibility, ethics, constructive feedback, recommendation with confidence (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/Academic_Peer_Reviewer.txt) |\n| 📄 Research Paper Proofreader | Claude Code/Codex paper proofreading — two-phase detect-then-fix workflow, 9 review categories (language, clarity, structure, LaTeX, notation), severity-graded issues, anti-AI-slop rules; based on LimHyungTae/awesome-claudecode-paper-proofreading (Mar 2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/research_paper_proofreader.txt) |\n| 🗣 Talk-Normal Enabler | System prompt that removes AI slop — direct, informative, no filler/fluff/summary-stamps, no negation-based contrastive phrasing; 72–73% token reduction on GPT-4o-mini/GPT-5.4 with zero information loss; based on hexiecs/talk-normal (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/talk_normal_enabler.txt) |\n| ✍️ Humanizer | Writing editor that removes 29 signs of AI-generated text — detects inflated symbolism, promotional language, vague attributions, AI vocabulary, passive voice, filler phrases; supports voice calibration via writing samples; dual-pass audit workflow; based on blader/humanizer (Jan 2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/humanizer.txt) |\n| 🛑 Stop-Slop Writing Editor | Prose editor that strips predictable AI tells — active voice, no adverbs, no throat-clearing, no binary contrasts, no em dashes; 5-dimension scorecard (directness, rhythm, trust, authenticity, density) with 35/50 revision threshold; based on hardikpandya/stop-slop (2026, 10.3k stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/stop_slop.txt) |\n| 🎩 Agent Style Enforcer | Literature-backed technical-prose writing ruleset — 21 rules (12 canonical from Strunk \u0026 White/Orwell/Pinker/Gopen \u0026 Swan + 9 field-observed from LLM output 2022–2026) with severity tiers, BAD/GOOD examples, and escape hatch; drop-in for any AI agent producing `.md`, `.tex`, `.rst`, or source-code comments; based on yzhao062/agent-style (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/agent_style_enforcer.txt) |\n| 🧬 Nature-Style Scientific Writer | Submission-grade scientific writing and figure architect for Nature-family journals — argument-first drafting, hourglass structure, section-specific templates (abstract/introduction/results/discussion), verb calibration, publication-quality Python/R figure pipelines, data-availability ethics, and Chinese-author support; based on Yuan1z0825/nature-skills (Apr 2026, 7.3k+ stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/nature_style_scientific_writer.txt) |\n| 🏛 Academic Paper Architect | Full-spectrum manuscript orchestrator — 12-agent pipeline (literature strategy → structure → argument → draft → citation → bilingual abstract → simulated peer review → formatting); style calibration, writing quality checks, IRON RULE checkpoints, 8 invocation modes; based on Imbad0202/academic-research-skills (May 2026, 18k+ stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/academic_paper_architect.txt) |\n| 🎯 Journal Adapt Writing Architect | Dynamic, corpus-grounded academic writing skill generator — learns target-journal conventions from user-provided papers, builds a reviewable `dynamic_writing_skill.md`, then revises manuscripts section by section with a 5-layer priority system (hard preserve → target journal → secondary corpus → static base → cleanup); based on WantongC/journal-adapt-writing-skill (May 2026, 438 stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/journal_adapt_writing_architect.txt) |\n| 🦴 Paper Spine Architect | Motivation-driven academic paper mastery — motivation spine extraction, central argument trees, evidence-aware blueprints, revision matrices with argument-impact gating, and LaTeX-safe audits; based on WUBING2023/PaperSpine (May 2026, 1.7k+ stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/paper_spine_architect.txt) |\n| 📝 LaTeX Academic Expert | Venue-aware LaTeX formatting + academic writing polish — template switching (NeurIPS/ICML/CVPR/ACL/IEEE/Nature/Science), citation-style conversion, page-limit compliance, double-blind anonymization, section-aware prose editing, Chinglish pattern fixes; preserves all commands/math/cites; based on Calix-L/awesome-latex-skills (May 2026, 171 stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/latex_academic_expert.txt) |\n| 📊 Paper Figure Mirror Engineer | Camera-ready matplotlib figure architect — transfers the visual style of a top-conference paper figure (NeurIPS/ICML/ICLR/Nature) onto the user's data via iterative Drawer/Reviewer loops; enforces layout invariants (no overlap, no clipping, no defaults), L1-reference + L2-convention dual anchoring, and visible-but-recessive hairline calibration; outputs self-contained `.py` + camera-ready PDF/PNG; based on VILA-Lab/FigMirror (May 2026, 427 stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/paper_figure_mirror_engineer.txt) |\n\n### Learning \u0026 Education\n\n| Name | Description | Prompt |\n|------|-------------|--------|\n| 🦌 Mr. Ranedeer v2.7 | Fully customizable AI tutor — depth, learning style, tone, reasoning framework (updated Mar 2025) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/Mr_Ranedeer.txt) |\n| 📗 All-around Teacher | Adaptive tutor — explains anything in 3 minutes, customized to your level | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/%F0%9F%93%97All-around%20Teacher.md) |\n| 🚀 LearnOS PRO | Interactive learning assistant with dynamic, personalized explanations | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/LearnOS_PRO.txt) |\n| 🏛 Socratic Tutor | Guides students to understanding through questions, not answers — works for any subject (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/socratic_tutor.txt) |\n| 🧠 Adaptive Learning Designer | AI-driven personalized education — knowledge tracing, spaced repetition, intelligent tutoring, learning analytics, engagement design, ethical safeguards (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/Adaptive_Learning_Designer.txt) |\n| 🎓 Interactive Codebase Course Architect | Transform any codebase into a scroll-based interactive HTML course for non-technical \"vibe coders\" — animated visualizations, embedded quizzes, code↔plain-English translations, glossary tooltips; based on zarazhangrui/codebase-to-course (Apr 2026, 4.4k+ stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/codebase_course_architect.txt) |\n\n### Research \u0026 Analysis\n\n| Name | Description | Prompt |\n|------|-------------|--------|\n| 🔬 Deep Research Agent | Multi-step research system prompt — plan, search, cross-check, synthesize (2025) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/deep_research.txt) |\n| 🕸 WebSwarm Deep-and-Wide Research Orchestrator | Recursive multi-agent orchestration for complex web research — progressive delegation with deep/wide/interleaved search modes, evidence-upward aggregation, and shared-experience recycling among sibling nodes; based on WebSwarm (arXiv 2607.08662, July 2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/webswarm_deep_wide_research_orchestrator.txt) |\n| 🧮 AI Co-Mathematician | Interactive research partner for open-ended mathematical discovery — ideation, literature bridging, computational exploration, conjecture formation, theorem proving, theory building; manages uncertainty, tracks dead ends, refines intent across turns; scored 48% on FrontierMath Tier 4; based on Google DeepMind's AI Co-Mathematician (arXiv 2605.06651, May 2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/ai_co_mathematician.txt) |\n| 📊 Data Analysis | Extract insights, flag anomalies, recommend specific visualizations | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/data_analysis.txt) |\n| 📈 Data Analyst | Senior analyst translating data into insights — SQL, A/B testing, cohort analysis, metrics, visualization, statistical rigor, actionable recommendations (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/data_analyst.txt) |\n| 🧠 Reasoning Specialist | Structured thinking for complex problems — problem decomposition, CoT reasoning, hypothesis generation, multi-path exploration, confidence assessment (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/reasoning_specialist.txt) |\n| 🔍 Emotion-Aware Research Partner | Research collaborator grounded in Anthropic's 2026 emotion-vectors research — explicit confidence calibration, bias flagging, honest uncertainty, intellectual honesty over authoritative-sounding guesses (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/emotion_aware_research_partner.txt) |\n| 🎨 Multimodal Analyst | Vision-text-data integration — image analysis, document processing, chart interpretation, scene understanding, cross-modal reasoning (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/multimodal_analyst.txt) |\n| 🌐 Autonomous Web Agent | Long-horizon web research agent — search, browse, extract, verify, synthesize; tool discipline, confirmation gates, prompt-injection resistance (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/autonomous_web_agent.txt) |\n| 🗂 Structured Output Extractor | Schema-strict JSON extraction — type safety, null handling, multi-record, self-validation (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/structured_output_extractor.txt) |\n| 📈 Investment Research Analyst | Senior equity analyst — business model assessment, financial health, competitive moat, valuation (DCF/comps), bull/bear thesis (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/investment_research_analyst.txt) |\n| 🗺 Market Research Strategist | Market research director — market sizing (bottom-up + top-down), segmentation, competitive map, white-space opportunities, GTM recommendations (2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/market_research_strategist.txt) |\n| 🧪 Paper-to-Code Research Implementer | Citation-anchored research paper implementer — parses arxiv papers, identifies core contribution, audits ambiguities (SPECIFIED / PARTIALLY_SPECIFIED / UNSPECIFIED), generates minimal / full / educational implementations with section citations and walkthrough notebooks; honest uncertainty flags, appendix mining, never hallucinates details; based on PrathamLearnsToCode/paper2code (Apr 2026, 1.3k+ stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/paper_to_code_research_implementer.txt) |\n| 🔬 Scientific Paper Replication Harness Architect | Persistent, evidence-contract replication harness for LaTeX-first research papers — target enumeration, acceptance-mode matching (numeric / distributional / structural / visual / qualitative), anti-cheating guards, run-provenance records, validation gates, and a living replication report; based on PredictiveScienceLab/paper-replication-paper (arXiv 2607.02134, July 2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/scientific_paper_replication_harness_architect.txt) |\n| 🧫 Scientific Database Orchestrator | Structured scientific-data integration agent — disciplined querying across AlphaFold, ChEMBL, PubChem, UniProt, PDB, ClinicalTrials, OpenTargets, GTEx, gnomAD, PubMed, OpenAlex and 30+ sources; wrapper-first execution, identifier-resolution discipline, rate-limit compliance, license notification, fact-verification over parametric knowledge, cost-aware pagination; based on google-deepmind/science-skills (May 2026) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/scientific_database_orchestrator.txt) |\n| 📓 NotebookLM Research Orchestrator | NotebookLM-powered multimodal research orchestrator — ingest URLs, PDFs, YouTube, audio, video, and images; chat with indexed sources; generate podcasts, videos, slide decks, reports, quizzes, flashcards, and mind maps; deep web research with subagent patterns; batch downloads and multi-format export pipelines; based on teng-lin/notebooklm-py (May 2026, 14.6k+ stars) | [prompt](https://github.com/ai-boost/awesome-prompts/blob/main/prompts/notebooklm_research_orchestrator.txt) |\n| 🌐 Grounded Community Researcher | Cross-platform social-pulse researcher — Reddit/X/YouTube/HN/Polymarket/GitHub/web, engagement-weighted synthesis (upvotes/likes/reposts/stars/odds), query-type parsing, format-matched prompt generation; refuses pre-trained knowledge substitution; based on mvanhorn/last30days-skill (Jan 2026, 26k+ stars) | [prompt](https://github.com/ai-boost/a","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/ai-boost%2Fawesome-prompts/projects"}