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","archived":false,"fork":false,"pushed_at":"2026-03-08T11:50:03.000Z","size":423,"stargazers_count":4,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2026-03-08T14:51:38.353Z","etag":null,"topics":["ai","bug-bounty","dorking","fuzzing","hacking","osint","reconnaissance","red-teaming"],"latest_commit_sha":null,"homepage":"https://vulnpire.github.io/Banshee-AI/","language":"Go","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Vulnpire.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2026-02-25T07:27:30.000Z","updated_at":"2026-03-08T12:59:07.000Z","dependencies_parsed_at":null,"dependency_job_id":null,"html_url":"https://github.com/Vulnpire/Banshee-AI","commit_stats":null,"previous_names":["vulnpire/banshee-ai"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Vulnpire/Banshee-AI","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Vulnpire%2FBanshee-AI","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Vulnpire%2FBanshee-AI/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Vulnpire%2FBanshee-AI/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Vulnpire%2FBanshee-AI/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Vulnpire","download_url":"https://codeload.github.com/Vulnpire/Banshee-AI/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Vulnpire%2FBanshee-AI/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31434625,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-05T08:13:15.228Z","status":"ssl_error","status_checked_at":"2026-04-05T08:13:11.839Z","response_time":75,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["ai","bug-bounty","dorking","fuzzing","hacking","osint","reconnaissance","red-teaming"],"created_at":"2026-04-05T12:03:12.468Z","updated_at":"2026-04-05T12:03:13.222Z","avatar_url":"https://github.com/Vulnpire.png","language":"Go","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Banshee\n\nBanshee is an AI-assisted dorking and OSINT CLI for finding exposed documents, sensitive data, misconfigurations, and vulnerable surfaces using search engines, AI-generated queries, and built-in analysis pipelines.\n\nIt is designed for security researchers, bug bounty hunters, and defenders who want a repeatable workflow for search-based discovery without manually crafting every query.\n\nThis repository README is a practical getting-started guide.\n\nIt is not the full manual.\n\nFor the complete flag reference, workflow explanations, internal files/caches, outputs, FAQ, and more detailed examples, use the documentation page:\n\n- `https://vulnpire.github.io/Banshee-AI`\n- Local copy: `docs/index.html`\n\nThe docs interface also includes a simulated Banshee shell with a virtual `banshee` binary and a very basic CTF for learning/demo purposes.\n\n## README Scope (Important)\n\nThis README intentionally focuses on:\n\n- What Banshee is\n- What you need to run it\n- How to install it\n- How to configure the basics\n- Common usage patterns\n- How to get help quickly\n\nThis README intentionally does not try to fully cover:\n\n- Every flag and mode\n- All edge-case behaviors\n- Every cache and internal file format\n- Every AI enhancement workflow\n- Full troubleshooting matrix\n- Full examples for every feature combination\n\nFor those, use the docs site:\n\n- `https://vulnpire.github.io/Banshee-AI`\n- `docs/index.html`\n\n## What Banshee Does\n\nAt a high level, Banshee helps you:\n\n- Generate dorks from natural-language prompts using AI\n- Run dorks across supported search engines\n- Deduplicate and organize findings\n- Analyze discovered documents and responses for sensitive indicators\n- Learn from previous successful scans to improve future dorks\n- Use technology detection and CVE-aware logic for better targeting\n- Scale to multiple targets using stdin and file-based workflows\n\n is especially useful when you want to:\n\n- Search for exposed documents across a target's indexed footprint\n- Hunt for leaked configuration files and secrets\n- Discover admin panels, API paths, backups, and debug endpoints\n- Prioritize high-signal results instead of raw search noise\n- Build repeatable recon workflows with output files and intelligence caches\n\n## Why a Docs-First Approach\n\n has grown into a broad toolkit.\n\nA single README that tries to cover everything becomes hard to maintain and hard to read.\n\nThe web docs are a better place for:\n\n- Interactive examples\n- Rich outputs and annotated screenshots\n- Full flag explanations\n- Structured navigation\n- FAQ and operational notes\n- Demo shell and training content\n\nUse this README to get moving.\n\nUse the docs page as your main reference.\n\n## Feature Overview (Short Version)\n\n includes support for workflows such as:\n\n- AI dork generation from prompts\n- Random dork generation by category\n- Multi-engine search execution\n- SMART dork optimization and follow-ups\n- Learning mode with per-target intelligence\n- Multi-language dork support for non-English targets\n- Document analysis and filtering\n- Response analysis and code analysis\n- Tech detection and technology-aware dorks\n- CVE database workflows and related dork generation\n- Wayback-assisted discovery and creative dorking\n- Monitor-style recurring scans\n- Output files with de-duplication\n- Intelligence viewing/export utilities\n\nThis is only a summary.\n\nFor full feature coverage, use:\n\n- `https://vulnpire.github.io/Banshee-AI`\n- `docs/index.html`\n\n## Requirements\n\n### Runtime\n\n- Go `1.20+` to build/install from source\n- Network access for search APIs and target content retrieval (when used)\n- Shell environment (`bash`/`zsh` etc.) for CLI usage\n\n### Search Providers (Typical)\n\nBanshee commonly uses:\n\n- Google Custom Search (CSE)\n- Brave Search API\n\n### AI (Optional but Recommended for `-ai`)\n\nFor AI prompt-based dork generation, Banshee can use `gemini-cli`.\n\nYou will typically need:\n\n- `gemini-cli` installed\n- A valid Gemini API key or configured auth method (depending on your setup)\n\n### Notes\n\n- You can use Banshee without every feature enabled.\n- Some modes require specific APIs or local configuration.\n- The docs page explains each dependency path in detail.\n\n## Installation\n\n### Option 1: Install with Go\n\n```bash\nGOPROXY=direct go install -v github.com/Vulnpire/Banshee-AI/cmd/banshee@main\n```\n\nAfter install, make sure your Go bin path is in `PATH`.\n\nTypical paths:\n\n- `~/go/bin`\n- `$GOBIN`\n\nCheck:\n\n```bash\nwhich banshee\nbanshee --help\n```\n\n### Option 2: Build from Source (Repository Clone)\n\nIf you prefer local builds from this repository:\n\n```bash\ngo build -o banshee .\n```\n\nOr if your local setup requires building specific files directly (project/version dependent), use the method described in the docs or your existing workflow.\n\nThen run:\n\n```bash\n./banshee --help\n```\n\n### Option 3: Use the Docs Interface First (No Install Yet)\n\nIf you are evaluating Banshee and do not want to install anything yet:\n\n- Open `web/docs.html`\n- Or visit `example.com`\n- Use the simulated shell to learn the command style\n- Try the basic demo/CTF in the virtual environment\n\nThis is useful for:\n\n- New users\n- Team onboarding\n- Training sessions\n- Quick demonstrations\n\n## Basic Configuration (High Level)\n\nBanshee typically needs configuration for API keys and related files.\n\nThe exact paths and formats may vary by feature and version.\n\nUse the docs page for authoritative details:\n\n- `https://vulnpire.github.io/Banshee-AI`\n- `docs/index.html`\n\n### Common Configuration Concepts\n\nYou will usually configure some or all of the following:\n\n- Google API key(s)\n- Google CSE / CX configuration\n- Brave API key(s)\n- Gemini API key or CLI auth\n- Optional proxy settings\n- Optional out-of-scope lists\n\n### Typical Config Directory Pattern\n\nMany setups use a config directory like:\n\n```bash\n~/.config/banshee/\n```\n\nExamples of files you may see there (depending on features used):\n\n- `keys.txt`\n- `brave-keys.txt`\n- `gemini-api-key.txt`\n- other feature-specific files/caches\n\nDo not rely only on this README for exact file names and behavior.\n\nUse the docs page for exact configuration instructions and current expectations.\n\n## Quick Start Workflows\n\nThis section is intentionally practical.\n\nCopy a command, replace the target, and run.\n\nThen move to the docs page for deeper tuning.\n\n### 1. Traditional Dork (Single Target)\n\n```bash\necho example.com | banshee -q \"inurl:admin\" -v\n```\n\nWhat this does:\n\n- Reads target from stdin\n- Runs a custom dork/query\n- Prints verbose logs (`-v`)\n\n### 2. AI Prompt -\u003e Dorks -\u003e Scan\n\n```bash\necho example.com | banshee -ai \"find exposed dashboards and APIs\" --smart --learn -quantity 5 -v\n```\n\nWhat this does:\n\n- Generates dorks from a natural-language prompt\n- Executes them on the target\n- Uses SMART mode to analyze successful patterns\n- Uses LEARN mode to reuse prior intelligence for the target\n\nFor prompt-writing tips and advanced AI tuning, use the docs page.\n\n### 3. Document-Focused Hunting\n\n```bash\necho example.com | banshee -e pdf,docx,xlsx --analyze-docs --filter-docs -o results.txt -v\n```\n\nWhat this does:\n\n- Focuses on document extensions\n- Analyzes documents for sensitive indicators\n- Filters to more relevant document findings\n- Writes output to `results.txt`\n\nFor analyzer behavior, file handling, and output specifics, see the docs page.\n\n### 4. Random Dork Generation\n\n```bash\necho example.com | banshee -random sqli --quantity 10 -v\n```\n\nWhat this does:\n\n- Generates category-based dorks without an AI prompt\n- Uses the requested quantity\n- Runs a quick focused hunt\n\n### 5. Tech Detection + Search\n\n```bash\necho example.com | banshee --tech-detect -ai \"find exposed admin or debug panels\" -v\n```\n\nThis is useful when you want technology context to influence how you search and prioritize.\n\nExact feature interplay varies by mode.\n\nUse the docs page for the recommended workflow patterns.\n\n### 6. Response Analysis Only\n\n```bash\necho https://example.com/api/status | banshee --analyze-response-only -v\n```\n\nUse this when you already have a URL and want analysis without running dorks first.\n\n### 7. Monitor-Style Workflow (Recurring)\n\n```bash\ncat domains.txt | banshee --monitor \"sensitive pdf\" --monitor-time 60 --filter-mon --analyze-mon\n```\n\nThis runs recurring scans on a schedule-like interval (feature behavior depends on your selected flags and environment).\n\nFor safe operational usage and tuning, use the docs page.\n\n## Input Patterns\n\nBanshee supports several ways to define targets and search intent.\n\n### stdin (Recommended for Pipelines)\n\nExamples:\n\n```bash\necho example.com | banshee -q \"inurl:login\"\ncat domains.txt | banshee -ai \"find exposed docs\"\nsubfinder -d example.com -silent | banshee -q \"inurl:admin\"\n```\n\nWhy stdin is useful:\n\n- Easy integration with recon pipelines\n- Batch processing from other tools\n- Cleaner automation in shell scripts\n\n### Direct Single Target (If Supported by Your Workflow)\n\nSome examples in older usage patterns or docs may show direct target flags.\n\nPrefer the docs page for the current recommended syntax and examples for your version.\n\n### File-Based Inputs\n\nCommon patterns include:\n\n- Domain lists\n- Prompt lists\n- Dork files\n- Scope lists / exclusion lists\n\nThe exact flags for each file-based workflow are documented in the web docs.\n\n## AI Dorking (Practical Summary)\n\nAI dorking is one of Banshee's core strengths.\n\nInstead of manually crafting every query, you can describe the goal.\n\nExample prompts:\n\n- `find admin panels`\n- `find exposed invoices and customer docs`\n- `find SQLi candidates`\n- `find debug endpoints and test environments`\n- `find PIIs in documents`\n\n### Basic AI Usage Example\n\n```bash\necho example.com | banshee -ai \"find PIIs in documents\" -quantity 5 -v\n```\n\n### AI + Learning + SMART Example\n\n```bash\necho example.com | banshee -ai \"find leaked config and secrets\" --learn --smart -quantity 8 -v\n```\n\n### AI + Multi-language Example\n\n```bash\necho example.com | banshee -ai \"find sensitive HR documents\" --multi-lang -quantity 6 -v\n```\n\n### AI Notes\n\n- Prompt quality matters.\n- Quantity influences breadth and runtime.\n- SMART and LEARN are most useful over repeated scans.\n- Multi-language mode can improve results for non-English targets.\n\nFor:\n\n- prompt engineering tips\n- quantity tuning\n- AI model selection\n- multi-language behavior\n- edge cases and compatibility notes\n\nUse the docs page.\n\n## Document, Response, and Code Analysis\n\nBanshee can do more than collect URLs.\n\nIt can analyze content and help prioritize results.\n\n### Document Analysis\n\nDocument analysis is helpful for:\n\n- PDF reports\n- Office documents\n- exported spreadsheets\n- files likely to contain PII or internal data\n\nTypical usage pattern:\n\n```bash\necho example.com | banshee -e pdf,docx,xlsx --analyze-docs --filter-docs -v\n```\n\n### Response Analysis\n\nResponse analysis is helpful when:\n\n- You already have a list of URLs\n- You want to inspect returned content for secrets/indicators\n- You want signal without broad dork generation\n\nTypical usage pattern:\n\n```bash\necho https://example.com/path | banshee --analyze-response-only -v\n```\n\n### Inline Code / Source-Oriented Analysis\n\nDepending on the mode and target content, Banshee can analyze code-like responses or embedded data for high-signal indicators.\n\nThe docs page explains:\n\n- analyzers\n- filters\n- output labels\n- severity/sensitivity interpretation\n- performance tradeoffs\n\n## Output Files and De-duplication (Basic)\n\nBanshee supports writing results to an output file (for example via `-o`, depending on your command).\n\nCommon reasons to use output files:\n\n- Save findings for later analysis\n- Track discoveries across runs\n- Feed results into other tools\n- Build target-specific result sets\n\nGeneral behavior (high level):\n\n- Banshee de-duplicates results written to output files\n- Existing entries can affect how repeated results are handled\n- New results are appended when discovered\n\nExact behavior around:\n\n- re-analysis skipping\n- analyzer compatibility\n- output formatting\n- caching interactions\n\nis documented in the web docs.\n\n### Example\n\n```bash\necho example.com | banshee -ai \"find sensitive docs\" --analyze-docs --filter-docs -o findings.txt -v\n```\n\n### Output Hygiene Tips\n\n- Keep one output file per target/program when possible\n- Use descriptive filenames\n- Archive old runs before large experiments\n- Review output with context before reporting findings\n\n## Intelligence, Learning, and Caches (Overview)\n\nBanshee can store and reuse information from previous runs.\n\nThis helps improve later scans through features like learning and smart optimization.\n\nHigh-level concepts you may encounter:\n\n- Target intelligence files\n- AI cache(s)\n- successful URL tracking\n- research caches\n- Wayback caches\n\nBenefits:\n\n- Faster repeat runs in some workflows\n- Better dork quality over time\n- Less repeated work across similar scans\n\nFor cache paths, formats, and maintenance, use the docs page.\n\n## Tech Detection and CVE-Aware Workflows (Overview)\n\nBanshee can perform technology detection and use that context to generate or prioritize better dorks.\n\nThis is especially useful for:\n\n- exposed admin pages tied to specific stacks\n- known technology-specific file patterns\n- CVE-related recon hypotheses\n\nTypical workflow idea:\n\n1. Identify target(s)\n2. Detect technologies\n3. Generate technology-aware dorks\n4. Run searches and analyze results\n5. Refine using SMART/LEARN\n\nThe exact flags and advanced combinations are documented in the web interface.\n\nFor detailed setup and usage examples, use:\n\n- `https://vulnpire.github.io/Banshee-AI`\n- `docs/index.html`\n\n## TLD-Scale and Multi-Target Scanning (High Level)\n\nBanshee can be used in broader discovery workflows, including multi-target input patterns and TLD-oriented recon use cases (feature/mode dependent).\n\nBecause these workflows are more complex and easier to misuse, this README keeps the guidance high level.\n\nUse the docs page for:\n\n- mode compatibility notes\n- performance tuning\n- scope controls\n- output management at scale\n- safe usage patterns\n\nIf you are scanning multiple targets or broad scopes, make sure your authorization and program rules explicitly allow it.\n\n## Search Strategy Tips (Beginner-Friendly)\n\nThese are practical tips that improve results without needing the full manual.\n\n- Start narrow, then expand.\n- Pick one goal per run (docs, configs, admin, debug, SQLi candidates, etc.).\n- Use `-o` to preserve and review results.\n- Add analysis flags when signal matters more than volume.\n- Use `--learn` and `--smart` for repeat targets.\n- Use `--multi-lang` when the target is non-English.\n- Keep your prompts specific when using `-ai`.\n\n### Prompt Examples (Good)\n\n- `find exposed invoices and customer spreadsheets`\n- `find admin panels and dashboard logins`\n- `find SQLi candidates with id parameters`\n- `find debug or staging endpoints`\n- `find secrets in config files and logs`\n\n### Prompt Examples (Too Vague)\n\n- `hack site`\n- `find bugs`\n- `everything`\n\nThe docs page includes much better prompt-writing guidance and workflow-specific examples.\n\n## Example Commands (More Practical Samples)\n\nUse these as starting points.\n\nThen tune in the docs.\n\n### Sensitive Documents (AI)\n\n```bash\necho target.com | banshee -v -ai \"find PIIs in documents\" --learn --smart --analyze-docs --filter-docs -o docs.txt\n```\n\n### Admin Panels (Traditional Query)\n\n```bash\necho target.com | banshee -q \"inurl:admin OR intitle:login\" -v -o admin.txt\n```\n\n### Backup and Config File Hunt\n\n```bash\necho target.com | banshee -ai \"find backup files and exposed config files\" -quantity 8 -v -o files.txt\n```\n\n### API / Debug Surface Discovery\n\n```bash\necho target.com | banshee -ai \"find debug endpoints, test environments, and APIs\" --tech-detect --smart -v\n```\n\n### Random SQLi Candidate Sweep\n\n```bash\necho target.com | banshee -random sqli --quantity 12 --learn -v\n```\n\n### Batch Domains from File\n\n```bash\ncat domains.txt | banshee -ai \"find exposed dashboards\" -quantity 3 -v -o batch.txt\n```\n\n### Response Analysis for Known URL List (Shell Loop Example)\n\n```bash\nwhile read -r url; do\n  echo \"$url\" | banshee --analyze-response-only -v\ndone \u003c urls.txt\n```\n\n### Quiet-ish Pipeline Logging (Adjust Flags)\n\n```bash\ncat scope.txt | banshee -ai \"find sensitive docs\" -quantity 4 -o results.txt\n```\n\nIf a command fails or behaves unexpectedly, check the docs page before assuming the feature is broken.\n\nFlag combinations can change output and behavior significantly.\n\n## Web Documentation Interface (Primary Reference)\n\nBanshee ships with a web documentation interface in the `web/` directory.\n\nOpen it locally:\n\n```bash\nxdg-open docs/index.html\n```\n\nOr use the hosted version:\n\n- `https://vulnpire.github.io/Banshee-AI`\n\n### What the Web Docs Include\n\n- Quickstart walkthroughs\n- Practical examples with sample output\n- Feature overviews by category\n- Analysis mode guidance\n- Monitoring/intelligence notes\n- Internal files and configuration notes\n- Outputs and caches explanations\n- Full flag reference\n- FAQ\n- Safety/EULA notes\n\n### Simulated Shell + Demo/CTF\n\nThe docs include a simulated terminal (JavaScript-only).\n\nIt is useful for:\n\n- learning Banshee command style\n- demos in presentations\n- onboarding new users\n- basic CTF-like interaction practice\n\nWhat it is:\n\n- a virtual environment\n- a fake/simulated shell\n- a learning interface\n- a docs feature\n\nWhat it is not:\n\n- a real shell\n- a system terminal\n- a replacement for local installation\n- a live exploit environment\n\nThe simulated shell includes a virtual `banshee` binary and a very basic CTF flow.\n\n## Suggested Learning Path (New Users)\n\n1. Open `docs/index.html` or `https://vulnpire.github.io/Banshee-AI` and skim the Quickstart section.\n2. Run `banshee --help` locally to confirm installation.\n3. Try one traditional dork (`-q`) on a test target you are authorized to assess.\n4. Try one AI prompt (`-ai`) with low quantity.\n5. Add `--smart` and `--learn` on a repeated target.\n6. Try a document-focused scan with `--analyze-docs --filter-docs`.\n7. Start saving outputs with `-o`.\n8. Move to the docs reference for advanced flag combinations.\n\nThis path gets you productive quickly without needing to memorize every flag upfront.\n\n## Basic Troubleshooting (Quick Checks)\n\nThis is not the full troubleshooting guide.\n\nUse the docs page for the detailed troubleshooting section.\n\n### `banshee: command not found`\n\nCheck:\n\n- `PATH` includes your Go bin directory\n- `go install` completed successfully\n- the binary exists (`which banshee`)\n\n### `--help` Works but Searches Return No Results\n\nCheck:\n\n- API keys are configured\n- quotas are not exhausted\n- target/query is too narrow\n- network/proxy settings are correct\n- your dork is syntactically reasonable\n\nTry a simpler query first.\n\n### AI Features Not Working\n\nCheck:\n\n- `gemini-cli` is installed\n- AI credentials/auth are configured correctly\n- the prompt is specific enough\n- the selected quantity is reasonable for testing\n\nTest with a simple prompt first:\n\n```bash\necho example.com | banshee -ai \"find admin panels\" -quantity 3 -v\n```\n\n### Too Much Noise in Results\n\nTry:\n\n- narrower prompts\n- smaller quantity\n- analysis/filter flags\n- output files and manual review\n- tech detection before broad searching\n\n### Too Slow\n\nPerformance depends on:\n\n- enabled features\n- API limits\n- target volume\n- analysis modes\n- network conditions\n\nUse the docs page for tuning guidance and strategy recommendations.\n\n## Security, Ethics, and Responsible Usage\n\nUse Banshee only on systems and assets you own or are explicitly authorized to test.\n\nAlways respect:\n\n- program scope\n- rate limits\n- terms of service\n- local laws and regulations\n- responsible disclosure practices\n\nBanshee is a search and analysis tool.\n\nMisuse is your responsibility.\n\nIf you are doing bug bounty hunting:\n\n- read the program policy first\n- confirm target scope before scanning\n- avoid broad scans outside authorization\n- verify findings before reporting\n- redact sensitive data in reports when required\n\n## Operational Tips for Bug Bounty Hunters\n\nThese are intentionally simple and practical.\n\n- Keep separate output files per program.\n- Re-scan high-value targets periodically.\n- Use low quantities first, then expand.\n- Save interesting prompts that worked well.\n- Revisit targets with `--learn` and `--smart` after accumulating history.\n- Use document analysis for programs with lots of PDFs and public docs.\n- Review results manually before escalating any issue.\n\nFor advanced hunting playbooks, use the docs page.\n\n## FAQ (Mini)\n\n### Is this README the full documentation?\n\nNo.\n\nThis README is a practical getting-started guide.\n\nUse `https://vulnpire.github.io/Banshee-AI` or `docs/index.html` for full documentation.\n\n### Can I learn Banshee without installing it first?\n\nYes.\n\nOpen the docs interface and use the simulated shell.\n\nIt includes a virtual `banshee` binary and a very basic CTF-like flow.\n\n### Is the docs shell a real terminal?\n\nNo.\n\nIt is a JavaScript simulation for learning and demos.\n\n### Where do I find all flags?\n\nUse the web docs reference section:\n\n- `https://vulnpire.github.io/Banshee-AI`\n- `docs/index.html`\n\n### Where do I find output/cache/internal file explanations?\n\nUse the web docs sections for:\n\n- outputs\n- caches\n- internal files\n- configuration\n\n### Where do I report issues or ask for help?\n\nSee the Support section below.\n\n## Versioning and Documentation Accuracy\n\nBanshee evolves quickly.\n\nSome flags, workflows, and defaults may change across versions.\n\nThe web docs should be treated as the primary source of usage guidance.\n\nIf you notice a mismatch between this README and the docs page:\n\n- Prefer the docs page for detailed behavior\n- Check `banshee --help` locally\n- Open an issue or contact support\n\n## Contributing\n\nContributions are welcome.\n\nUseful contribution types include:\n\n- bug fixes\n- feature improvements\n- documentation improvements\n- examples and recipes\n- UX improvements in the docs interface\n\nBefore making large changes:\n\n- check existing issues/discussions\n- describe the problem clearly\n- explain expected behavior\n- include reproduction steps where possible\n\nIf you are updating documentation:\n\n- keep README concise and onboarding-focused\n- put detailed reference material in the web docs\n- keep examples realistic and safe\n\n## Support\n\nSupport: `gorkem@cyberpars.com`\n\nIf you are reaching out for help, include:\n\n- command used\n- target type (sanitized if needed)\n- relevant flags\n- error/output snippet\n- what you expected\n\nThis makes troubleshooting much faster.\n\n## Final Notes\n\nBanshee works best when used as a workflow, not just a single command.\n\nStart simple.\n\nSave outputs.\n\nLet SMART/LEARN build context over time.\n\nUse the web docs for the full picture.\n\nPrimary docs:\n\n- `https://vulnpire.github.io/Banshee-AI`\n- `docs/index.html`\n\nStay within scope.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvulnpire%2Fbanshee-ai","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fvulnpire%2Fbanshee-ai","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvulnpire%2Fbanshee-ai/lists"}