{"id":51197072,"url":"https://github.com/haskaomni/serenity-skill","last_synced_at":"2026-07-16T06:00:46.143Z","repository":{"id":361537909,"uuid":"1254842258","full_name":"haskaomni/serenity-skill","owner":"haskaomni","description":null,"archived":false,"fork":false,"pushed_at":"2026-07-15T09:24:20.000Z","size":84,"stargazers_count":606,"open_issues_count":0,"forks_count":93,"subscribers_count":1,"default_branch":"main","last_synced_at":"2026-07-15T11:14:39.817Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":null,"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/haskaomni.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":"2026-05-31T04:13:42.000Z","updated_at":"2026-07-15T09:24:25.000Z","dependencies_parsed_at":null,"dependency_job_id":null,"html_url":"https://github.com/haskaomni/serenity-skill","commit_stats":null,"previous_names":["haskaomni/serenity-skill"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/haskaomni/serenity-skill","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/haskaomni%2Fserenity-skill","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/haskaomni%2Fserenity-skill/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/haskaomni%2Fserenity-skill/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/haskaomni%2Fserenity-skill/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/haskaomni","download_url":"https://codeload.github.com/haskaomni/serenity-skill/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/haskaomni%2Fserenity-skill/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":35532646,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-07-16T02:00:06.687Z","response_time":83,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"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":[],"created_at":"2026-06-27T21:31:06.298Z","updated_at":"2026-07-16T06:00:46.136Z","avatar_url":"https://github.com/haskaomni.png","language":null,"funding_links":[],"categories":["Data Analysis Skills"],"sub_categories":[],"readme":"# Serenity Skills\n\nCodex skills for translating market information into testable investment research frameworks.\n\n## Skills\n\n- `serenity-alpha`: translates market news into alpha hypotheses using a `news -\u003e demand -\u003e financial statements -\u003e small-cap elasticity -\u003e validation path` framework.\n- `bayesian-intrinsic-growth-valuation`: estimates a company's intrinsic 3-5 year growth rate with Bayesian hypothesis updates, then compares it with market-implied growth and FOMO.\n- `gf-dma-health-index`: scores whether a stock's current valuation/trend health is supported by fundamental growth speed, DMA trend speed, divergence, escape ratio, and estimate revisions.\n- `tam-adj-peg`: evaluates growth-stock valuation by adjusting traditional PEG with TAM runway and business quality.\n- `buy-side-equity-research-memo`: generates source-backed buy-side equity research memos from a ticker, with investment view, SEC/IR-backed financial analysis, valuation scenarios, catalysts, risks, and Serenity framework cross-checks.\n- `juglar-cycle-stock-stage`: classifies a stock and its core industry across Juglar fixed-asset investment cycle stages with probabilities, evidence, counter-evidence, migration signals, and investment implications.\n\n## 直接使用托管版\n\n如果你觉得本地安装、配置 Codex skill 或维护环境不方便，也可以订阅 [@iamai_omni](https://x.com/iamai_omni/creator-subscriptions/subscribe)，然后访问 [app.k2ai.dev](https://app.k2ai.dev) 直接使用托管版。订阅版不需要你自己搭建，并且会附赠许多其他功能，适合想快速上手、持续使用 Serenity 体系的用户。也可以扫码直接打开订阅页：\n\n\u003cimg src=\"docs/assets/iamai-omni-subscribe-qr.png\" alt=\"Subscribe to @iamai_omni QR code\" width=\"220\"\u003e\n\n## Repository Layout\n\n```text\nskills/\n├── serenity-alpha/\n│   ├── SKILL.md\n│   ├── agents/openai.yaml\n│   └── references/original-framework.md\n├── bayesian-intrinsic-growth-valuation/\n│   ├── SKILL.md\n│   ├── agents/openai.yaml\n│   └── references/original-framework.md\n├── gf-dma-health-index/\n│   ├── SKILL.md\n│   ├── agents/openai.yaml\n│   └── references/original-framework.md\n├── tam-adj-peg/\n│   ├── SKILL.md\n│   ├── agents/openai.yaml\n│   └── references/original-framework.md\n├── buy-side-equity-research-memo/\n│   ├── SKILL.md\n│   ├── agents/openai.yaml\n│   └── references/original-framework.md\n└── juglar-cycle-stock-stage/\n    ├── SKILL.md\n    ├── agents/openai.yaml\n    └── references/original-framework.md\n```\n\nEach subdirectory under `skills/` is an independent Codex skill. Codex discovers a skill from its `SKILL.md`; files under `references/` are supporting material loaded only when needed.\n\n## Mermaid Visualizations\n\nAll six skills use adaptive Mermaid visualization in full reports. The default target is 2-4 decision-useful diagrams, selected for the framework rather than repeated mechanically. Short answers and reports with incomplete data may use fewer diagrams.\n\n- Stable relationship views use `flowchart`, `pie`, or `stateDiagram` where possible.\n- Numerical comparisons may use `xychart-beta`; matrices and catalyst views may use `quadrantChart` or `timeline` as progressive enhancement.\n- Enhanced diagrams always keep the adjacent Markdown table, so the analysis remains complete when a renderer does not support that Mermaid type.\n- Diagrams use only values already present in the report, remain consistent with the tables, and never replace citations, assumptions, risks, or falsification conditions.\n- Each diagram is placed beside the section it explains and followed by a concise analytical takeaway.\n\n## Install\n\nCopy all skills into your Codex skills folder:\n\n```bash\nmkdir -p \"${CODEX_HOME:-$HOME/.codex}/skills\"\ncp -R skills/* \"${CODEX_HOME:-$HOME/.codex}/skills/\"\n```\n\nOr install only one skill:\n\n```bash\nmkdir -p \"${CODEX_HOME:-$HOME/.codex}/skills\"\ncp -R skills/serenity-alpha \"${CODEX_HOME:-$HOME/.codex}/skills/\"\ncp -R skills/bayesian-intrinsic-growth-valuation \"${CODEX_HOME:-$HOME/.codex}/skills/\"\ncp -R skills/gf-dma-health-index \"${CODEX_HOME:-$HOME/.codex}/skills/\"\ncp -R skills/tam-adj-peg \"${CODEX_HOME:-$HOME/.codex}/skills/\"\ncp -R skills/buy-side-equity-research-memo \"${CODEX_HOME:-$HOME/.codex}/skills/\"\ncp -R skills/juglar-cycle-stock-stage \"${CODEX_HOME:-$HOME/.codex}/skills/\"\n```\n\nThen invoke `$serenity-alpha` for news-to-alpha analysis, `$bayesian-intrinsic-growth-valuation` for Bayesian intrinsic-growth valuation, `$gf-dma-health-index` for trend/valuation health scoring, `$tam-adj-peg` for TAM-adjusted PEG valuation, `$buy-side-equity-research-memo` for a full buy-side stock memo, or `$juglar-cycle-stock-stage` for Juglar fixed-asset cycle stage classification. If a newly copied skill does not appear, restart Codex.\n\n## What They Do\n\n`serenity-alpha`:\n\n- Separates narrative news from already-observable demand changes.\n- Maps demand into revenue, margin, cash-flow, and balance-sheet impact.\n- Searches for small, pure, potentially misclassified beneficiaries.\n- Builds 1-4 quarter verification chains and falsification points.\n- Frames position posture conditionally as research, not personalized investment advice.\n\n`bayesian-intrinsic-growth-valuation`:\n\n- Converts fundamentals, industry cycle, TAM, valuation, and new information into H0-H5 growth-hypothesis probabilities.\n- Updates 3-5 year revenue CAGR assumptions with Bayesian reasoning instead of surface bullish/bearish labels.\n- Separates intrinsic growth updates from FOMO, narrative heat, and valuation multiple expansion.\n- Compares weighted intrinsic growth with market-implied growth.\n- Classifies valuation as undervalued, fair, expensive but tradable, or bubble-like.\n\n`gf-dma-health-index`:\n\n- Combines revenue growth, profit growth, estimate revisions, and 20/50/100/200DMA structure.\n- Scores fundamental-DMA match, price-DMA divergence, trend parallelism, and revision confirmation.\n- Classifies the current state from healthy momentum to broken/escaping.\n\n`tam-adj-peg`:\n\n- Adjusts traditional PEG with TAM Runway Factor and Quality Factor.\n- Separates growth speed from growth duration, TAM capture, pricing power, cyclicality, dilution, and execution risk.\n- Classifies valuation from very cheap to very expensive and maps it to core, high-beta, turnaround, option-like, or cyclical position framing.\n\n`juglar-cycle-stock-stage`:\n\n- Maps a ticker to its core fixed-asset investment cycle, such as semiconductors, memory, AI data centers, power equipment, industrial automation, property-chain, engineering machinery, chemicals, shipping, or optical communications.\n- Scores demand, ASP, margins, capex, inventory, capacity release, customer behavior, and capital-market reaction from -2 to +2.\n- Outputs probabilities across Stage 1 recovery, Stage 2 expansion, Stage 3 overheating, Stage 4 downturn, and Stage 5 clearing.\n- Separates industry cycle stage, company operating position, and stock valuation stage.\n- Lists core evidence, counter-evidence, migration signals, investment implications, and strategy framing.\n\n`buy-side-equity-research-memo`:\n\n- Starts with rating bias, target-price range, upside/downside, key debate, and thesis breakpoint.\n- Uses SEC filings, company IR, earnings calls, presentations, and other current sources to anchor key facts.\n- Builds industry-chain, competitive-position, financial-statement, value-driver, SOTP/valuation, and Bull/Base/Bear scenario sections.\n- Integrates Serenity Alpha, Bayesian Intrinsic Growth, TAM-Adj-PEG, and GF-DMA lenses only when they improve the investment decision.\n- Lists catalysts, risks, variant perception, monitoring dashboard, and source list for follow-up research.\n\n## License\n\nMIT\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhaskaomni%2Fserenity-skill","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhaskaomni%2Fserenity-skill","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhaskaomni%2Fserenity-skill/lists"}