https://github.com/haskaomni/serenity-skill
https://github.com/haskaomni/serenity-skill
Last synced: 2 days ago
JSON representation
- Host: GitHub
- URL: https://github.com/haskaomni/serenity-skill
- Owner: haskaomni
- License: mit
- Created: 2026-05-31T04:13:42.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2026-07-15T09:24:20.000Z (3 days ago)
- Last Synced: 2026-07-15T11:14:39.817Z (3 days ago)
- Size: 82 KB
- Stars: 606
- Watchers: 1
- Forks: 93
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-agent-skills - serenity-skill - Codex skills that turn market news and company data into testable investment research frameworks, valuation checks, and buy-side memos. (Data Analysis Skills)
README
# Serenity Skills
Codex skills for translating market information into testable investment research frameworks.
## Skills
- `serenity-alpha`: translates market news into alpha hypotheses using a `news -> demand -> financial statements -> small-cap elasticity -> validation path` framework.
- `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.
- `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.
- `tam-adj-peg`: evaluates growth-stock valuation by adjusting traditional PEG with TAM runway and business quality.
- `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.
- `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.
## 直接使用托管版
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：
## Repository Layout
```text
skills/
├── serenity-alpha/
│ ├── SKILL.md
│ ├── agents/openai.yaml
│ └── references/original-framework.md
├── bayesian-intrinsic-growth-valuation/
│ ├── SKILL.md
│ ├── agents/openai.yaml
│ └── references/original-framework.md
├── gf-dma-health-index/
│ ├── SKILL.md
│ ├── agents/openai.yaml
│ └── references/original-framework.md
├── tam-adj-peg/
│ ├── SKILL.md
│ ├── agents/openai.yaml
│ └── references/original-framework.md
├── buy-side-equity-research-memo/
│ ├── SKILL.md
│ ├── agents/openai.yaml
│ └── references/original-framework.md
└── juglar-cycle-stock-stage/
├── SKILL.md
├── agents/openai.yaml
└── references/original-framework.md
```
Each 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.
## Mermaid Visualizations
All 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.
- Stable relationship views use `flowchart`, `pie`, or `stateDiagram` where possible.
- Numerical comparisons may use `xychart-beta`; matrices and catalyst views may use `quadrantChart` or `timeline` as progressive enhancement.
- Enhanced diagrams always keep the adjacent Markdown table, so the analysis remains complete when a renderer does not support that Mermaid type.
- Diagrams use only values already present in the report, remain consistent with the tables, and never replace citations, assumptions, risks, or falsification conditions.
- Each diagram is placed beside the section it explains and followed by a concise analytical takeaway.
## Install
Copy all skills into your Codex skills folder:
```bash
mkdir -p "${CODEX_HOME:-$HOME/.codex}/skills"
cp -R skills/* "${CODEX_HOME:-$HOME/.codex}/skills/"
```
Or install only one skill:
```bash
mkdir -p "${CODEX_HOME:-$HOME/.codex}/skills"
cp -R skills/serenity-alpha "${CODEX_HOME:-$HOME/.codex}/skills/"
cp -R skills/bayesian-intrinsic-growth-valuation "${CODEX_HOME:-$HOME/.codex}/skills/"
cp -R skills/gf-dma-health-index "${CODEX_HOME:-$HOME/.codex}/skills/"
cp -R skills/tam-adj-peg "${CODEX_HOME:-$HOME/.codex}/skills/"
cp -R skills/buy-side-equity-research-memo "${CODEX_HOME:-$HOME/.codex}/skills/"
cp -R skills/juglar-cycle-stock-stage "${CODEX_HOME:-$HOME/.codex}/skills/"
```
Then 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.
## What They Do
`serenity-alpha`:
- Separates narrative news from already-observable demand changes.
- Maps demand into revenue, margin, cash-flow, and balance-sheet impact.
- Searches for small, pure, potentially misclassified beneficiaries.
- Builds 1-4 quarter verification chains and falsification points.
- Frames position posture conditionally as research, not personalized investment advice.
`bayesian-intrinsic-growth-valuation`:
- Converts fundamentals, industry cycle, TAM, valuation, and new information into H0-H5 growth-hypothesis probabilities.
- Updates 3-5 year revenue CAGR assumptions with Bayesian reasoning instead of surface bullish/bearish labels.
- Separates intrinsic growth updates from FOMO, narrative heat, and valuation multiple expansion.
- Compares weighted intrinsic growth with market-implied growth.
- Classifies valuation as undervalued, fair, expensive but tradable, or bubble-like.
`gf-dma-health-index`:
- Combines revenue growth, profit growth, estimate revisions, and 20/50/100/200DMA structure.
- Scores fundamental-DMA match, price-DMA divergence, trend parallelism, and revision confirmation.
- Classifies the current state from healthy momentum to broken/escaping.
`tam-adj-peg`:
- Adjusts traditional PEG with TAM Runway Factor and Quality Factor.
- Separates growth speed from growth duration, TAM capture, pricing power, cyclicality, dilution, and execution risk.
- Classifies valuation from very cheap to very expensive and maps it to core, high-beta, turnaround, option-like, or cyclical position framing.
`juglar-cycle-stock-stage`:
- 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.
- Scores demand, ASP, margins, capex, inventory, capacity release, customer behavior, and capital-market reaction from -2 to +2.
- Outputs probabilities across Stage 1 recovery, Stage 2 expansion, Stage 3 overheating, Stage 4 downturn, and Stage 5 clearing.
- Separates industry cycle stage, company operating position, and stock valuation stage.
- Lists core evidence, counter-evidence, migration signals, investment implications, and strategy framing.
`buy-side-equity-research-memo`:
- Starts with rating bias, target-price range, upside/downside, key debate, and thesis breakpoint.
- Uses SEC filings, company IR, earnings calls, presentations, and other current sources to anchor key facts.
- Builds industry-chain, competitive-position, financial-statement, value-driver, SOTP/valuation, and Bull/Base/Bear scenario sections.
- Integrates Serenity Alpha, Bayesian Intrinsic Growth, TAM-Adj-PEG, and GF-DMA lenses only when they improve the investment decision.
- Lists catalysts, risks, variant perception, monitoring dashboard, and source list for follow-up research.
## License
MIT