# muxuuu/serenity-skill

Serenity-inspired Agent Skill for supply-chain bottleneck stock research

Repository: https://github.com/muxuuu/serenity-skill
Canonical: https://ross.abutalabs.com/products/serenity-skill
Language: Python
License: MIT
License Family: permissive
Topics: agent-skills, ai-agents, claude-code, codex, investment-research, stock-research, supply-chain
Last push: 2026-05-05T03:23:53+00:00

## Health v2 (maintenance only)
Score: 50/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 80, release rhythm 35, longevity 8
- inputs: {"age_days": 121, "days_push": 120, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, young
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3870, forks 606 (observed 2026-08-28T04:08:24.193151+00:00)

## What it is
An Agent Skill (SKILL.md-based prompt/methodology package) that encodes a Serenity-style investment research workflow for supply-chain bottleneck stock and fund analysis. It guides AI agents like Claude Code or Codex to decompose hot themes into industry chains, identify chokepoint segments, screen candidate stocks and funds, and produce prioritized research lists backed by filings and public evidence.

## Use cases
- research which segments of the AI semiconductor supply chain are worth investing in
- find which robot industry chain components are closest to real supply bottlenecks
- challenge whether a stock is truly a core CPO supplier or just riding the hype
- compare candidate stocks and rank them by clarity of their upside logic
- identify which upstream segments a robotics-themed ETF or fund should expose to
- build a repeatable screening workflow for turning hot news into research directions

## When to choose
- you invest in hot tech themes (AI chips, robotics, optics, innovative drugs) and need a structured first-pass research process
- you use Claude Code, Codex, or another agent that supports SKILL.md skills and want it to do supply-chain equity research
- you want AI to turn vague market hype into evidence-based, prioritized stock and fund watchlists

## When to avoid
- you need automated trading, real-time market data feeds, or execution - this is research support only
- you require guaranteed financial accuracy - outputs are AI reasoning over public sources and must be verified
- you need a traditional backtesting or quantitative analysis library rather than a prompt-driven research methodology

## Facets
- artifact type: plugin
- maturity: active
- function: agent-framework, prompt-engineering, rag, search-engine, analytics
- domain: fintech, artificial-intelligence, large-language-models, data-science, developer-tools
- platform: python, cli, cross-platform
- tags: agent-skills, claude-code, codex, investment-research, stock-research, supply-chain-analysis, equity-research, prompt-pack, skyll-md, chinese-language, ai-agents

## Member repositories
- muxuuu/serenity-skill (main) score 50

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:24.193151+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:25:48.733071+00:00, confidence not recorded.
  - readme: https://github.com/muxuuu/serenity-skill (fetched 2026-08-28T04:08:24.193151+00:00, sha 13b9ea47339f)
- Data as of 2026-08-30T08:39:29.467469+00:00.