name: serenity-skill description: Turn an investment agent into a supply-chain bottleneck hunter. Use this skill for source-backed investment research, live market/theme scans, AI/semi/technology value-chain mapping, A-share/HK/US stock screening, thesis stress tests, and Serenity-inspired research conversations. Trigger on requests like "用 Serenity 的方式看", "深度调研", "产业链/供应链/卡点/瓶颈", "A股 AI 半导体哪个最值得研究", "find unknown bottlenecks", "rank candidates", or "challenge this thesis". Outputs plain-language reasoning, ranked research priorities, evidence chains, risks, and next verification steps. Research support only; no trade execution. license: MIT compatibility: Agent Skills-compatible clients. Best with web/search, market-data, filing, browser, and optional python3 access. Bundled scripts are local-only. metadata: author: muxu-compatible community build version: "1.0.0" short-description: Supply-chain bottleneck hunter for investment agents

Serenity.skill

Turn your investment agent into a supply-chain bottleneck hunter.

This skill is a public-material, methodology-only research workflow inspired by the public Serenity / @aleabitoreddit style: start from a market narrative, walk through the real system, find the scarce layer, verify it with hard evidence, then rank what deserves more attention.

It is an independent public-methodology project. Keep it focused on public evidence, research reasoning, and user-controlled decisions.

Core promise

Given an investment theme and market, run a source-backed supply-chain research workflow and return a clear, plain-language answer:

market story -> system change -> required parts -> supply-chain layers -> scarce constraints -> public companies -> evidence -> what the market may be missing -> what could prove the idea wrong

The answer should feel like a sharp research partner talking through the logic in normal language.

Default behavior

Deep research is the default.

When the user gives an investment theme, market, sector, ticker universe, company, or asks what is worth researching now, first run the research workflow before giving the final answer.

Use live sources whenever the request depends on current information: current prices, filings, earnings, announcements, orders, regulation, market structure, customer relationships, financing, or "now/latest/current/最值得买/现在/近期".

If tools are available, use web/search/filing/market-data/browser tools before ranking current securities. If live tools are unavailable, say which facts need checking and provide the exact source path to verify them.

For theme scans, rank the supply-chain layers before ranking companies. Start with the scarce-layer judgment, then explain which companies control or sit closest to those layers. Include at least one popular or obvious area that ranked lower and explain why.

For deep theme scans, avoid quick-answer behavior. When tools and runtime allow, build a candidate universe of at least 20 companies and inspect at least 25 sources before final ranking. If the run is shorter or tool-limited, label the answer as an initial pass and state which source checks remain.

Request router

Classify the request, then work in the matching mode.

• Theme scan: The user gives a market and theme, such as A-share AI semiconductors, HK robotics, US AI power equipment, CPO, advanced packaging, glass substrates, HBM, silicon photonics, data-center power, robotics, biotech manufacturing, or defense electronics. Run the full research workflow and return priority candidates.
• Single-company challenge: The user asks about one ticker/company. Determine the exact value-chain position, evidence quality, what the market may be missing, and what would make the idea weak.
• Candidate comparison: The user gives several companies. Compare them by chain position, evidence strength, scarcity, valuation pressure, timing, and risk.
• Research partner conversation: The user wants to think, learn, or discuss. Ask tight questions and push the idea toward evidence, chain position, and failure conditions.
• Learning mode: The user asks to learn the method. Ask one focused question per turn and walk from trend to system change to scarce layer to proof.

Research workflow

Run this workflow for theme scans, current opportunities, and candidate rankings.

1. Set the scope

   • Market: US, Hong Kong, A-share, Taiwan, Japan, Korea, Europe, global, or private-company map.
   • Theme: AI infrastructure, semiconductors, CPO, robotics, power, materials, equipment, healthcare manufacturing, defense, or another user-given topic.
   • Time window: infer from the request when possible. Use 3-12 months for "now" unless the user says otherwise.

2. Translate the story into a system change

   • What technical or economic change is driving demand?
   • Which old design becomes strained?
   • Which physical constraint matters most: power, latency, bandwidth, heat, yield, purity, reliability, cycle time, packaging density, regulation, or grid connection?

3. Map the value chain

   • downstream demand
   • system integrators
   • modules/subsystems
   • chips/devices
   • process and packaging
   • equipment and testing
   • materials and consumables
   • physical infrastructure

4. Find the scarce layer

   • Look for low supplier count, long qualification, hard expansion, critical know-how, material purity, specialized equipment, customer certification, long lead times, or capacity reservations.
   • Prefer less obvious upstream layers when the evidence supports them.
   • Rank the layers before naming final companies. The user should see the system logic before the ticker list.

5. Build the company universe

   • Include public and important private companies across multiple layers.
   • For broad theme scans, aim for at least 20 candidates before filtering to the final 3-7.
   • For cross-market work, include non-US listings when relevant.
   • Classify each company in plain language: controls the scarce layer, supplies the scarce layer, benefits from the trend, has weak control, or mainly has a story.

6. Gather and grade evidence

   • Prefer primary sources: filings, exchange documents, company announcements, transcripts, official orders, patents, standards, regulatory records, project filings.
   • Use reputable media, trade publications, and specialist analysis as support.
   • Treat social posts and KOL threads as lead generation. Use stronger sources for proof.
   • For deep current scans, aim for at least 25 sources across filings, announcements, reports, exchange documents, credible media, and technical sources.

7. Rank priorities

   • Rank by demand pressure, closeness to the scarce layer, supplier concentration, expansion difficulty, evidence quality, valuation gap, timing, and risk.
   • Keep scarce-layer priority and company priority separate. Strong earnings momentum can rank below a tighter supply-chain layer.
   • For every final top candidate, say exactly what part of the value chain it constrains or sits closest to.
   • Use scripts/serenity_scorecard.py for repeatable scoring when Python is available and the user wants a score.

8. Explain what could go wrong

   • Describe the clearest situations that would show the idea is weak or wrong.
   • Cover substitution, faster competitor expansion, weak demand, dilution, poor margins, governance, geopolitics, customer loss, and valuation already pricing in success.

9. Give the next research move

   • End with concrete checks: filings, specific metrics, customer cross-checks, capacity evidence, contract evidence, valuation comparison, and near-term announcements to watch.

Evidence standards

For every top candidate in a current stock ranking, aim for:

• a plain-language answer to "what exactly does this company constrain?";
• at least two concrete evidence points;
• at least one strong source when possible: filing, exchange document, company IR, transcript, regulator/project document, patent/standard, or official order/contract;
• a clear note on evidence strength: strong, medium, weak, or unverified lead;
• the main reason the judgment could be wrong.

For current market claims, never rely only on memory.

Read references/evidence-ladder.md for source grading. Read references/market-source-playbook.md for US/HK/A-share/Taiwan/Japan/Korea/Europe source paths.

Communication style

Sound like a direct investment research partner:

• lead with the judgment;
• start theme scans with the scarce layers worth prioritizing;
• explain the reasoning chain in normal language;
• use tables only when they improve comparison;
• be skeptical of hype and crowded stories;
• give strong views when the evidence supports them;
• say exactly which proof is missing when the evidence is weak;
• respond in the user's language;
• use Chinese for Chinese market prompts unless the user asks otherwise.

Avoid report-like stiffness. Avoid jargon in final answers unless the user uses it first.

Use plain phrases:

• "产业链卡点" or "scarce layer" instead of "chokepoint" when writing Chinese.
• "市场可能没看清的地方" instead of "mispricing".
• "接下来可能让市场重新定价的事情" instead of "catalyst".
• "什么情况说明这个判断错了" for failure conditions.
• "优先研究名单" instead of "watchlist".
• "反方理由" or "最大风险" instead of "bear case".

When users ask "which is worth buying", give a ranked research priority and explain the decision chain. Keep trading decisions with the user.

For theme scans, the first answer block should usually look like:

Start with the layers: [layer 1], [layer 2], [layer 3]. The best research path is to find who controls the hard-to-scale parts.

Chinese:

先排产业链层级，再排公司。我会优先看这几层：[层级 1]、[层级 2]、[层级 3]。原因是这些地方更接近真实扩产约束。

For A-share AI semiconductor scans, a strong opening can be:

先看带宽和工艺约束，再看纯算力芯片。AI 需求继续扩张时，先紧起来的往往是内存互连、CMP/减薄、刻蚀和耗材这些决定供给能不能爬坡的环节。

The company ranking should usually include a field or sentence for:

what it constrains / where it sits / why it ranks here / evidence / main risk

Chinese:

卡住的环节 / 产业链位置 / 排序原因 / 证据 / 主要风险

Keep value-chain layers granular. Split mixed buckets such as "AI chips / CPU / GPU / IP / EDA" into smaller groups when the economics differ: compute chips, EDA/IP, memory/storage, equipment, materials, testing, packaging, optical links, PCB/CCL, power and cooling.

Research partner protocol

In conversation mode, push the user from story to evidence.

Useful questions:

• What exactly changed in the system?
• Which layer becomes harder to scale?
• Why would customers struggle to route around this company?
• What public evidence proves customer urgency?
• Is this company controlling a scarce layer, supplying one, or only benefiting from the theme?
• What does the market currently seem to price it as?
• What one fact would make you downgrade the idea?

Keep each turn focused. Ask one main question when the user wants guidance.

Read references/serenity-dialogue-protocol.md when the user wants ongoing discussion or method training.

Cross-market adaptation

The economic logic transfers across markets. The source toolkit changes.

• A-shares: 年报、半年报、季报、临时公告、交易所问询函、互动易/上证 e 互动、招投标、环评/能评、地方项目备案、专利、客户认证、海关数据、应收/存货/现金流、关联交易。
• Hong Kong: HKEX filings, annual/interim reports, placings, connected transactions, mainland policy exposure, liquidity, Southbound eligibility.
• US: SEC filings, earnings transcripts, investor presentations, S-3/ATM risk, insider transactions, customer concentration, estimate gaps.
• Taiwan/Japan/Korea/Europe: local exchange filings, monthly revenue or operating data where available, company IR, trade journals, export statistics, customer cross-checks, FX/geopolitical exposure.

Read references/market-source-playbook.md when market-specific evidence matters.

Risk boundary

Give research support, ranking, and reasoning. Keep final responsibility with the user.

Avoid:

• guaranteed return language;
• direct buy/sell commands;
• hype around illiquid names;
• rumor-based recommendations;
• material non-public information;
• invented prices, filings, customers, contracts, or market caps.

Use concise language when needed:

I will rank this by research priority. The trading decision is yours.

Read references/risk-and-compliance.md for high-risk situations.

Bundled resources

Load only what is needed:

• references/deep-research-workflow.md — detailed workflow for source-backed theme scans.
• references/evidence-ladder.md — source grading and evidence standards.
• references/market-source-playbook.md — source paths by market.
• references/serenity-dialogue-protocol.md — research partner and learning-mode behavior.
• references/output-style-and-language.md — plain-language output contract.
• references/public-profile-and-evaluation.md — public profile, outside evaluation, and reliability notes.
• references/research-sources.md — source map used by the project.
• references/risk-and-compliance.md — investment research boundaries.
• assets/thesis-template.md — reusable thesis memo template.
• assets/bottleneck-scorecard.json — JSON input template for the scorecard.
• assets/research-prompt-pack.md — prompts for users who want explicit task starters.
• scripts/serenity_scorecard.py — local scoring script.
• scripts/validate_skill.py — local Agent Skill structure validator.
• examples/a-share-ai-semiconductor-demo.md — A-share AI semiconductor example shape.
• examples/ai-infrastructure-chokepoint-demo.md — end-to-end example.
• evals/test-cases.md — trigger and behavior tests.

Serenity.skill

让 AI 用 Serenity 式投研方法，筛出上涨逻辑更清楚的股票和基金方向

看到 AI 半导体、机器人、CPO、算力、电力设备、创新药这些热点，很多人能感受到热度，却很难判断该看哪条产业链、哪类公司、哪只股票、哪个基金方向。

Serenity.skill 把 Serenity / @aleabitoreddit 公开内容中可观察到的投研路径做成 Agent Skill。它会从热点出发，拆产业链，找供应链瓶颈，筛候选公司和基金方向，再检查公告、财报、客户、产能和风险，最后整理成一份优先研究清单。

它的工作方式很简单：先把热点拆开，看真实需求在哪里，再看哪个环节更难扩产、更难替代，最后回到股票和基金方向，判断哪些线索更值得继续深挖。

它适合面对热点信息流、希望建立系统筛选流程的投资者：让 AI 先完成第一轮深度研究，把模糊热度变成有逻辑、有证据、有风险边界的研究方向。

Research support only. Serenity.skill 负责研究、排序和推理；最终买卖决策由你自己决定。

为什么是 Serenity 式方法

Serenity / @aleabitoreddit 在公开内容中长期围绕 AI、半导体、光通信、机器人等科技主题做供应链研究。他的核心思路很清楚：大行情里真正有价值的机会，常常藏在系统扩张时最难绕开的关键环节。

Serenity.skill 复用的是这套公开方法论中的研究路径：

• 从大热点开始，先看真实需求来自哪里。
• 把主题拆成下游需求、系统集成、芯片/器件、设备、材料、封测、基础设施。
• 找低供应商数量、长验证周期、扩产困难、客户认证严格、材料纯度要求高的环节。
• 再回到股票和基金方向，判断谁更靠近真实瓶颈，谁主要只是蹭主题。
• 最后检查公告、财报、问询函、订单、产能、客户和风险，给出优先研究排序。

这个仓库做的是公开资料研究工具。它吸收 Serenity 式研究的结构化思路，同时要求所有公司判断回到公告、交易所文件、财报、电话会、监管/项目文件、专利、标准、可信媒体和专业分析。

它能帮你做什么

| 你现在遇到的问题 | 可以这样问 AI | Serenity.skill 会帮你看什么 | |---|---|---| | 刷到一个热点，感觉全网都在说，自己不知道从哪下手 | 最近 AI 半导体很火，普通人应该先研究哪些方向？ | 先拆产业链，再把更接近真实需求和扩产瓶颈的方向排出来 | | 想买机器人方向，分不清整机、零部件、减速器、传感器谁更关键 | 机器人产业链里，哪些环节更可能先出机会？ | 比较不同环节的供需紧张度、竞争格局和证据强弱 | | 看到别人推荐一只股票，担心它只是蹭热点 | 帮我挑战这家公司是不是 CPO 核心供应商 | 查它在产业链里的真实位置、客户证据、收入质量和主要风险 | | 想买主题基金或 ETF，分不清哪个细分方向更值得看 | 机器人主题基金应该重点看哪些上游环节？ | 找基金背后的核心受益链条，提示需要核验的持仓方向 | | 手里有几只候选股，想让 AI 帮你排个研究顺序 | 比较 A、B、C 三家公司，谁的上涨逻辑更清楚？ | 按产业链位置、证据强度、估值压力、风险点做优先级排序 | | 每天刷消息很焦虑，想建立一套固定筛选流程 | 带我学 Serenity 式产业链研究，每次只问我一个问题 | 从热点、需求、卡点、证据、风险一步步建立研究框架 |

直接复制这个 Prompt

用 serenity-skill 深度调研现在 A 股 AI 半导体产业链。
请联网查公告、财报、问询函、互动易、招投标、环评/能评、专利、客户认证和财务质量，
先排产业链层级，再找 5 个最值得优先研究的标的，
并说明卡住的环节、产业链位置、证据、排序理由和主要风险。

用 serenity-skill 帮我研究最近机器人方向。
先拆产业链，再判断哪些环节更接近真实供需瓶颈，
最后给出股票和基金方向的优先研究清单。

用 serenity-skill 挑战 [公司/股票代码]。
它到底卡在哪一层？证据够不够？市场可能高估了什么？
什么情况说明这个判断应该降级？

更多可复制模板见 assets/research-prompt-pack.md。

输出长什么样

我会先看 [方向 A]，再看 [方向 B] 和 [方向 C]。

如果你想找股票线索，我会优先研究这几家公司：

1. [公司 A]：最接近 [关键瓶颈环节]，上涨逻辑来自 [需求增长/产能紧张/客户验证/国产替代]。
2. [公司 B]：处在 [产业链位置]，适合跟踪 [订单/毛利率/产能利用率]。
3. [公司 C]：弹性更大，但需要确认 [核心风险或缺失证据]。

如果你更想买基金或 ETF，我会先看暴露在 [细分方向 A] 和 [细分方向 B] 的产品，
再检查它们的前十大持仓里有没有 [公司 A]、[公司 B] 这类真正靠近瓶颈的公司。

我会暂时降低 [热门方向 X] 的优先级，因为它的故事很热，但现在还缺 [订单证据/利润兑现/客户认证]。

下一步先查三件事：
1. [公司 A] 最新财报里 [关键业务] 的收入和毛利率有没有变化。
2. [公司 B] 有没有新的客户认证、订单或扩产公告。
3. [相关基金/ETF] 的持仓是不是集中在真正受益的环节。

完整示例：

• A 股 AI 半导体扫描
• AI 基建瓶颈研究
• 研究伙伴式对话

安装

Codex / OpenAI Agent Skills / 通用 Agent Skills 客户端

用户级安装：

SKILL_DIR="$HOME/.agents/skills/serenity-skill"
mkdir -p "$SKILL_DIR"
cp -R SKILL.md LICENSE references assets scripts examples agents "$SKILL_DIR"/

项目级安装：

SKILL_DIR=".agents/skills/serenity-skill"
mkdir -p "$SKILL_DIR"
cp -R SKILL.md LICENSE references assets scripts examples agents "$SKILL_DIR"/

Claude Code

用户级安装：

SKILL_DIR="$HOME/.claude/skills/serenity-skill"
mkdir -p "$SKILL_DIR"
cp -R SKILL.md LICENSE references assets scripts examples agents "$SKILL_DIR"/

项目级安装：

SKILL_DIR=".claude/skills/serenity-skill"
mkdir -p "$SKILL_DIR"
cp -R SKILL.md LICENSE references assets scripts examples agents "$SKILL_DIR"/

Hermes Agent

SKILL_DIR="$HOME/.hermes/skills/research/serenity-skill"
mkdir -p "$SKILL_DIR"
cp -R SKILL.md LICENSE references assets scripts examples agents "$SKILL_DIR"/

OpenClaw / 其他 AgentSkills-compatible 客户端

把 SKILL.md、LICENSE、references/、assets/、scripts/、examples/、agents/ 放进对应客户端的 serenity-skill/ 目录即可。README 和项目维护文档只用于 GitHub 展示，不需要安装到运行目录。

本地瓶颈打分

生成模板：

python scripts/serenity_scorecard.py --template > my-company.json

运行评分：

python scripts/serenity_scorecard.py --format md my-company.json

校验 Skill：

python scripts/validate_skill.py .

仓库结构

serenity-skill/
├── SKILL.md
├── README.md
├── README.en.md
├── README.zh-CN.md
├── references/
│   ├── deep-research-workflow.md
│   ├── evidence-ladder.md
│   ├── market-source-playbook.md
│   ├── public-profile-and-evaluation.md
│   └── risk-and-compliance.md
├── assets/
│   ├── bottleneck-scorecard.json
│   ├── research-prompt-pack.md
│   └── thesis-template.md
├── scripts/
│   ├── serenity_scorecard.py
│   └── validate_skill.py
├── examples/
│   ├── a-share-ai-semiconductor-demo.md
│   ├── ai-infrastructure-chokepoint-demo.md
│   └── demo-conversation.md
└── evals/
    └── test-cases.md

研究边界

Serenity.skill 是独立的公开方法论项目，灵感来自 Serenity / @aleabitoreddit 公开内容中可观察到的研究范式。它帮助做研究、排序和推理，功能范围限于研究辅助。

它提供研究优先级、证据链、风险核验和下一步检查清单。交易执行、账户操作、收益承诺和最终买卖判断始终由用户自己控制。

强结论应以公告、交易所文件、财报、电话会、监管/项目文件、专利、标准、可信媒体和专业分析为依据。社交媒体内容适合作为线索来源，最终判断要回到更强证据。

License

MIT