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Serenity Radar

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lanfuli
serenity-radar

Use @aleabitoreddit ("Serenity")'s full mention archive (built by the follow-aleabito skill) to anticipate where her attention is moving and generate candidate ideas in her style. Two modes — (1) RADAR reads the live mention data for attention momentum (which tickers she is heating up on, new entrants, conviction core, theme rotation) via scripts/radar.js; (2) GENERATOR applies her empirically-mined patterns (theme-rotation logic, selection signature, catalyst playbook) to propose her likely next focus. Every candidate is gated through the serenity-method checklist. This is a CANDIDATE GENERATOR + CHECKLIST, never an oracle or buy/sell signal. Trigger on "what is Serenity ramping on / her next pick / aleabito radar / predict her next move / generate ideas like her / 她下一个可能看什么".

Overview

Publisherlanfuli
Repositoryaleabito-serenity-skills
Skill nameserenity-radar
Stars
94
Forks
18
Bundled files
3
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by lanfuli on GitHub. Read the source before you install it.

Installation

Install the Serenity Radar AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/lanfuli/aleabito-serenity-skills/tree/main/skills/serenity-radar
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/lanfuli/aleabito-serenity-skills.git /tmp/aleabito-serenity-skills
mkdir -p .claude/skills
cp -r /tmp/aleabito-serenity-skills/skills/serenity-radar .claude/skills/serenity-radar
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Serenity Radar in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Serenity Radar on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Serenity Radar is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Serenity Radar

A data-driven companion to serenity-method. Where serenity-method teaches how she analyzes, this skill uses her actual 11-month archive (2025-07-02 → present, ~6,120 posts / 750 tickers) to estimate where her attention is going and to generate candidates the way she would.

What this is NOT. Not a predictor, not a buy/sell signal, not "she will pump X next." It is a candidate generator + checklist. A single account is fragile; virality ≠ correctness; her archive has survivorship bias (winners get re-cited, losers fade). Read the Caveats section before using output. Always end with: 仅作信息跟踪,不构成投资建议。

Prerequisites

  • The mention archive must exist (the follow-aleabito skill produces it). Default path: $FOLLOW_ALEABITO_REPORTS_DIR/aleabito-mentions-events.csv (else the workspace reports/).
  • Keep it current with follow-aleabito's incremental fetch (analyze-mentions.js --incremental --resume) before running radar, so signals reflect the latest days.
  • For the analytical gate, use the local serenity-method skill.

Mode 1 — RADAR (data-driven, run this first)

Run the signal extractor:

bash
FOLLOW_ALEABITO_REPORTS_DIR="<reports dir>" node skills/serenity-radar/scripts/radar.js --window 14 --top 20
# add --json for machine-readable output; --asof YYYY-MM-DD to evaluate a past date; --window 7 for a tighter read

It prints four signal blocks (see references/signals.md for the exact math):

  • 🔥 Heating — tickers whose mention count is accelerating (recent window vs prior window). This is the core "she's ramping attention here" signal.
  • 🆕 New entrants — tickers that first appeared within the window. Candidate next focus — she often seeds a name quietly, then ramps.
  • 🎯 Conviction watch — high recent volume + sustained + still active. Her core book right now (defended, repeated).
  • 🔄 Theme rotation — theme mention-share recent vs prior. Tells you which narrative she is rotating into / out of.

How to read it: a name that is both a New entrant and Heating, in a theme that is rotating up, is the strongest "emerging focus" signal. A Conviction-watch name that is cooling (falling out of Heating) may be maturing toward exit/realization. Cross-check a heating name's recent posts (via follow-aleabito) to confirm it is a genuine thesis, not a one-off reply.

Mode 2 — GENERATOR (pattern-driven, for "her likely next move")

When the user wants ideas she hasn't surfaced yet, apply her empirical patterns (full detail in references/patterns.md). Her behavior is remarkably consistent; the three levers that predict her next focus:

  1. Move UP the supply chain — from today's hot end-product to the upstream chokepoint that isn't priced. (She went interconnect → laser → InP substrate → red phosphorus.) Ask: what is the bottleneck of the current bottleneck?
  2. Move EARLIER in the cycle — front-run a dated catalyst (ETF approval, index inclusion, earnings read-through, government filing, M&A). Ask: what catalyst is ~1-2 quarters out that the market hasn't mapped?
  3. Move SMALLER / less-covered — toward a sub-$3B, designed-in, often FUD-labelled name. Ask: who actually does the work (the subsidiary / upstream supplier), not the headline brand?

Generate 3-5 candidates by running these levers off the current Heating/Conviction themes, then gate each.

The gate (mandatory for every candidate)

A radar signal or generated idea is only a lead. Before presenting it as a thesis, run it through serenity-method (Steps 1-5: chokepoint test → first principles → Buffett quality gate (default unverified) → narrative-vs-fundamentals hygiene → classify as 研究地图 vs 可投资结论). Output should show the candidate and its gate result. Never promote a signal to a recommendation.

Output shape

Language / 语言: respond in the user's language — 中文 by default, English on request. Bilingual labels below.

For each surfaced candidate, give:

  1. 信号 · Signal — why it surfaced (heating Δ, new entrant since X, conviction core, theme rotating up).
  2. 她的角度(推测) · Her angle (inferred) — the likely Serenity-style thesis (chokepoint / catalyst / un-priced), clearly marked as inference.
  3. 闸门结果 · Gate result — the serenity-method verdict (almost always 研究地图 / research-map, with the specific things to verify).
  4. 可信度 · Confidence — high/medium/low, with the caveat that drove it down (one-off reply, no fundamentals, single-account risk).

Caveats (read before trusting any output)

  • Candidate generator, not oracle. Attention momentum predicts her interest, not price or correctness.
  • Survivorship bias. Her archive over-weights names that worked; the radar inherits it. Treat "she ramped X and it ran" as not evidence X will repeat.
  • Single-account fragility. One person, one style, one era (a mostly-AI-up-cycle, though it does include the Nov-2025 drawdown where IREN −38% / NBIS −35% — proof she is not infallible and holds through pain).
  • Reply noise. A heating name driven by replies (conversation) ≠ a conviction post. Confirm with the source.
  • No front-running. This surfaces public attention patterns for research; do not use it to trade ahead of or against anyone, and never emit buy/sell calls.

References

NeedRead
Her empirical patterns: theme-rotation logic, selection signature, catalyst playbook, conviction tells, track recordreferences/patterns.md
Exact radar math + how to read each signal + data caveatsreferences/signals.md
The analytical gate every candidate must passthe serenity-method skill
Keeping the archive current / pulling raw poststhe follow-aleabito skill

仅作信息跟踪,不构成投资建议。 / For information tracking only; not investment advice.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Serenity Radar AI skill do?

Use @aleabitoreddit ("Serenity")'s full mention archive (built by the follow-aleabito skill) to anticipate where her attention is moving and generate candidate ideas in her style. Two modes — (1) RADAR reads the live mention data for attention momentum (which tickers she is heating up on, new entrants, conviction core, theme rotation) via scripts/radar.js; (2) GENERATOR applies her empirically-mined patterns (theme-rotation logic, selection signature, catalyst playbook) to propose her likely next focus. Every candidate is gated through the serenity-method checklist. This is a CANDIDATE GENE...

Why use Serenity Radar on TypingMind?

Because you install it once and use it with any model. Serenity Radar is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Serenity Radar in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/lanfuli/aleabito-serenity-skills/tree/main/skills/serenity-radar. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Serenity Radar?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Serenity Radar?

As many as you like. As long as a model supports skills, you can use Serenity Radar with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Serenity Radar AI skill free?

Yes. It is published on GitHub by lanfuli under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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