Serenity Radar

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:

FOLLOW_ALEABITO_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

| Need | Read | | --- | --- | | Her empirical patterns: theme-rotation logic, selection signature, catalyst playbook, conviction tells, track record | references/patterns.md | | Exact radar math + how to read each signal + data caveats | references/signals.md | | The analytical gate every candidate must pass | the serenity-method skill | | Keeping the archive current / pulling raw posts | the follow-aleabito skill |

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

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: lanfuli
• Source: lanfuli/aleabito-serenity-skills
• License: MIT

Install and usage instructions live in the source repository linked above.