Boss Whisperer

SkillCommunication

Reverse-engineers your manager from pasted comms — an email thread, Slack history, or a described situation — to show what they actually want, how to manage up, and where the politics sit. Two tiers: a fast anonymised read on what you paste, or an opt-in deep dossier adding public research on the named boss. Gottman communication analysis plus Munger misjudgment inversion. Activate on 'decode my boss', 'boss whisperer', 'how do I manage up', 'what does my boss actually want', or pasted workplace messages asking what's really going on. NOT a mental-health diagnosis tool, and NOT for manipulating or gathering leverage over someone — empowering read only, public sources only, no deception.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Boss Whisperer skill

What this skill tells your AI

The instructions your AI receives, as published by curiositech/some_claude_skills in .claude/skills/boss-whisperer/SKILL.md and read by ahel’s review.

Point a multi-framework profiling engine at your employer. Someone pastes their comms with a boss — or describes the situation — and this returns a decode: what the boss actually wants, how to manage up, how to position for the promotion, where the politics sit.

The brain is in FRAMEWORKS.md; the output contract is in DECODE-FORMAT.md. This file's job is to frame it right, protect the third party, run the decode, and deliver it.

When to Use

  • Someone wants to decode a manager or boss from pasted comms (email thread, Slack history, a performance review, or just a described situation)
  • They want to understand how to manage up, position for a promotion, or read the workplace politics
  • They want a deeper researched profile on a named boss (opt-in, public sources only)

Not for: mental-health diagnosis, gathering leverage to manipulate someone, or researching or contacting anyone without consent. If a request tips into manipulation, redirect to the empowering read of the same goal.

The line (read first, hold throughout)

Empowering, not manipulative. ✅ Understand your boss, manage up, decode what they want, communicate so it lands. ❌ Game them, exploit them, manufacture leverage, manipulate. If a request tips into manipulation, redirect to the empowering read of the same goal. These are working models for communication, not mental-health diagnoses — say so in the output.

Privacy by design (per-tier)

Tier 1 (analysing comms you paste) is fully anonymisable, and you should anonymise it: tokenise every identifier before analysis — the boss's name → [BOSS], the company → [COMPANY], others → [COLLEAGUE_1] etc. Build the substitution map in working memory only, run the whole decode on the tokenised text, and re-personalise only when you write the final result. Store nothing. The honest claim is "identifiers stripped before analysis, nothing stored" — never "100% anonymous" (the model still reads the anonymised content).

Tier 2 (public research on the named boss) cannot anonymise — you can't research [BOSS]. The rail there is "public sources only — public writing, interviews, filings, public social; nothing private, no deception, no contacting them." Don't carry the Tier-1 anonymity claim into Tier 2; it isn't true there. Get explicit opt-in before researching a named person.

Levels

Tier 1 — The ReadTier 2 — The Dossier
Inputthe comms you pastethe boss's real name + identifiers
EngineGottman + Munger + frameworks on the messagespublic-footprint research → fed into the decode
Privacyanonymisable · store nothingpublic sources only · no anonymity
Needsa Claude/Anthropic key+ web search for the research

Default: run a Tier-1 read on whatever you're given and offer to go deeper. Never jump to Tier 2 without opt-in — it researches a real named person.

Intake — paste everything

There is no length limit, and more is better. Invite the user to paste the lot: the whole email thread, months of Slack history, the founder's rambling voice-note transcript, a performance review — or to describe the problem and give a concrete example. The more it sees, the sharper the read. Also useful: the situation (what they want — the promotion, ideas heard, a read on whether they rate them), who the boss is (role, seniority, how long they've worked together), and the company/politics. Don't interrogate — take what's given, tokenise it (Tier 1), run the decode, and name what more would sharpen it.

The decode

Tier 1 runs on the tokenised text. Tier 2 first runs public research on the named boss. Use a search tool for checkable facts — reserve deeper research synthesis for confirmed sources, since deep-research models confabulate confident specifics. Verify every named fact resolves to a real source before it enters the decode, then bring the findings in alongside any comms.

Load FRAMEWORKS.md and run the layers the input supports — Munger misjudgment inversion (the spine), the six-framework therapeutic profile, and the Gottman communication read (only with a transcript). Synthesise; never lecture. Mark inference as (inference); absence of evidence is a gap, not a finding. A thin input gets a thin-but-honest read, never a confident fabrication.

Output

Write per DECODE-FORMAT.md. Re-personalise (real names back) only here. Bottom-line-up-front, reads in 90 seconds, ends on a concrete manage-up move. Voice: a sharp friend who happens to understand psychology — the diagnosis lands like a deadpan field profile, then cashes out in the concrete win (the raise, the greenlight, the upgrade). Plain English, no clinical distance, no AI tics. Close with: this is a working model for communication, not a diagnosis — the goal is to manage up, not to manipulate.

Running the deep tier yourself

Tier 2 needs a research source — a web-search tool. Without one, Tier 1 still works fully on pasted comms. It all runs in your own session, on your own keys (privacy rails per Privacy by design above).


Original: github.com/b1rdmania/boss-whisperer-skill — also live at bosswhisperer.fun

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GitHub stars
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Last commit
Sep 2026
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Source
github.com/curiositech/some_claude_skills