boss skills

name: create-boss
description: Distill a real boss into an AI skill, or generate a boss skill from a famous entrepreneur archetype such as Elon Musk, Steve Jobs, Jeff Bezos, or Jensen Huang, or build a persona from free public sources (papers, GitHub, Wikipedia) for a mentor or PI. Use when the user wants boss analysis, managing-up guidance, persona extraction, decision-model distillation, or entrepreneur-style boss presets.
argument-hint: "[boss-name-or-archetype]"
version: "1.2.0"
user-invocable: true
allowed-tools: Read, Write, Edit, Bash

Create Boss

Use this skill in three modes:

1. real boss mode Turn real chat logs, meeting notes, emails, comments, and project artifacts into a boss skill. 2. archetype mode Generate a boss skill inspired by a public entrepreneur operating style. 3. public research mode Build a persona for a mentor, PI, or boss from free public sources (OpenAlex, Semantic Scholar, arXiv, Crossref, GitHub, Wikipedia, web search).

Trigger phrases

• /create-boss
• /list-bosses
• /boss-rollback
• /delete-boss
• "create a boss skill"
• "analyze my boss"
• "build a Musk-style boss"
• "make a Steve Jobs style leader"
• "give me a Bezos-style management model"
• "list boss archetypes"
• "research my advisor / mentor / PI"
• "建一个我导师的画像"
• "run replay eval" / "测一下这个老板像不像"

Tools

• Parse imported material with the files in [tools/](tools).
• Write or update generated boss skills with [tools/skill_writer.py](tools/skill_writer.py).
• Search free public sources about a person with [tools/person_research.py](tools/person_research.py).
• Evaluate persona fidelity with [tools/replay_eval.py](tools/replay_eval.py).
• Read template prompts from [prompts/](prompts) when working from real source material.
• Read bundled entrepreneur templates from [archetypes/](archetypes) when working in archetype mode.

These scripts are internal implementation details for the agent. Do not ask the user to run Python commands manually unless they explicitly want a developer workflow.

Workflow

Mode 1: Real Boss

1. Ask for the boss name, baseline profile, and initial management impression. 2. Ask for source material: chats, meeting notes, docs, email, or pasted text. 3. Extract structured decision cases first, following [prompts/decision_extractor.md](prompts/decision_extractor.md). Each case goes into bosses/{slug}/cases/ via skill_writer.py --action add-case. 4. Distill three narrative outputs:

5. Build the decision layer from the cases:

[prompts/decision_model_builder.md](prompts/decision_model_builder.md) (write with --action set-rubric / --action set-rules)

[prompts/playbook_builder.md](prompts/playbook_builder.md) (write with --action add-playbook) 6. Run the writer script yourself to write the boss bundle into bosses/{slug}/. 7. If there are 3+ cases, offer to run a decision replay eval, following [prompts/replay_evaluator.md](prompts/replay_evaluator.md). 8. Show the generated commands:

• judgment.md
• management.md
• persona.md
• rubric.json and decision_rules.md, following
• scene playbooks, following
• /{slug}
• /{slug}-judgment
• /{slug}-management
• /{slug}-persona

Mode 2: Entrepreneur Archetype

1. If the user asks for an entrepreneur-style boss, infer the best matching archetype or offer a short list:

2. Run the writer script yourself to generate the skill. Do not expose the internal command as the primary UX. 3. Tell the user the generated trigger command, for example:

4. If the user asks to browse or inspect templates, summarize the available archetypes in natural language instead of telling them to run a script.

• elon-musk
• steve-jobs
• jeff-bezos
• jensen-huang
• /elon-musk
• /steve-jobs

Mode 3: Public Research (mentor / PI / public boss)

Follow [prompts/person_researcher.md](prompts/person_researcher.md):

1. Collect the person's real name, affiliation, and field from the user. 2. Run tools/person_research.py --name "..." --affiliation "..." --sources all --save-dir bosses/{slug}/knowledge/research. All sources are free and need no API key. 3. Disambiguate candidates with the affiliation hint; if still ambiguous, ask the user to pick. Never guess. 4. Supplement with your own web search for interviews, talks, and blog posts. 5. Distill the persona with evidence levels: private > public-quote > public-inferred. Every public-sourced claim must carry its source. 6. This mode can be combined with Mode 1: real private material always outranks public inference.

Management Commands

When the user asks for boss management operations, handle them internally with the bundled scripts:

Run tools/skill_writer.py --action list and summarize the available boss skills.

Confirm the target slug and version, then run tools/version_manager.py --action rollback.

Confirm before deletion, then run tools/skill_writer.py --action delete --slug {slug}.

Roleplay the boss across multiple turns using the matching playbook's expected reactions and failure branches. End with a debrief against rubric.json.

Run the decision replay eval per prompts/replay_evaluator.md and report the fidelity score.

• /list-bosses
• /boss-rollback {slug} {version}
• /delete-boss {slug}
• /{slug}-drill {scene}
• /boss-eval {slug}

Do not tell normal users to copy these commands manually. Execute the workflow yourself and report the result.

Bundled Archetypes

• elon-musk: first-principles, speed, technical pressure
• steve-jobs: taste, simplicity, product clarity
• jeff-bezos: mechanism design, customer obsession, written thinking
• jensen-huang: platform strategy, technical depth, constructive intensity

Files Created

Every generated boss skill should include:

• SKILL.md
• judgment.md
• management.md
• persona.md
• meta.json
• judgment_skill.md
• management_skill.md
• persona_skill.md

When source material contains real decisions, also create the decision layer:

• cases/*.json — structured decision events with original quotes and sources
• rubric.json — the boss's review checklist (blocker / major / minor items)
• decision_rules.md — IF/THEN decision rules with case evidence
• playbooks/*.md — scene workflows (bad news, resource request, pitch, ...)
• eval/ — replay eval artifacts (question pack, answer key, fidelity report)

Corrections

When the user corrects the model ("he wouldn't say that", "he cares about X more"):

1. Locate the affected rubric item, rule, case, or persona section. 2. Mark the old conclusion as overruled instead of deleting it, then add the corrected rule with evidence user-correction-{date}. 3. Follow [prompts/correction_handler.md](prompts/correction_handler.md). 4. After corrections accumulate, re-run the replay eval to confirm fidelity did not regress.

Safety Framing

logins or paywalls, and present results as a public-style portrait with sources.

• Treat entrepreneur presets as public-style archetypes, not claims of exact private impersonation.
• Prefer management patterns, decision rules, and communication norms over catchphrases.
• If the user asks for a hybrid with a real boss, keep real evidence higher priority than the archetype.
• In public research mode, use only freely accessible public data, never bypass
• Redact unrelated third-party names from extracted cases.