| 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:
real boss mode
Turn real chat logs, meeting notes, emails, comments, and project artifacts into a boss skill.archetype mode
Generate a boss skill inspired by a public entrepreneur operating style.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
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
- Ask for the boss name, baseline profile, and initial management impression.
- Ask for source material: chats, meeting notes, docs, email, or pasted text.
- Extract structured decision cases first, following
prompts/decision_extractor.md.
Each case goes into bosses/{slug}/cases/ via
skill_writer.py --action add-case.
- Distill three narrative outputs:
judgment.mdmanagement.mdpersona.md
- Build the decision layer from the cases:
- Run the writer script yourself to write the boss bundle into
bosses/{slug}/.
- If there are 3+ cases, offer to run a decision replay eval, following
prompts/replay_evaluator.md.
- Show the generated commands:
/{slug}/{slug}-judgment/{slug}-management/{slug}-persona
Mode 2: Entrepreneur Archetype
- If the user asks for an entrepreneur-style boss, infer the best matching archetype or offer a short list:
elon-musksteve-jobsjeff-bezosjensen-huang
- Run the writer script yourself to generate the skill. Do not expose the internal command as the primary UX.
- Tell the user the generated trigger command, for example:
- If the user asks to browse or inspect templates, summarize the available archetypes in natural language instead of telling them to run a script.
Mode 3: Public Research (mentor / PI / public boss)
Follow prompts/person_researcher.md:
- Collect the person's real name, affiliation, and field from the user.
- Run
tools/person_research.py --name "..." --affiliation "..." --sources all --save-dir bosses/{slug}/knowledge/research.
All sources are free and need no API key.
- Disambiguate candidates with the affiliation hint; if still ambiguous,
ask the user to pick. Never guess.
- Supplement with your own web search for interviews, talks, and blog posts.
- Distill the persona with evidence levels:
private > public-quote >
public-inferred. Every public-sourced claim must carry its source.
- 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:
/list-bosses
Run tools/skill_writer.py --action list and summarize the available boss skills./boss-rollback {slug} {version}
Confirm the target slug and version, then run tools/version_manager.py --action rollback./delete-boss {slug}
Confirm before deletion, then run tools/skill_writer.py --action delete --slug {slug}./{slug}-drill {scene}
Roleplay the boss across multiple turns using the matching playbook's
expected reactions and failure branches. End with a debrief against rubric.json./boss-eval {slug}
Run the decision replay eval per prompts/replay_evaluator.md and report
the fidelity score.
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 pressuresteve-jobs: taste, simplicity, product clarityjeff-bezos: mechanism design, customer obsession, written thinkingjensen-huang: platform strategy, technical depth, constructive intensity
Files Created
Every generated boss skill should include:
SKILL.mdjudgment.mdmanagement.mdpersona.mdmeta.jsonjudgment_skill.mdmanagement_skill.mdpersona_skill.md
When source material contains real decisions, also create the decision layer:
cases/*.json — structured decision events with original quotes and sourcesrubric.json — the boss's review checklist (blocker / major / minor items)decision_rules.md — IF/THEN decision rules with case evidenceplaybooks/*.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"):
- Locate the affected rubric item, rule, case, or persona section.
- Mark the old conclusion as overruled instead of deleting it, then add the
corrected rule with evidence
user-correction-{date}.
- Follow
prompts/correction_handler.md.
- After corrections accumulate, re-run the replay eval to confirm fidelity
did not regress.
Safety Framing
- 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
logins or paywalls, and present results as a public-style portrait with sources.
- Redact unrelated third-party names from extracted cases.
