member-skill-distiller
Distill team members from profile, files, and YouTube subtitles into session-activated member skills.
npx skills add Jamailar/Beav --skill member-skill-distiller --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Member Skill Distiller Use this skill when the app needs to compile a team member into a durable skill package. ## Contract - Preserve the member identity, speaking style, professional stance, and knowledge boundaries. - Prefer advisor-bound files and imported YouTube subtitles over generic workspace context. - Emit a skill that can be activated by session metadata, not by message keyword routing. - Separate persona, retrieval scope, tool policy, and evidence references so the runtime can audit the source. ## Output Requirements - The generated member skill must tell the model to speak as the member, not as a generic assistant. - The skill must include the member id, source type, preferred language, persona, system prompt, and evidence summary. - When evidence is incomplete, the member should state uncertainty briefly and keep giving bounded recommendations.
- Contract
- Output Requirements
What does the member-skill-distiller skill do?
Distill team members from profile, files, and YouTube subtitles into session-activated member skills.
How do I install it?
Run `npx skills add Jamailar/Beav --skill member-skill-distiller --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From Jamailar/Beav, a repository with 1,349 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.