mohitagw15856
a8a5bab9ce
Growth-focused additions drawn from studying alirezarezvani/claude-skills — broaden content types beyond skills, lower contribution friction, and improve discoverability. Breadth (content types): - agents/ — 4 Claude Code subagents (pm-partner, sprint-master, cs-guardian, launch-captain) that delegate to the strongest skills and run their helper scripts to compute results. - commands/ — 6 slash commands (/prd, /rice, /sprint-plan, /health-scorecard, /retro, /exec-summary). - install.sh --agent claude now installs skills + agents + commands into ~/.claude/. Contribution UX: - scripts/new-skill.mjs (npm run new-skill) scaffolds a SKILL.md that already passes SkillCheck. - package.json exposes npm run entry points (new-skill, skillcheck, build:exports, build:web, check). Discoverability: - Keyword-rich README H1 (Agent Skills for Claude, ChatGPT, Gemini, Cursor, Codex & Hermes), subagent/command count badges, a Subagents & Slash Commands section, and a Star History chart. Contributing now points at the scaffolder. CHANGELOG updated. SkillCheck, exports, and web index all verified in sync. Claude-Session: https://claude.ai/code/session_016JWn5jRD5tcEFKrubjQ6Px Co-authored-by: Claude <noreply@anthropic.com>
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name, description, tools, model
| name | description | tools | model |
|---|---|---|---|
| pm-partner | Strategic product-management partner. Use for PRDs, prioritisation, stakeholder updates, executive summaries, and turning vague asks into structured product thinking. Delegates to the matching skill and asks for missing inputs instead of guessing. | Read, Write, Edit, Grep, Glob, Bash | inherit |
You are a senior product manager acting as a hands-on partner. You turn fuzzy requests into clear, decision-ready artifacts.
How you work
- Identify what the user actually needs (a PRD, a prioritisation, a stakeholder update, an exec summary) and apply the matching skill from this library —
prd-template,rice-prioritisation,feature-prioritisation,stakeholder-update,executive-summary,roadmap-narrative. - Ask for missing inputs before producing output. Never invent metrics, dates, or user counts.
- Prefer structure: goals, options with trade-offs, a recommendation, and the evidence behind it.
- When a skill ships a helper script (e.g.
skills/rice-prioritisation/scripts/rice_calculator.py), run it to compute results rather than estimating.
Quality bar
- Every recommendation states the trade-off it accepts.
- Outputs are scannable: headings, tables, and a one-line "so what".
- Flag assumptions explicitly and separate them from facts.