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generate-docs.py learns the agent-launcher domain (5 hardcoded maps extended); regenerated docs tree: 343 skill pages / 96 agent pages / 122 command pages (561 total). mkdocs.yml nav gains the Agent Launcher skill section (7 pages), 4 cs-agent-* agent entries, and 8 /cs:* command entries; all nav targets verified to exist. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_012FwXG6TqCXKZQvF4iD69cv
51 lines
2.1 KiB
Markdown
51 lines
2.1 KiB
Markdown
---
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title: "/cs-pm — Slash Command for AI Coding Agents"
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description: "Top-level project-management router. Classifies a PM inquiry across 8 lanes (sprint/flow, portfolio health, Jira, Confluence, admin, templates. Slash command for Claude Code, Codex CLI, Gemini CLI."
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---
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# /cs-pm
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<div class="page-meta" markdown>
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<span class="meta-badge">:material-console: Slash Command</span>
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<span class="meta-badge">:material-github: <a href="https://github.com/alirezarezvani/2-claude-skills/tree/main/project-management/commands/cs-pm.md">Source</a></span>
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</div>
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Route this inquiry through the `pm-skills` orchestrator:
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**$ARGUMENTS**
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## Routing (deterministic — run the script, don't eyeball)
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```bash
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python3 project-management/skills/pm-skills/scripts/pm_goal_router.py --text "$ARGUMENTS" --output json
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```
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- Exit 0 → load `skill_path`/SKILL.md and follow that skill's own workflow in a fork.
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- Exit 2 → ask ONE clarifying question naming the listed candidates, recommended answer
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first.
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- Exit 3 → ask the user to restate the goal with the deliverable named. Never guess.
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- Explore the workspace first — a saved Jira snapshot, retro log, or transcript resolves
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the lane silently. Never silently chain a second sub-skill.
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## Output (≤200-word digest)
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- What was analyzed (with the data source — snapshot file, not memory)
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- Top 3 findings, each anchored to a canon citation
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- Top 3 next actions with a named human owner
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- Artifact path
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- One grill challenge (e.g. "Your health report is self-reported RAG — where's the
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derived diff that catches watermelons?")
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## Hard rules
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- Flow numbers come from `jira_snapshot_bridge.py` on real snapshot data.
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- Forecasts are Monte Carlo percentile ranges, never single dates.
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- Live Jira/Confluence ops use only the tools in
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`project-management/references/atlassian-mcp-tools.md` — never invent tool names.
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- Goals (not questions) go to `/cs:pm-loop` instead.
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## Distinct from
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- `product-team` — what to build. This domain is how to deliver it.
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- `/cs:harness` — the generic loop engine; `/cs:pm-loop` is its PM-domain adapter.
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