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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
53 lines
2.1 KiB
Markdown
53 lines
2.1 KiB
Markdown
---
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title: "/cs-product — Slash Command for AI Coding Agents"
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description: "Top-level product-team router. Classifies a product inquiry across 16 lanes (prioritization, OKRs, UX, design system, competitive, analytics. Slash command for Claude Code, Codex CLI, Gemini CLI."
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---
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# /cs-product
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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/product-team/commands/cs-product.md">Source</a></span>
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</div>
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Route this inquiry through the `product-skills` orchestrator:
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**$ARGUMENTS**
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## Routing (deterministic — run the script, don't eyeball)
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```bash
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python3 product-team/skills/product-skills/scripts/product_goal_router.py --text "$ARGUMENTS" --output json
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```
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- Exit 0 → load `skill_path`/SKILL.md (covers the 4 standalone plugins too) and follow
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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 — an `ost.json`, `discovery_log.json`, or `features.csv`
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resolves 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
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- Top 3 findings, each anchored to a canon citation
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- Top 3 next actions with a named owner
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- Artifact path
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- One grill challenge (e.g. "This roadmap cites an OST that fails the linter — which
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opportunity backs item 3?")
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## Hard rules
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- Insights carry participant counts; singletons are anecdotes.
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- Experiments carry computed sample size + MDE, never gut feel.
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- Prioritization names its framework (RICE / WSJF / opportunity score) and why.
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- AI features get an eval spec (golden set + rubric + guardrails) in the PRD.
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- Recurring discovery work goes to `/cs:product-loop` instead.
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## Distinct from
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- `project-management` — how to deliver. This domain is what to build.
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- `marketing/landing` — from-scratch marketing pages; `landing-page-generator` here
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scaffolds product Next.js/TSX pages.
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