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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
55 lines
2.7 KiB
Markdown
55 lines
2.7 KiB
Markdown
---
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title: "/cs-product-loop — Slash Command for AI Coding Agents"
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description: "Run the continuous-discovery loop — score the weekly cadence (Torres), act on the named gap, lint the Opportunity Solution Tree as the machine gate. Slash command for Claude Code, Codex CLI, Gemini CLI."
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---
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# /cs-product-loop
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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-loop.md">Source</a></span>
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</div>
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Inputs (defaults: `discovery_log.json` and `ost.json` in the workspace; shapes in
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`product-team/skills/product-skills/assets/`):
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**$ARGUMENTS**
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## Sequence (one iteration per invocation)
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1. **Observe** —
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```bash
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python3 product-team/skills/product-skills/scripts/discovery_cadence_tracker.py --input discovery_log.json
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```
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Exit 5 (< 2 interviews): there is no cadence to measure — help the user book the
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first two weekly touchpoints and write the outcome statement; stop there.
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2. **Choose** — the report's `next_loop_action` is the choice. Typical actions: book the
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missing weekly touchpoint · re-anchor the interview guide on the outcome · test the
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top untested assumption (route to `product-discovery`'s assumption_mapper to rank).
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3. **Act** — execute with the routed sub-skill's tools (ux-researcher-designer for the
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interview, experiment-designer for the test design). One bounded action per
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iteration.
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4. **Verify** —
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```bash
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python3 product-team/skills/product-skills/scripts/ost_linter.py --input ost.json
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```
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Exit 2 → fix the listed O1–O5 violations before the tree may drive any roadmap or
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experiment. Then re-run the cadence tracker and confirm the health score did not
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drop.
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5. **Record** — update `discovery_log.json` (interview/test entries) and `ost.json`;
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note the health score in the digest so the trend is visible across iterations.
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6. **Repeat or stop** — terminal states:
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- **Graduate**: HEALTHY + a validated assumption → hand off to `experiment-designer`
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(A/B gate) or `product-manager-toolkit` (PRD with eval spec if the feature is
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AI-powered).
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- **Escalate**: DORMANT 4+ weeks → name the product lead and say the habit is dead —
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never let discovery die silently.
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- **Clean no-op**: cadence HEALTHY, no gaps — book next week's touchpoint and exit.
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## Rules
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- Never modify the linter or tracker to make a gate pass.
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- Insights require recurrence across independent participants — singletons stay
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anecdotes.
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- The loop edits the log and the tree, never the gates (locked-evaluator invariant).
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