scriptoriumadmin/claude-skills
1
0
Fork 0
mirror of https://github.com/alirezarezvani/claude-skills.git synced 2026-08-28 04:24:58 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions
claude-skills/docs/commands/cs-product-loop.md
Claude abd9c9d8de
docs(site): generate agent-launcher pages (18th domain) + nav 
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
2026-08-24 17:26:12 +00:00

2.7 KiB
Raw Permalink Blame History

title description
/cs-product-loop — Slash Command for AI Coding Agents 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.

/cs-product-loop

:material-console: Slash Command :material-github: Source

Inputs (defaults: discovery_log.json and ost.json in the workspace; shapes in product-team/skills/product-skills/assets/):

$ARGUMENTS

Sequence (one iteration per invocation)

  1. Observe — 
    python3 product-team/skills/product-skills/scripts/discovery_cadence_tracker.py --input discovery_log.json
    Exit 5 (< 2 interviews): there is no cadence to measure — help the user book the first two weekly touchpoints and write the outcome statement; stop there.
  2. Choose — the report's next_loop_action is the choice. Typical actions: book the missing weekly touchpoint · re-anchor the interview guide on the outcome · test the top untested assumption (route to product-discovery's assumption_mapper to rank).
  3. Act — execute with the routed sub-skill's tools (ux-researcher-designer for the interview, experiment-designer for the test design). One bounded action per iteration.
  4. Verify — 
    python3 product-team/skills/product-skills/scripts/ost_linter.py --input ost.json
    Exit 2 → fix the listed O1–O5 violations before the tree may drive any roadmap or experiment. Then re-run the cadence tracker and confirm the health score did not drop.
  5. Record — update discovery_log.json (interview/test entries) and ost.json; note the health score in the digest so the trend is visible across iterations.
  6. Repeat or stop — terminal states:
    • Graduate: HEALTHY + a validated assumption → hand off to experiment-designer (A/B gate) or product-manager-toolkit (PRD with eval spec if the feature is AI-powered).
    • Escalate: DORMANT 4+ weeks → name the product lead and say the habit is dead — never let discovery die silently.
    • Clean no-op: cadence HEALTHY, no gaps — book next week's touchpoint and exit.

Rules

  • Never modify the linter or tracker to make a gate pass.
  • Insights require recurrence across independent participants — singletons stay anecdotes.
  • The loop edits the log and the tree, never the gates (locked-evaluator invariant).