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claude-skills/docs/commands/cs-pm-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

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title description
/cs-pm-loop — Slash Command for AI Coding Agents Drive a project-delivery goal through a bounded agentic loop — Jira MCP snapshot → flow/sprint analytics bridge → routed sub-skill execution →. Slash command for Claude Code, Codex CLI, Gemini CLI.

/cs-pm-loop

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

Goal:

$ARGUMENTS

Sequence (gates are blocking — never skip forward)

  1. Intake gate — the goal must name an observable outcome and its proof. If vague, run the /cs:grill-pm branches first (one question per turn). Do not loop on fuzz.
  2. Observe — pull fresh data: mcp__atlassian__getAccessibleAtlassianResources (get cloudId) → mcp__atlassian__searchJiraIssuesUsingJql → save snapshot.json, then: 
    python3 project-management/skills/pm-skills/scripts/jira_snapshot_bridge.py --input snapshot.json --to flow
python3 project-management/skills/pm-skills/scripts/jira_snapshot_bridge.py --input snapshot.json --to sprint > sprint_data.json
  3. Plan — write the task plan (owners, executors, reviewers, machine-checkable acceptance per task; shape via delivery_loop_gate.py --sample), then gate it: 
    python3 project-management/skills/pm-skills/scripts/delivery_loop_gate.py --plan plan.json --mode plan
    Exit 2 → fix the listed G1–G4 violations before executing. For multi-task goals, compile through the repo harness instead (goal_compiler.py with the project-management.json manifest) and drive it with loop_controller.py.
  4. Execute — one task at a time: route with pm_goal_router.py, run the routed sub-skill's own tools, record real exit codes and evidence. Retry means a changed approach; max 3 attempts per task.
  5. Verify — the task's acceptance command must exit 0; sub-skill gates apply (scrum-master's ≥3-sprints rule, atlassian-admin's VERIFY steps). Never adjudicate your own verification; never edit a gate to make it pass.
  6. Close — 
    python3 project-management/skills/pm-skills/scripts/delivery_loop_gate.py --plan plan.json --mode close
    Exit 4 → close refused: finish, escalate, or get a human waiver (with reason). Exit 0 → report the handoff: tasks, statuses, evidence, waivers, and the flow-metrics before/after.

Rules

  • Terminal states: success · clean no-op · blocked · approval-required · exhausted · stagnated. Exhausted budgets escalate to the named human — never reported as success.
  • Jira writes are auditable: no transitionJiraIssue to Done without verify evidence; admin/destructive actions are approval-required, full stop.
  • Max 12 loop iterations per goal; 3 attempts per task.