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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
56 lines
2.9 KiB
Markdown
56 lines
2.9 KiB
Markdown
---
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title: "/cs-pm-loop — Slash Command for AI Coding Agents"
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description: "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."
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---
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# /cs-pm-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/project-management/commands/cs-pm-loop.md">Source</a></span>
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</div>
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Goal:
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**$ARGUMENTS**
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## Sequence (gates are blocking — never skip forward)
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1. **Intake gate** — the goal must name an observable outcome and its proof. If vague,
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run the `/cs:grill-pm` branches first (one question per turn). Do not loop on fuzz.
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2. **Observe** — pull fresh data: `mcp__atlassian__getAccessibleAtlassianResources` (get
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cloudId) → `mcp__atlassian__searchJiraIssuesUsingJql` → save `snapshot.json`, then:
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```bash
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python3 project-management/skills/pm-skills/scripts/jira_snapshot_bridge.py --input snapshot.json --to flow
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python3 project-management/skills/pm-skills/scripts/jira_snapshot_bridge.py --input snapshot.json --to sprint > sprint_data.json
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```
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3. **Plan** — write the task plan (owners, executors, reviewers, machine-checkable
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acceptance per task; shape via `delivery_loop_gate.py --sample`), then gate it:
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```bash
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python3 project-management/skills/pm-skills/scripts/delivery_loop_gate.py --plan plan.json --mode plan
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```
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Exit 2 → fix the listed G1–G4 violations before executing. For multi-task goals,
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compile through the repo harness instead (`goal_compiler.py` with the
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`project-management.json` manifest) and drive it with `loop_controller.py`.
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4. **Execute** — one task at a time: route with `pm_goal_router.py`, run the routed
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sub-skill's own tools, record real exit codes and evidence. Retry means a changed
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approach; max 3 attempts per task.
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5. **Verify** — the task's acceptance command must exit 0; sub-skill gates apply
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(scrum-master's ≥3-sprints rule, atlassian-admin's VERIFY steps). Never adjudicate
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your own verification; never edit a gate to make it pass.
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6. **Close** —
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```bash
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python3 project-management/skills/pm-skills/scripts/delivery_loop_gate.py --plan plan.json --mode close
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```
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Exit 4 → close refused: finish, escalate, or get a human waiver (with reason). Exit 0
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→ report the handoff: tasks, statuses, evidence, waivers, and the flow-metrics
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before/after.
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## Rules
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- Terminal states: success · clean no-op · blocked · approval-required · exhausted ·
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stagnated. Exhausted budgets escalate to the named human — never reported as success.
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- Jira writes are auditable: no `transitionJiraIssue` to Done without verify evidence;
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admin/destructive actions are approval-required, full stop.
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- Max 12 loop iterations per goal; 3 attempts per task.
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