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Deep audit of both domains against the AR v1 agentic-readiness rubric (audit/pm-product-agentic-2026-07/: master + per-domain reports + research-backed improvement fields + research digest), plus the harness layer the audit motivated: - pm-skills rebuilt as a context:fork orchestrator with an agentic delivery loop: pm_goal_router.py (8 lanes, exit-code route/ask/refuse), jira_snapshot_bridge.py (searchJiraIssuesUsingJql output -> Kanban Guide 2025 flow metrics with SLE + aging-WIP alerts + seeded Monte Carlo forecasts, or scrum-master sprint schema — verified end-to-end into velocity_analyzer.py), delivery_loop_gate.py (G1-G6 delegation governance: human owner, reviewer for agent tasks, machine-checkable acceptance, evidence-before-done, close refusal, exhausted-budget-is-escalation). - product-skills rebuilt as a context:fork orchestrator with the continuous-discovery loop: product_goal_router.py (16 lanes incl. standalone plugins), discovery_cadence_tracker.py (Torres weekly-habit health 0-100 with named gaps), ost_linter.py (O1-O5 Opportunity Solution Tree structural gates). - 6 new references citing 6-7 sources each (flow/forecasting canon, agentic delivery governance, PM loop playbook, continuous discovery, product operating model, AI product evals); pinned fixtures (expected_flow_metrics.json, sample OST/log). - cs-pm-orchestrator + cs-product-orchestrator agents; /cs:pm, /cs:grill-pm, /cs:pm-loop, /cs:product, /cs:grill-product, /cs:product-loop commands. - Fixed the two CLI-noncompliant product tools (user_story_generator.py, persona_generator.py): real argparse --help, seeded determinism, backward-compatible positionals. - Regenerated agent-harness manifests for both domains (orchestrators now score all five agentic_signals); updated domain CLAUDE.mds, plugin manifests (2.10.4), marketplace entries, and headline counters (602 tools / 731 references / 99 agents / 109 commands; derive_counters --check passes). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_019Uzm8dKoeXPayJVMojpSbw
50 lines
2.8 KiB
Markdown
50 lines
2.8 KiB
Markdown
---
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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 → machine-verified gates → close refused until everything is verified or human-waived. The PM-domain adapter over engineering/agent-harness.
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argument-hint: "<delivery goal, e.g. 'get sprint 14 to a verified close with health >= 70'>"
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---
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# /cs:pm-loop — run a delivery goal to a verified close
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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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