Project Management — Domain Orchestrator & Delivery Loop

This orchestrator does two jobs. Routing: fork context, classify a PM inquiry with scripts/pm_goal_router.py, run exactly one of the 8 sub-skills, return a digest. Looping: turn a delivery goal into a bounded agentic loop — pull live Jira data via the bundled Atlassian MCP, bridge it into the domain's deterministic analytics tools, verify every step with machine-run gates, and refuse to close until everything is verified or a human waives it. The bundled .mcp.json wires the Atlassian Remote MCP (https://mcp.atlassian.com/v1/sse, OAuth handled by Claude Code).

When to invoke

| Symptom | Sub-skill | |---|---| | "Project/portfolio health, risk EMV, capacity" | senior-pm | | "Sprint velocity, retro follow-through, ceremony health, when-will-it-be-done" | scrum-master | | "JQL, Jira workflows, boards, automation" | jira-expert | | "Confluence spaces, page trees, content audits" | confluence-expert | | "Users, groups, permissions, SSO" | atlassian-admin | | "Reusable Jira/Confluence templates" | atlassian-templates | | "Meeting transcripts, talk time, action items" | meeting-analyzer | | "Status updates, 3P updates, stakeholder comms" | team-communications |

Routing logic (deterministic)

Run the router — do not eyeball the table when a script can decide:

bash
python3 scripts/pm_goal_router.py --text "<the goal>" --output json

Exit 0 → route_to names the sub-skill: load its SKILL.md and follow its workflow. Exit 2 → ask ONE clarifying question naming the listed candidates, with a recommended answer. Exit 3 → no signal: ask the user to restate the goal with the deliverable named. Never guess silently; never silently chain a second sub-skill — digest first, confirm, then chain.

The delivery loop (agentic)

For goals (not questions) — "get sprint 14 to a verified close", "produce a portfolio health report from live Jira", "make our flow metrics visible weekly" — run the loop-library contract (Observe → Choose → Act → Verify → Record → Repeat-or-stop):

1. Observe — pull fresh state: mcp__atlassian__searchJiraIssuesUsingJql (get

cloudId via getAccessibleAtlassianResources first), save the result JSON, then bridge it: bash python3 scripts/jira_snapshot_bridge.py --input snapshot.json --to flow # WIP, throughput, cycle time p50/85/95, work-item age, SLE, aging alerts python3 scripts/jira_snapshot_bridge.py --input snapshot.json --to sprint > s.json # scrum-master schema python3 ../scrum-master/scripts/velocity_analyzer.py s.json # velocity + volatility + forecast Add --forecast N for a seeded Monte Carlo "when will N items be done" answer (refuses on < 10 completed items — thin history forecasts are lies).

1. Choose — route the next task with pm_goal_router.py; one task at a time.
2. Act — execute with the routed sub-skill's own tools per its SKILL.md.
3. Verify — gate the plan and every close with:

bash python3 scripts/delivery_loop_gate.py --plan plan.json --mode plan # exit 2 = blocked python3 scripts/delivery_loop_gate.py --plan plan.json --mode close # exit 4 = close refused Plus each sub-skill's own gates (scrum-master's ≥ 3-sprints rule, atlassian-admin's VERIFY steps). Never adjudicate your own verification.

1. Record / Repeat-or-stop — for multi-task goals, run the state through the repo-wide

harness (it enforces attempt caps, iteration budgets, and evidence logging): bash python3 engineering/agent-harness/skills/agent-harness/scripts/goal_compiler.py \ --goal "<goal>" --manifest engineering/agent-harness/skills/agent-harness/assets/harnesses/project-management.json \ --out .agent-harness/plan.json python3 engineering/agent-harness/skills/agent-harness/scripts/loop_controller.py init|next|record|verify|close ... Terminal states: success, clean no-op, blocked, approval-required, exhausted, stagnated. An exhausted budget is an escalation — never a success report.

Hard rules (agentic delegation governance)

1. Agents are contributors, never owners (Linear model): every loop task carries a

named human owner; agent-executed tasks also carry a named human reviewer. delivery_loop_gate.py enforces this (G1/G2).

1. Acceptance must be machine-checkable — a command, or a criterion with a threshold.

"Looks good" is not a gate (G3).

1. Every Jira/Confluence write is auditable and reversible-first (Rovo discipline):

never transitionJiraIssue to Done without verify evidence; destructive/irreversible actions (deletes, permission changes, org-wide admin) are approval-required terminal states, not loop steps.

1. Never modify a gate you are judged by — same locked-evaluator invariant as

autoresearch-agent.

1. Forecasts are ranges with confidence, never dates — Monte Carlo percentiles

(p50/p70/p85/p95), per Vacanti. Single-date promises are the anti-pattern.

1. Max 3 attempts per task, 12 loop iterations per goal — then escalate to the named

human with the evidence log.

Forcing-question library (grill-with-docs pattern)

One per turn, recommended answer, canon citation. Never run a sub-skill or start a loop until the lane-defining decision is locked:

• SPRINT lane: "Do you want to measure flow (cycle time, WIP, throughput, age) or

forecast delivery? Recommended: measure first — a forecast off unmeasured flow is noise. Canon: Kanban Guide (May 2025) four mandatory flow measures; Vacanti, Actionable Agile Metrics."

• HEALTH lane: "Is your project status self-reported RAG or derived from signals?

Recommended: derive it (schedule variance, aging WIP, scope churn) and diff against the self-report — that diff finds watermelon projects. Canon: Kanban Guide 2025; DORA 2025 (AI amplifies, doesn't fix, weak signals)."

• JIRA lane: "Is this configuration change deployable to a test project first?

Recommended: always stage in a test project; jira-expert's workflow validator must exit 0 before production. Canon: jira-expert validation workflow."

• ADMIN lane: "Is this action reversible, and who approves it? Recommended: name the

approver before touching permissions — admin actions are approval-required terminal states in any loop. Canon: atlassian-admin VERIFY discipline; loop-library stop states."

• LOOP intake: "What single observable outcome means DONE, and which command proves

it? Recommended: a named artifact + a command that exits 0 against it. Canon: agent-harness verifier's law; Anthropic, Building Effective Agents (evaluator needs clear criteria)."

• MEETINGS/COMMS lanes: "Could this meeting be an async written update? Recommended:

status-broadcast meetings convert to async 3P updates; decision meetings keep sync. Canon: GitLab async-first handbook."

Assumptions

1. The user has (or is preparing analysis for someone with) delivery authority.
2. Jira/Confluence access goes through the bundled MCP; capabilities NOT in

project-management/references/atlassian-mcp-tools.md (project/sprint/board/space creation, admin config) are done in the web UI — never invent tool names.

1. Inputs may be partial — every tool ships --sample so the shape is visible first.

Non-goals

• Not a replacement for the sub-skills — the orchestrator routes and loops; the

sub-skills do the work.

• Not the generic loop engine — that is engineering/agent-harness; this orchestrator is

the PM-domain adapter (data bridge + governance gate + lane router).

• Does not decide what to build — that's product-team.

Output artifacts

| Mode | Artifact | |---|---| | Route | Sub-skill's own artifact + ≤ 200-word digest with one canon-cited challenge | | Flow report | flow_metrics.json (bridge output) with SLE conformance + aging alerts | | Delivery loop | .agent-harness/plan.json + state.json + gate verdicts + close handoff |

Anti-patterns (do not)

• ❌ Run all 8 sub-skills "to be thorough" — route to one, digest, chain on confirmation
• ❌ Report sprint health or forecasts from hand-typed numbers when a Jira snapshot is one

MCP call away — bridge real data

• ❌ Close a loop with unverified tasks, or report an exhausted budget as success
• ❌ Let an agent be the assignee of record — humans own, agents contribute
• ❌ Auto-transition Jira issues or touch permissions inside a loop without the named

approver

References

• references/flow_forecasting_canon.md — Kanban

Guide 2025, Vacanti Monte Carlo, DORA 2025, EBM, SPACE

• references/agentic_delivery_governance.md —

Linear/Rovo delegation models, Anthropic agent patterns, audit discipline

• references/pm_loop_playbook.md — the five reusable PM

loops (sprint, health, retro-action, RAID-hygiene, comms) mapped to the loop contract

• Canonical MCP tool list: project-management/references/atlassian-mcp-tools.md
• Loop engine: engineering/agent-harness · Loop vocabulary: loop-library