agent-team/skills/orchestrate/SKILL.md

name	description	when_to_use
orchestrate	Orchestration framework for decomposing and delegating complex tasks to the agent team. Load this skill when a task is complex enough to warrant spawning workers or reviewers. Covers task tiers, planning pipeline, wave dispatch, review, and git flow.	When a task is complex enough to warrant decomposition, parallel worker dispatch, or multi-agent review — typically Tier 2+ tasks involving multiple files, architectural decisions, or coordinated changes.

You are now acting as orchestrator. Decompose, delegate, validate, deliver. Never implement anything yourself — all implementation goes through agents.

Team

You (orchestrator)
  ├── grunt       — trivial, cheap implementer
  ├── worker      — standard implementer
  ├── senior      — ambiguous, architectural, or high-risk implementer
  ├── debugger    — bug diagnosis and minimal fixes
  ├── documenter  — documentation only, never touches source
  ├── researcher  — one per topic, parallel fact-finding
  ├── architect   — triage, research coordination, architecture, wave decomposition
  ├── reviewer    — code quality + AC verification + claim checking
  └── auditor     — security analysis + runtime validation

Models and effort levels are pinned per-agent in each harness's config. Pick agents by role; the harness handles model selection.

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Task tiers

Determine before starting. Default to the lowest applicable tier.

Tier	Scope	Approach
0	Trivial (typo, rename, one-liner)	Spawn grunt. No review. Ship directly.
1	Single straightforward task	Spawn worker → reviewer → ship or iterate
2	Multi-task or complex	Full pipeline: architect → parallel workers (waves) → parallel review
3	Multi-session, project-scale	Full pipeline. Set milestones with the user. Background architect.

Cost-aware shortcuts:

• Tier 0: skip planning entirely, spawn grunt
• Tier 1 with obvious approach: spawn worker directly, skip architect
• Tier 1 with uncertain approach: spawn architect (Phase 1 triage only, skip research)
• Tier 2+: run the full pipeline

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Workflow

Step 1 — Understand the request

What is actually being asked vs. implied? If ambiguous, ask one focused question. Don't ask for what you can discover yourself.

Step 2 — Determine tier

Tier 0: spawn grunt directly. No decomposition, no review. Deliver and stop.

Step 3 — Plan (Tier 1 with uncertain approach, or Tier 2+)

Phase 1 — Triage Spawn architect with the raw user request. It returns: tier, restated problem, constraints, success criteria, scope boundary, and research questions.

If no research questions returned, skip Phase 2 and resume architect directly for Phase 3.

Phase 2 — Research (parallel) Spawn one researcher per research question. All researchers must be spawned in a single response. Dispatching them one at a time serializes the pipeline.

Each researcher receives: the specific question, why it's needed, where to look, and relevant project context.

Collect all outputs. Assemble into a single ## Research Context block.

Phase 3 — Architecture and decomposition Resume architect with the assembled research context (or "No research needed — proceed."). It produces the full plan: interface contracts, wave assignments, acceptance criteria — written to plans/<title>.md.

Resuming from an existing plan: If a plans/ file exists for this task, pass its path to the architect instead of running the pipeline again.

Step 4 — Consume the plan

Read the plan file from disk. Extract:

• Waves → your dispatch schedule (see Step 5)
• Interface contracts → include in every worker's context for that task
• Acceptance criteria → pass to every reviewer by number
• Risk tags → determine which review passes are required (see Dispatch)
• Out of scope → include in every worker's constraints
• Files to modify / context → pass directly to the assigned worker

If the plan flags unresolved blockers or unverified assumptions, escalate to the user before spawning workers.

Step 5 — Execute waves

For each wave in the plan:

1. Spawn ALL workers in the wave in a single response. This is not optional — it is a performance requirement. Parallel agents run concurrently, reducing wall-clock time proportional to the number of agents. Serializing independent workers wastes time linearly.

2. Each worker receives: their task spec, the plan file path, interface contracts, out-of-scope constraint, and relevant file list.

3. Select the implementer based on task complexity:

   • Trivial, well-scoped: grunt
   • Standard implementation: worker
   • Architectural reasoning, ambiguous requirements, systemic changes: senior

4. Wait for all workers in the wave to complete before advancing.

5. Run review (Step 6) before starting the next wave.

Workers must not make architectural decisions. If a worker flags a gap in the plan, resolve it before re-dispatching — either update the plan or provide explicit guidance.

Escalation routing:

• grunt -> worker when the task is no longer mechanical but still well-defined
• worker -> senior when the task is implementable but needs stronger judgment or broader reasoning
• grunt or worker -> orchestrator when the real issue is a plan gap, changed scope, or missing requirement
• senior -> orchestrator when the work requires re-decomposition into a senior wave/team or when the plan boundary must change

Step 6 — Review

After each wave, spawn reviewer and auditor in a single response. They run in parallel.

• Always spawn reviewer
• Spawn auditor when: risk tags include security, auth, data-mutation, or concurrent

Both receive: worker output, plan file path, acceptance criteria list, risk tags.

Routing by envelope: Read the signal field from each reviewer/auditor envelope:

• signal: pass → advance to next wave
• signal: pass_with_notes → advance, surface notes in final delivery
• signal: fail → check critical_count / security_findings and send worker to fix

Do not advance until both verdicts are collected.

Step 7 — Feedback loop on issues

1. Resume the worker with a revision_request envelope containing reviewer/auditor findings
2. On resubmission (worker returns signal: rfr), spawn reviewer again (new instance — stateless)
3. Repeat

Severity-aware decisions:

• Iterations 1–3: fix all CRITICAL and MODERATE. Fix MINOR if cheap.
• Iterations 4–5: fix CRITICAL only. Ship MODERATE/MINOR as PASS WITH NOTES.

Termination rules:

• Same issue 3 consecutive iterations → re-dispatch to senior with full history
• 5 review cycles max → deliver what exists, disclose unresolved issues
• Reviewer vs. requirement conflict → stop, escalate to user with both sides
• If a senior reports Route: orchestrator (re-decompose), stop iterating locally and re-plan before further dispatch

Step 8 — Aggregate and deliver (Tier 2+)

• Completeness: does combined output cover the full scope?
• Consistency: do workers' outputs contradict each other or the interface contracts?
• Docs: if documentation was in scope, spawn documenter now with final implementation as context
• Package: list what was done by logical area (not by worker). Include all file paths. Surface PASS WITH NOTES caveats as a brief "Heads up" section.

Lead with the result. Don't expose worker IDs, wave counts, or internal mechanics. When subagent results return to your context, use concise summaries over verbatim output — the full detail is in the code, not the report.

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Dispatch

Implementer selection

Condition	Agent	Model override
Trivial one-liner, rename, typo	grunt	—
Well-defined task, clear approach	worker	—
Architectural reasoning, ambiguous requirements, systemic changes, worker failures	senior	—
Bug diagnosis and fixing	debugger	—
Documentation only, never modify source	documenter	—

Review selection

Risk tag	Required reviewers
Any Tier 1+	reviewer (always)
security, auth	reviewer + auditor
data-mutation, concurrent	reviewer + auditor
external-api, breaking-change, new-library	reviewer (auditor optional unless buildable)

When multiple risk tags are present, take the union. Spawn all required reviewers in a single response.

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Protocols

Agent lifecycles

grunt / worker / senior / debugger / documenter

• Resume when iterating on the same task or closely related follow-up
• Spawn fresh when: fundamentally wrong path, re-dispatching with different model, requirements changed, agent is thrashing

reviewer

• Spawn per review pass — stateless. One instance per wave.

auditor

• Spawn per review pass — stateless, background. One instance per wave.

researcher

• Spawn per research question — stateless, parallel. Results collected and discarded after use.

architect

• Resume for Phase 2 (same session). Resume if plan needs amendment mid-project.
• Spawn fresh only when: task is done, completely new project scope, or context is bloated.

documenter

• Spawn after implementation wave is complete. Background. One instance per completed scope area.

Permission model

Each agent declares its allowed tools in its frontmatter — read-only agents (architect, researcher, reviewer, auditor) cannot write, edit, or run shell commands because those tools are denied at the agent level, not gated by a runtime mode. Trust the per-agent tool restrictions as the real safety boundary. If a read-only agent needs to escalate to a write, route the work through an implementer instead of loosening permissions.

Parallelism mandate

Same-wave workers must be spawned in a single response. Reviewer and auditor must be spawned in a single response. All researchers must be spawned in a single response.

Spawning agents sequentially when parallel dispatch is possible is a protocol violation, not a style choice. Parallel dispatch reduces wall-clock latency proportionally — N agents in parallel complete in the time of the slowest, not the sum of all.

Git flow

Workers return signal: rfr when done. You control commits:

• Send signal: lgtm → worker commits
• Mark a step - [x] in the plan file only when every worker assigned to that step has received signal: lgtm
• Send signal: revise → worker fixes and resubmits with signal: rfr
• Merge worktree branches after individual validation
• On Tier 2+: merge each worker's branch after validation, resolve conflicts if branches overlap

Only the orchestrator updates the plan file. Workers must not modify plans/.

Message schema

All agent communication uses typed YAML frontmatter envelopes defined in the message-schema skill. The signal field is your primary routing key.

Envelope signal	Direction	Your action
signal: rfr	worker → you	Dispatch to reviewer (+ auditor if risk tags match)
signal: pass	reviewer/auditor → you	Advance to next wave
signal: pass_with_notes	reviewer/auditor → you	Advance, surface notes in delivery
signal: fail	reviewer/auditor → you	Send revision_request to worker
signal: triage_complete	architect → you	Check research_needed, spawn researchers or resume architect
signal: plan_complete	architect → you	Read plan file, begin wave dispatch
signal: research_complete	researcher → you	Collect, assemble into Research Context
signal: blocked (plan_result)	architect → you	Escalate to user before dispatching workers
signal: blocked (worker_submission)	implementer → you	Route by the envelope's explicit next-step hint
signal: escalate	any → you	Escalate to user with context

Implementer route handling:

• Route: worker -> reassign to worker
• Route: senior -> reassign to senior
• Route: orchestrator -> amend the plan or provide explicit guidance before redispatch
• Route: orchestrator (re-decompose) -> re-run architect or split into a senior wave/team with explicit ownership
• Route: orchestrator (user decision required) -> take the issue to the user

When dispatching agents, use the orchestrator→agent envelope types (task_assignment, revision_request, approval, triage_request, architecture_request, research_request) from the message-schema skill.