agent-team/skills/orchestrate/SKILL.md

name	description
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.

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

Team

You (orchestrator)
  ├── worker        (sonnet default — haiku for trivial, opus for architectural)
  ├── debugger      (sonnet) — bug diagnosis and minimal fixes
  ├── documenter    (sonnet) — documentation only, never touches source
  ├── researcher    (sonnet, background) — one per topic, parallel fact-finding
  ├── architect     (opus, effort: max) — triage, research coordination, architecture, wave decomposition
  ├── reviewer      (sonnet) — code quality + AC verification + claim checking
  └── auditor       (sonnet, background) — security analysis + runtime validation

---

Task tiers

Determine before starting. Default to the lowest applicable tier.

Tier	Scope	Approach
0	Trivial (typo, rename, one-liner)	Spawn worker (haiku). 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 worker with model: haiku
• 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

---

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 worker directly with model: haiku. 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 .claude/plans/<title>.md.

Resuming from an existing plan: If a .claude/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 cost and performance requirement. Parallel workers share the same cached context prefix at ~10% token cost. Serializing independent workers wastes both money and time.

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

3. Select model based on task complexity:

   • Trivial, well-scoped: model: haiku
   • Standard implementation: model: sonnet (default)
   • Architectural reasoning, ambiguous requirements, systemic changes: model: opus

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.

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 — or any code that can be built and tested

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

Collect both verdicts before deciding whether to advance to the next wave or send back for fixes.

Step 7 — Feedback loop on issues

1. Resume the worker with reviewer findings and instruction to fix
2. On resubmission, 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 as worker with model: opus and full history
• 5 review cycles max → deliver what exists, disclose unresolved issues
• Reviewer vs. requirement conflict → stop, escalate to user with both sides

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.

---

Dispatch

Implementer selection

Condition	Agent	Model override
Trivial one-liner, rename, typo	worker	haiku
Well-defined task, clear approach	worker	sonnet (default)
Architectural reasoning, ambiguous requirements, systemic changes, worker failures	worker	opus
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.

---

Protocols

Agent lifecycles

worker / 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.

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 they could run in parallel is a protocol violation, not a style choice. Parallel agents share a cached context prefix — each additional parallel agent costs ~10% of what the first agent paid for that shared context.

Git flow

Workers signal RFR when done. You control commits:

• LGTM → worker commits
• Mark a step - [x] in the plan file only when every worker assigned to that step has received LGTM
• REVISE → worker fixes and resubmits with 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 .claude/plans/.

Review signals

Signal	Direction	Meaning
RFR	worker → orchestrator	Ready for review
LGTM	orchestrator → worker	Approved, commit your changes
REVISE	orchestrator → worker	Fix the listed issues and resubmit
VERDICT: PASS / PASS WITH NOTES / FAIL	reviewer → orchestrator	Review result
VERDICT: PASS / PARTIAL / FAIL	auditor → orchestrator	Runtime validation result