Multi-Agent Orchestration

Standalone Codex

Create each bounded worker with spawn_agent, deliver context to a running worker with send_message, trigger another turn for an idle existing worker with followup_task, wait with wait_agent, and stop a current turn with interrupt_agent only when necessary. Never spawn codex-verifier or call codex-run.ts.

Read the live concurrency limit and stay within it. Writers share the working tree unless the live host explicitly offers isolation. Assign non-overlapping ownership and serialize implementer and test-writer phases; only read-only reviewers may overlap. Codex implementers are not promised Claude worktree isolation.

Phase 1: Research & Feasibility (GO/NO-GO Gate)

Before delegating to agents:

1. Parse requirements - Break down what needs to happen
2. Identify workstreams - Which are independent? Which have dependencies?
3. Assess scope - Is this actually multi-agent work, or simpler than it looks?
4. Sort into two piles (the Orchestration Tax — your review attention is the serial bottleneck and it doesn't parallelize):
   • Delegate-async — isolated, well-specified work where your judgment lands at the gate (you review the finished result): scaffolding, mechanical refactors, test writing, doc generation, independent file areas. Fan these out.
   • Hold-the-lock — work where the judgment is the work: a subtle bug, an architecture decision, anything that needs your evolving mental model of the system. Parallelizing these doesn't scale output — it thrashes the one serial resource and everything comes back worse. Do them yourself, serially, one at a time.

GO/NO-GO Verdict:

• GO - 3+ delegate-async workstreams, clear boundaries, agents work independently. Proceed — fan out the first pile only.
• SIMPLIFY - <3 workstreams, OR the work is mostly hold-the-lock regardless of size. Delegate the isolated bits with direct Agent() calls and keep the judgment-heavy parts yourself.
• NO-GO - Requirements unclear, scope too large, or high risk of file conflicts. Report and stop.

Do not proceed past this gate without an explicit verdict.

Phase 2: Orchestrate

Delegate to the Maestro agent for multi-agent task orchestration.

The Maestro agent handles: agent selection, parallel execution, workflow coordination, and agent teams.

For simple delegation (1-2 agents), use Agent() directly without invoking this skill.

Verify Subagent Claims Independently

A subagent's "done" is a claim, not a result. Before building on it, committing, or reporting success:

1. Re-run the briefed verification yourself against the real artifact — the actual build, the actual binary's output, the actual grep sweep. Never forward a subagent's self-reported pass as your own verification.
2. Check the capability envelope. Subagents may lack tools you assume (a no-Bash implementer cannot run builds or delete files — it will improvise, e.g. zeroing a file instead of removing it, and still report done). Read what the agent says it couldn't do, then absorb the gap yourself: run the deletion, the build, the test.
3. Re-delegate vs fix solo. A trivial break found during verification (single file, few lines) — fix it solo with a one-line stated reason; round-tripping to a fresh subagent costs more than the fix. Anything that adds scope goes back out as a new, fully-briefed delegation.
4. Resume, don't respawn. If a subagent's report was cut off or is missing a section, Claude uses SendMessage; standalone Codex uses send_message while it is running or followup_task once idle. A respawn rebuilds its context from nothing and re-does paid work.

Failure mode this section exists to prevent: chaining on an unverified "done" and discovering three phases later that the build never ran.

Standalone Codex orchestration

Follow the native lifecycle at the top of this skill. Keep the number of live agents within the concurrency limit reported by the current Codex session; queue dependent work instead of over-subscribing it. Use spawn_agent for each new bounded workstream and never invoke the Claude-to-Codex bridge.

Claude agent teams and dynamic workflows are Claude-only. Do not try to emulate their APIs in Codex; native agents plus the current concurrency limit are the Codex path for the fan-out guidance below.

When to Fan Out (Teams mode)

Use full parallel team fan-out instead of sequential subagent delegation when:

Scenario	Fan out?	Why
3+ independent file areas	Yes	Maximum parallelism, isolated context per agent
Frontend + Backend + Tests	Yes	No file conflicts, clear boundaries
Large codebase analysis	Yes	Independent context per agent prevents bleed
Competing approaches	Yes	Explore alternatives in parallel before deciding
Sequential dependent work	No	Use subagents in sequence; fan-out adds overhead
Quick single investigation	No	Overhead not worth it; use /explore directly

Prerequisites for fan-out

Two different mechanisms get confused here, so be explicit about which one you want:

• Subagent fan-out (Agent calls in one message) needs nothing enabled. This is what cc-settings actually uses, and what the rest of this skill assumes. Each subagent gets its own context window and reports back to you.
• Agent teams (teammates that message each other and share a task list) are experimental and disabled by default upstream; cc-settings enables them via CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS: "1" in config/10-core.json. Enabled means available, not automatic — Claude forms a team only when you ask, or when it proposes one and you approve. Pick a team over plain fan-out only when workers need to challenge each other mid-flight; if you just need N results collected, fan-out is cheaper and lands them in one place. Full rule: CLAUDE-FULL.md → "Agent teams — enabled, deliberately not the default".
  • Feasibility gates that come before cost: teammate permission prompts surface in the lead, so a team is not unattended; /resume does not restore in-process teammates; teammates cannot spawn teammates; and two teammates editing one file overwrite each other, so split by file ownership at spawn.
  • For split panes: tmux, or iTerm2 with the it2 CLI. config/10-core.json pins teammateMode: "auto".
  • A team forms when the lead spawns the first teammate and is cleaned up when the session ends; there is no create or delete tool.

Alternative: dynamic workflows (research preview)

A dynamic workflow is a JS harness that spawns subagents, holds plan state outside your context window, runs up to 16 agents concurrently (1000 total), and resumes from cached results within a session. The trigger isn't task size — it's whether the task risks one of three failure modes a single context window is prone to:

• Agentic laziness — stopping at 20 of 50 items and declaring done. → fan-out-and-synthesize: one agent per item, barrier-join the results.
• Self-preferential bias — preferring your own output when you're also the judge. → adversarial verification: a separate agent refutes each finding; or a tournament of pairwise comparisons (more reliable than absolute scoring for ranking or taste).
• Goal drift — losing "don't do X" constraints across compaction. → each subagent gets a focused, isolated goal that can't drift.

Shapes worth naming when you build one: classify-and-act, fan-out-and-synthesize, generate-and-filter, tournament, loop-until-done (spawn until a stop condition, not a fixed count). Not only for marathons — a quick workflow is valid: "quick workflow to adversarially check this one assumption."

• Budget — workflows burn more tokens; cap with "…budget 10k tokens" and the harness enforces it.
• Size — dynamic workflows default to a medium size guideline, aiming for fewer than 15 agents; the running workflow's status line shows the current default. Override per-project via the workflowSizeGuideline settings key when a task genuinely needs more.
• Quarantine — for triage over untrusted input, agents that read public/untrusted content must not also take privileged actions; split reading from acting so an injected page can't trigger a privileged step.

Two entry points:

• One-shot: say "use a workflow to …" or the keyword ultracode in your prompt. Pair with /loop for repeatable triage/verification/research.
• Session-wide: /effort ultracode — auto-orchestrates a workflow for every substantive task.

The maestro Agent() fan-out above is the default in cc-settings; workflows are for replayability or scale beyond subagent fan-out. Don't rewire skills to depend on the Workflow tool — its API is still preview-stage — but a skill may ship an opt-in example (see audit's references/nuclear-review.workflow.js): a template you adapt, never a runtime dependency.

Output

Report: team composition (when fan-out chosen), task assignments, coordination strategy, and progress.

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Variant: Phased Long-Running Execution

For tasks too large for a single context window. Implements checkpoint/restore, automatic verification, and graceful recovery.

When to Use

• Large refactors spanning 10+ files
• Full feature implementation with tests
• Migration tasks (dependency upgrades, API changes)
• Any task estimated at >50% context window

Checkpoint Strategy

Context-Threshold Checkpoints

No hook saves checkpoints automatically — the agent must invoke checkpoint.ts save itself when it notices context usage crossing a threshold. See hooks/checkpoint.md for the recommended actions at 70% / 80% / 90%.

Manual Checkpoints

Save checkpoints at these milestones:

• After completing a logical phase
• Before risky operations (schema changes, large refactors)
• After passing verification

Maintenance Checkpoints

A completed phase is a commit checkpoint, not a stopping point — the job is the whole plan, not the first green milestone; finishing a phase means starting the next, and you only hand back to the user on a genuine blocker. On long runs, spend one pass every few phases on maintenance before drift accumulates: prune plan bloat (tasks that no longer match what the code taught you), refresh the live handoff so a cold resume lands cleanly, delete dead TODOs, and reconcile the plan with the current architecture rather than preserving development-only shims the plan predates.

Stamp the plan with the commit it was written against (git rev-parse --short HEAD) and re-stamp on every maintenance pass. The reconcile semantics: re-run the done-criteria of tasks marked complete (a "done" that no longer verifies gets reopened, not trusted), refresh file/line refs that drifted since the stamp, and retire tasks obsoleted by intervening changes with a one-line reason so they aren't re-litigated later.

Checkpoint Contents

See hooks/checkpoint.md for the full checkpoint JSON schema, storage location, and recommended checkpoint threshold actions.

Save/restore state with /checkpoint — see that skill for the commands.

Verification Stack

Every checkpoint must pass Levels 1-3 before saving. Full verification (Levels 1-5) at task completion. See hooks/verification-check.md for the complete 5-level stack, per-agent requirements, and failure handling.

Workflow

Phase 1: Planning

Agent(planner, "Break down [task] into phases with dependencies and estimates")

Produce a phased plan with:

• A ## Functional DAG up top (docs/functional-dag.md) — the fan-out piles are its columns, so the parallelizable set is read off the diagram instead of guessed
• Ordered phases with dependencies
• Token estimates per phase
• Checkpoint points identified
• Verification criteria per phase

Phase 2: Execution Loop

For each phase:

1. Start: Log phase start
2. Implement: Execute the phase work
3. Verify: Run verification stack
4. Checkpoint: Save state if milestone reached
5. Monitor: Check context usage, checkpoint if threshold hit

To drive this loop unattended, set a /goal whose condition matches the completion promise — e.g. /goal all phases complete, tsc + lint + tests exit 0, git status is clean. The goal evaluator runs after every turn and keeps the session going until the condition holds; it survives --resume so a goal set before a handoff carries into the next session.

Phase 3: Completion

## Verification Summary
- [x] TypeScript compiles
- [x] Biome lint passes
- [x] Tests pass (N/N)
- [x] All phases completed

<promise>COMPLETE</promise>

Recovery from Interruption

When resuming after interruption:

1. Run checkpoint.ts restore to load latest state
2. Check git status for uncommitted work
3. Review remaining todos
4. Run verification to confirm baseline
5. Continue from next incomplete phase

Completion Promise

The task is NOT complete until:

1. All phases done
2. Verification passes (compile + lint + test)
3. Git is clean (all changes committed)
4. Summary provided with what was done

Only then output:

<promise>COMPLETE</promise>

Never claim completion with failing verification.

Example

User: "Migrate all class components to hooks across the app"

→ Agent(planner, "Break migration into phases by module")
Plan: 6 phases, ~4 context windows estimated

Phase 1: Core hooks (auth, routing)
  → Implement → Verify → Checkpoint at 30%
Phase 2: Feature hooks (dashboard, settings)
  → Implement → Verify → Checkpoint at 55%
  → Context at 70% → agent checkpoints, continue
Phase 3: Shared components
  → Context at 90% → agent checkpoints, hands off
  [New session resumes from checkpoint]
Phase 4-6: Continue...
  → Final verification → <promise>COMPLETE</promise>