execute-fleet

execute-fleet

Atlas Fleet is the premium parallel sibling of execute-task. It keeps the same proof discipline, but the orchestrator owns the scoreboard while workers only build inside isolated worktrees.

Hard Gates

Before the first wave, all gates must pass. If any gate fails, report the gap and stop; do not fall back to solo execution from inside this skill.

1. mcp__plugin_prd_go__detect_capabilities() reports tier: "premium" and atlas-launcher MCP registration/aliveness.
2. mcp__atlas-launcher__inbox_read is callable for this session.
3. .taskmaster/tasks/tasks.json exists.
4. .taskmaster/reports/task-complexity-report.json exists.
5. git status --short is empty. Fleet starts only from a committed base.
6. The integration branch policy is clear: use fleet-integration; main is never auto-touched.

SOLE-WRITER RULE: only this orchestrator writes .taskmaster/tasks/tasks.json and .atlas-ai/state/pipeline.json. Workers must never edit those files. The orchestrator may update task state only through TaskMaster or the plugin pipeline MCP, and only after verification.

Wave Loop

Repeat until no runnable tasks remain:

1. Call mcp__plugin_prd_go__compute_fleet_waves(concurrency=<N>, tag=<tag>). Use the returned frontier as the only dispatch source. If it reports a deadlock, render status, mark the blocked set, and stop dispatching those tasks.
2. For each chunk in the current wave, spawn exactly one worker: mcp__atlas-launcher__session_spawn(isolation="worktree", report_to=<this session>, model=<routing[task_id] model part>, prompt=<worker prompt>). Model is NEVER left default: compute_fleet_waves returns a routing map (task id -> backend:model) from the capability ladder — Fable for the hardest/longest-running (frontier tier), down the cost-efficiency curve to haiku for trivial tasks. Pass the model part explicitly; non-claude backends require experimental_backends=true and the launcher backend param. The prompt must include the full task JSON inline; never tell workers to read shared tasks.json.
3. Inspect the spawn result. If prompt_injected is false, re-kick once with mcp__atlas-launcher__session_send using the same worker prompt, then confirm injection/readiness. If it is still false, treat that worker as failed and apply the retry policy below.
4. Render the fleet status view after the wave starts and after each wave transition.
5. Poll mcp__atlas-launcher__inbox_read for terminal worker messages. The only accepted terminal status vocabulary is DONE, DONE_WITH_CONCERNS, NEEDS_CONTEXT, or BLOCKED; any other terminal word is a protocol failure and counts as a worker failure.
6. On a completion message, verify the branch, never the narration:
   • The worker branch contains .atlas-ai/cdd/task-<id>.json.
   • No .atlas-ai/evidence/ file in that branch contains a non-zero Exit status N line.
   • The branch changed only its own worktree scope and did not edit .taskmaster/tasks/tasks.json or .atlas-ai/state/pipeline.json.
7. Never mark a task done without the CDD card. Missing card means the worker did not satisfy the contract, regardless of any DONE message.
8. Merge verified worker branches into fleet-integration sequentially, one at a time. After each merge, run the checker/build gate expected for the project before merging the next branch.
9. Mark the task done only after the merge gate passes: python3 script.py set-status --id <id> --status done.
10. Recompute waves after every accepted merge. Do not keep dispatching from a stale frontier.

Worker Prompt Template

Embed this template verbatim for each worker, replacing placeholders before dispatch:

WORKER_CONTRACT_ORCHESTRATOR_REPORT_TO
You are an Atlas Fleet worker. Your orchestrator is <ORCHESTRATOR_ID>. Report every question, blocker, and terminal result to <REPORT_TO_SESSION>.

WORKER_CONTRACT_FULL_TASK_JSON_INLINE
Your assigned task JSON is inline below. Treat this as the source of truth. Do not read shared .taskmaster/tasks/tasks.json.
<FULL_TASK_JSON>

WORKER_CONTRACT_WORKTREE_BRANCH
Work only in this isolated worktree and branch:
worktree: <WORKTREE_PATH>
branch: <WORKER_BRANCH>

WORKER_CONTRACT_CDD_CARD
Before reporting any terminal status, write this CDD card in your worktree: .atlas-ai/cdd/task-<id>.json. The card must list the checks you ran and the evidence paths that prove them. Evidence files under .atlas-ai/evidence/ must contain the FINAL verification run ONLY (one green run, one exit-status line) — intermediate TDD red runs go to .atlas-ai/logs/, never evidence/ (ship-check Gate 5 reads every Exit status line in evidence/ as final-state proof).

WORKER_CONTRACT_TERMINAL_STATUS
End with exactly one terminal status: DONE | DONE_WITH_CONCERNS | NEEDS_CONTEXT | BLOCKED.
Report it via mcp__atlas-launcher__inbox_send(target_session=<REPORT_TO_SESSION>, message_type="task_handoff", payload=<JSON string with at least {"task_id": <id>, "status": "<terminal status>", "branch": "<worktree branch>", "cdd_card": ".atlas-ai/cdd/task-<id>.json"}>, sender_session=<your session name>). The launcher message_type allowlist is task_handoff | notification | data | request | heartbeat — terminal reports use task_handoff; the status lives INSIDE the payload JSON.

WORKER_CONTRACT_HARD_RULES
Hard rules: never edit .taskmaster/tasks/tasks.json or .atlas-ai/state/pipeline.json; never git push; commit only in your own worktree branch.

WORKER_CONTRACT_QUESTIONS_INBOX
Ask questions before building if context is missing: use mcp__atlas-launcher__inbox_send(target_session=<REPORT_TO_SESSION>, message_type="request", payload=<your question as a string>, sender_session=<your session name>). ("question"/"completion"/"blocker" are template intents, not runtime message types — see docs/INTEGRATION-prd-taskmaster.md in the atlas-launcher repo, contract v1.)

Failure Paths

• Silent/dead worker: if there is no inbox message and the session is gone, re-queue the task ONCE with a fresh worker prompt. On the second failure, mark the task BLOCKED in the orchestrator scoreboard and continue with remaining tasks.
• NEEDS_CONTEXT: answer through mcp__atlas-launcher__inbox_send, then let the same worker continue. If it cannot continue, count it under the same retry cap.
• Worker BLOCKED: record the blocker, mark the task BLOCKED, and continue with independent tasks.
• Merge conflict: do not force. Do not resolve by guessing. Create a fix task that captures the conflict and continue with remaining non-conflicting work.
• Evidence failure: do not merge, do not mark done, and do not rewrite the worker's CDD card on their behalf.

Status Rendering

After every wave transition, render the terminal status view with the UX-SPEC grammar. Use model plus index names such as claude-1, codex-1, and claude-2. Keep the gate line in plain English every time.

┌─ atlas fleet ── wave 2 of 3 ──────────────── ▶ running 12m ┐
│  wave 1  ✓ merged     3 tasks · 18m · integration green    │
│  wave 2  ▶ running                                         │
│    claude-1   task 6  API endpoints      ▰▰▰▱  3/4         │
│    codex-1    task 7  UI components      ▰▰▱▱  2/4         │
│    claude-2   task 9  DB migrations      ✓ done — waiting  │
│  wave 3  ○ queued     4 tasks · starts when wave 2 merges  │
│                                                            │
│  Gate: a wave merges only after the checker approves it    │
│        and the integration branch builds green.            │
│                                                            │
│  watch:  atlas fleet status        logs: .atlas-ai/fleet/  │
└────────────────────────────────────────────────────────────┘

Termination

When all waves are done, or all remaining tasks are BLOCKED, switch to fleet-integration and run:

python3 skel/ship-check.py

If it exits non-zero, report the failing gate and stop. If it exits 0, emit SHIP_CHECK_OK exactly once, then open one final PR from fleet-integration. Do not print the token anywhere else. Do not merge the PR yourself.

Non-Exits

This skill never kills the shell and never pushes. Halt conditions are reported to the caller and, when relevant, to the launcher inbox.