# AI Autopilot

> Delivers large multi-concern specs and backlog runs autonomously: decomposes specs into sub-specs (or normalizes work items into a backlog DAG), deep-plans with parallel agents, builds a dependency DAG, implements in waves, runs a single final quality loop with one bounded quality-remediation pass (verify+guard+review on full changeset), delivers via PR. Trigger for 'implement spec-NNN end to end', 'autopilot this', 'autonomous delivery', 'decompose and ship', 'run the backlog', 'execute these GitHub issues', 'process the sprint backlog'. Invocation is the approval gate. Not for small or single-concern tasks; use /ai-build instead. Not for ambiguous requirements; use /ai-brainstorm first.

- Skill: `arcasilesgroup/ai-autopilot` (Agent Skill, multi-file: 8 files)
- Install (CLI): `npx skillmds@latest add arcasilesgroup/ai-autopilot`
- Raw SKILL.md: https://api.skillmd.com/api/skills/arcasilesgroup/ai-autopilot/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Product & Planning
- Author: arcasilesgroup (https://skillmd.com/u/arcasilesgroup)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/arcasilesgroup/ai-autopilot

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# Autopilot v2

Delivers large multi-concern specs and backlog runs autonomously — decomposing into sub-specs (or a backlog DAG), deep-planning with parallel agents, implementing in waves, and shipping via PR with one bounded quality-remediation pass. Use it for end-to-end delivery of specs with ≥3 concerns or ≥10 file changes; use /ai-build for smaller single-concern work.

## Purpose

Autonomous execution of large approved specs via a 6-phase pipeline: decompose into N focused sub-specs, deep-plan each with parallel agents, orchestrate a dependency-aware DAG, implement in waves, run one final verify+guard+review pass (with one bounded quality-remediation pass on the full changeset), deliver via PR with a transparency report. One invocation, full disclosure. Done only when sub-spec work converges into a delivery PR against protected main.

Thin orchestrator: phases READ other skills' SKILL.md and EMBED instructions into subagent prompts (no inline implementation), so autopilot inherits their improvements.

## When NOT to Use

Triggers (≥3 concerns / ≥10 files, post-`/ai-brainstorm` approval, `--backlog`) live in the description + Dispatch threshold. Do NOT use for:

- Need human review between phases — use `/ai-build` with manual checkpoints.
- Cross-repo changes — coordinate manually.
- Data migrations with destructive DDL — require explicit user approval per step.

## Process

**Step 0 — Validate**: confirm `.ai-engineering/specs/spec.md` is not a placeholder (else STOP → `/ai-brainstorm`). On `--resume`, read `.ai-engineering/runtime/autopilot/manifest.md` and re-enter at the Resume Protocol. Load stack contexts (manifest `providers.stacks` + `.ai-engineering/overrides/<stack>/conventions.md`); pass paths (not content) to subagents. plan.md is not required — Phase 2 agents generate their own. `ai-eng host probe` is diagnostic/advisory only; `ok_to_dispatch` is not a standard-flow execution gate and cannot block `/ai-autopilot`.

**Step 1 — DECOMPOSE** (`handlers/phase-decompose.md`): extract N independent concerns; abort if N<3 (recommend `/ai-build`); write sub-spec dirs + the execution manifest.

**Step 2 — DEEP PLAN** (`handlers/phase-deep-plan.md`): dispatch explore+plan agents in parallel; each enriches `sub-NNN/spec.md` (Exploration) + `plan.md` (checkbox tasks with exports/imports). Failed agents retry once → mark `plan-failed`.

**Step 3 — ORCHESTRATE** (`handlers/phase-orchestrate.md`): build the file-overlap matrix + import-chain graph from Phase-2 evidence (never from spec text alone); construct the wave-assigned DAG; merge unresolvable conflicts.

**Step 4 — IMPLEMENT** (`handlers/phase-implement.md`): per-wave kernel from `.claude/skills/_shared/execution-kernel.md`. Dispatch build agents per sub-spec in parallel within a wave; each task self-validates via TDD; wave-end guard advisory remains for governance. Collect Self-Reports + per-wave commits. Cascade-block dependents of failed sub-specs.

**Step 5 — QUALITY LOOP** (`handlers/phase-quality.md`): read ai-verify / ai-review / ai-governance SKILL.md once at loop entry; dispatch verify+guard+review in parallel on the full changeset; consolidate findings (unified severity). Clean → Phase 6. Blocker/critical/high findings enter Phase 5b only if the one bounded quality-remediation pass is unused and the fixes are finding-scoped. Remaining blocker/critical/high findings after final reassessment → STOP + escalate to user.

**Phase 5b — BOUNDED REMEDIATION** (`handlers/phase-quality.md`): persist `quality_remediation.max_attempts: 1` in the autopilot manifest, map each finding to `sub-NNN`, `integration`, or `shared`, run cross-platform focal reproducers, return to Step 5 final reassessment. No re-decompose, no re-plan, no second remediation pass.

**Step 6 — DELIVER** (`handlers/phase-deliver.md`): build the Integrity Report; follow `/ai-pr` SKILL.md; cleanup runtime dir; clear spec.md + plan.md; verify cleanup. `--resume` handles mid-pipeline re-entry.

## Flags

| Flag         | Behavior                                                                                                                                                |
| ------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `--resume`   | Read `.ai-engineering/runtime/autopilot/manifest.md`, determine pipeline state, re-enter at the correct phase/wave. Never re-executes completed phases. |
| `--no-watch` | Create PR without the watch-and-fix loop. For draft delivery or externally-managed CI.                                                                  |
| `--backlog --source <github|ado|local>` | Backlog mode (D-127-12, absorbs legacy `/ai-run`). Normalizes work items from the source into the run model: `github` (Issues + GitHub Projects v2), `ado` (Azure Boards work items), `local` (a markdown task-list path). The 6-phase pipeline still runs; Phase 1 DECOMPOSE → intake + per-item planning; Phase 4 IMPLEMENT dispatches one bounded build per item. |

## Dispatch threshold

Dispatch the `ai-autopilot` agent when work has ≥3 independent concerns, touches ≥10 files, follows `/ai-brainstorm` approval (spec.md exists, not a placeholder), or runs any `--backlog`; smaller scope hands off to `/ai-build`. The agent handle is `.claude/agents/ai-autopilot.md`; the procedural contract lives in this SKILL.md.

## Governance

DEC-023: invocation is the single approval gate; internal gates (sub-spec validation, DAG verification, quality convergence) are automatic and cannot be bypassed. The consolidation path is mandatory: sub-spec branch/worktree → wave or integration commits → final PR → protected main. State transitions live on disk in `.ai-engineering/runtime/autopilot/manifest.md`, never in agent memory.

## Examples

See `references/examples.md` for the three canonical invocations (end-to-end delivery, `--resume` after interruption, `--backlog --source github`), the failure-recovery rows, the telemetry event taxonomy, and the common-mistakes checklist (never run on draft specs, never cross repos, never carry context across sub-specs, never hand-edit mirrors).

## Integration

Called by: user directly post-`/ai-brainstorm` approval (or with `--backlog` for backlog runs). Reads: `_shared/execution-kernel.md`, `ai-verify/SKILL.md`, `ai-review/SKILL.md`, `ai-governance/SKILL.md`, `ai-pr/SKILL.md`, `ai-commit/SKILL.md`. Delegates to: `ai-explore`, `ai-build`, `ai-verify`, `ai-advise`, `ai-review` agents. Transitions to: `/ai-branch-cleanup`. See also: `/ai-build` (smaller scope), `/ai-board sync` (lifecycle transitions for backlog mode), `references/examples.md`.

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