auto

name	auto
description	Super-master orchestrator — 一行命令跑完整 6-stage feature 开发流 (research conditional → discuss → plan → task → verify → retro mandatory), 适合 trivial / well-defined feature OR 你 想 hands-off。每 stage 内部仍 fan-out sub-workflow per 现有 stage-master orchestrator pattern。 v3.2.0 强化:Phase 0 AI 1-shot complexity assessment + Phase 0.5 understanding check prompt + Phase 5 `/retro` mandatory。 schema_version: harnessed.workflow.v3 with delegates_to (6 sub: research conditional order 0 + 4 stage-master order 1-4 + retro mandatory order 5) + disciplines_applied (6 default) + tools_available (agent-teams-create + planning-with-files)。Fail-fast default; opt-in `--staged` flag 重现 stage gate UX (每 stage 完停 user review)。 Triggered by slash command `/auto` (bare per ADR 0030 namespace policy D-02 LOCK) after `harnessed setup`.
trigger_phrases	["auto","自动跑完","完整流程","一键开发","auto chain","super master"]

auto super-master orchestrator (v3.2.0 enhanced)

Overview

v3.2.0 NEW — 6-stage cadence (research conditional → discuss → plan → task → verify → retro mandatory), sister 4 stage-master /discuss /plan /task /verify 独立 invoke 仍 work; /auto 是 opt-in 一行命令 chain。

order	sub	gate ref	mode	when fires
0	research	judgments.stage-routing.auto-research-unclear.fires	serial	user_understanding_unclear == true (Phase 0.5 prompt n)
1	discuss	(unconditional — 4-stage chain 起点)	serial	always
2	plan	(unconditional — stage 2)	serial	always
3	task	(unconditional — stage 3)	serial	always
4	verify	(unconditional — stage 4 收尾)	serial	always
5	retro	(unconditional — auto mode mandatory)	serial	always

Engine runtime spawns 6 sub workflow.yaml via runMasterOrchestrator per T3.5.W0.1 — recursive 一层抽象 (super-master → stage-master OR standalone → sub-workflow):

• top-level invoke /auto → load workflows/auto/workflow.yaml → runAutoPreFlight hook
• pre-flight Phase 0: assessComplexity(taskDescription) → small/medium auto continue; large → prompt user 切 --staged (y) OR abort 建议手动 (n)
• pre-flight Phase 0.5: promptUserUnderstanding() → y skip research; n set user_understanding_unclear = true 进 ctx → research gate fires
• spawn order 0: workflows/research/workflow.yaml (if gate fires)
• spawn order 1-4: 4 stage-master workflows/<sub>/auto/workflow.yaml
• spawn order 5: workflows/retro/workflow.yaml (mandatory unconditional)

K8 ctx single snapshot:auto top-level invoke 1 snapshot, pass to all 6 spawn (sister Phase 3.5 W0.1 pattern verbatim, 1 snapshot per top-level invoke 跨整个 cycle)。

Default behavior

• Pre-flight gates: complexity assessment + understanding check (interactive prompts)
• Continuous chain: 6 stage 一行命令跑完, 中间不停
• Fail-fast: 任一 stage fail 立即停, harnessed resume 续
• New-project bootstrap: .planning/ROADMAP.md missing → stage ① runs /gsd-new-project (when available) or creates the minimal ROADMAP/STATE/REQUIREMENTS skeleton first
• Context 自动传递: planning-with-files .planning/phases/<NN>-<slug>/ 喂下 stage
• Retro mandatory: auto mode hands-off scenario,末尾强制 /retro 总结 (无 opt-out flag)
• 沿用 sister planning-with-files /plan 持久化 cadence

Optional flag

• --staged opt-in: 每 stage-master 跑完停, 等用户 review/confirm 后跑下 stage (stage gate UX)

When to use /auto vs 4 stage-master 手动

✅ 触发 /auto:

• Trivial / well-defined feature (e.g. CRUD endpoint + standard pattern)
• Hands-off use case (你想"跑完再回来看")
• 跨 stage decision 都明显 (无 open question)
• AI 自动判断需求复杂度 small/medium → 直接 continue;large → 自动建议 --staged

❌ 跳过 /auto → 分阶段手动 /discuss → /plan → /task → /verify:

• 关键发布 / 大重构 (需 stage gate review)
• 跨 stage 有 open implementation decisions
• 你想 stage 之间 hands-on review
• 不确定整体方向 (此时手动 /discuss 强 grill)
• AI complexity gate 判定 large 且 user 不切 --staged → 建议 abort 手动

Capability refs

Sister workflows/capabilities.yaml:

• agent-teams-create — Bucket 5 agent-platform (multispec Pattern C 4-specialist team in verify stage if critical-release-upgrade gate fires)
• planning-with-files — Bucket 4 核心 capability (持久化 task_plan.md + progress.md 跨 6 stage 自动 context 传递)
• Downstream sub refs:
  • sub research upstream → workflows/research/workflow.yaml (standalone)
  • sub discuss upstream → workflows/discuss/auto/workflow.yaml (stage-master)
  • sub plan upstream → workflows/plan/auto/workflow.yaml (stage-master)
  • sub task upstream → workflows/task/auto/workflow.yaml (stage-master)
  • sub verify upstream → workflows/verify/auto/workflow.yaml (stage-master)
  • sub retro upstream → workflows/retro/workflow.yaml (standalone)

Invocation

• Slash command: /auto <feature description> (bare per ADR 0030 namespace policy D-02 LOCK after harnessed setup)
• 4 stage-master /discuss /plan /task /verify 仍可独立 invoke — /auto 是 opt-in NEW workflow
• --staged opt-in for stage gate UX (每 stage 完停 user review)

How to invoke

!harnessed checkpoint intent auto

The banner above (when present) means this invocation is REGISTERED with the engine (an intent marker) — not yet compliant: steps 2-3 below seed the ledger, and a per-turn <workflow-intent> reminder persists until they run.

The numbered sequence below is the state machine — execute it step by step with Bash. Do NOT improvise an equivalent flow from the Overview above: freelancing bypasses the engine (no per-sub ledger, no evidence guard, no recovery). harnessed is the orchestration brain (harnessed gates says which subs fire, harnessed prompt gives each spawn-ready prompt, harnessed checkpoint records the ledger); YOU spawn with CC-native Task / Agent tools.

Do NOT pipe to harnessed run auto — that is the CI/headless path (in-process SDK spawn that blocks the session, bypasses Agent Teams, and hangs inside Claude Code).

1. FIRST run the discuss stage interactively in THIS session (spawned subagents cannot ask the user questions). Evaluate strategic / phase / subtask clarification criteria for "$ARGUMENTS"; dialogue with the user (AskUserQuestion) for each layer that fires, lock decisions, transparent-skip the rest. After locking blocking decisions, relay the deferrable set to the user in a single batched AskUserQuestion with each agent-recommended default pre-selected — deferrable defers scheduling, not user authority; only skip an item if the user explicitly defers it again. Produce a locked spec.
2. Bash: harnessed gates auto --task "<locked spec>" --skip-sub clarify → parse the JSON {fire, skip, parallelism}. This is the plan SoT (no spawn). Keep the verbatim JSON. For a small self-contained task (single-file / single-page class), the sanctioned lite path is adding --skip-sub verify --skip-sub retro (repeatable / comma-separated) — skipped subs are still recorded in the ledger with reasons; lite ≠ freestyle (the ledger/evidence IS the difference).
3. Bash: harnessed checkpoint start auto --plan '<the verbatim gates JSON from step 2>' → seeds the per-sub ledger so harnessed status --recover can re-orient you after compaction.
4. If parallelism.escalate_to_teams === true: read ~/.claude/rules/agent-teams.md, then drive the fired subs as an Agent Team (TeamCreate → Agent(name, team_name, …) per sub with its harnessed prompt <sub> prompt → coordinate via SendMessage → SendMessage shutdown_request + TeamDelete). Still checkpoint each sub (complete / fail) as below.
5. Otherwise, for each fired sub in order (serial subs sequentially, parallel subs concurrently):
   • If the entry has is_master: true (a stage master — e.g. /auto firing plan/task/verify): do NOT prompt+spawn it. RECURSE: run that master’s own harnessed gates <sub> --task "<spec>" --skip-sub clarify → harnessed checkpoint start <sub> --plan '<json>' → repeat this loop for ITS fired subs.
   • Else (leaf sub): a. Bash: harnessed prompt <sub> --task "<spec>" --json → parse {prompt, max_iterations, model}. b. Spawn a CC-native subagent (Task / Agent tool) with that prompt + model, wrapped in the ralph-loop plugin: /ralph-loop "<prompt>" --max-iterations <max_iterations> --completion-promise "COMPLETE". If the plugin is absent, use the native goal gate instead (Claude Code 2.1.139+ / Codex): /goal "this subtask is delivered: the subagent's final output contains verbatim <promise>COMPLETE</promise>; or stop after <max_iterations> turns" then spawn the subagent and let the goal evaluator drive re-spawns until it clears. If /goal is unavailable too, self-loop: spawn → check output for <promise>COMPLETE</promise> → re-spawn with prior output appended (up to max_iterations). Set the goal only at the leaf subtask level — /goal is single-slot per session and a nested goal overwrites the outer one. c. If the output contains STATUS: NEEDS_CLARIFICATION + questions: STOP, relay them verbatim via AskUserQuestion, append the answers to the spec, then re-spawn the same sub. d. On <promise>COMPLETE</promise>: Bash harnessed checkpoint complete <sub> --summary "<one-line>". The evidence guard runs here (fail-CLOSED): if a declared artifacts_expected file is missing it exits non-zero — re-spawn to produce it, or pass --force only to deliberately override. e. If the sub cannot reach COMPLETE (max_iterations exhausted / unrecoverable error): Bash harnessed checkpoint fail <sub> --summary "<why>", then STOP and report to the user.
6. After all fired subs are done (or recorded failed), Bash harnessed status --recover to confirm the ledger and report a per-sub fired/skipped/done/failed summary to the user. Then run the retro stage to capture lessons.

If you lose context (compaction / resume): run harnessed status --recover first — it reads the ledger and prints "you are here, this is next" so you resume at the first pending sub instead of restarting. If the ledger is empty, re-run steps 2-3.

References

• D-01 master orchestrator delegation pattern
• D-02 bare slash cmd convention (ADR 0030 namespace policy LOCK)
• D-13 declarative SoT (delegates_to[] 声明 + engine consume)
• workflows/{research,retro}/workflow.yaml — 2 standalone (research conditional + retro mandatory)
• workflows/{discuss,plan,task,verify}/auto/workflow.yaml — 4 stage-master Phase 3.5 SHIPPED
• workflows/judgments/stage-routing.yaml — auto-research-unclear trigger (v3.2.0 NEW)
• src/workflow/masterOrchestrator.ts — 'auto' literal + recursive spawn + runAutoPreFlight hook
• CHANGELOG [3.2.0] — complexity gate + research/retro flow + --staged rename