openclaw-maintainer（OpenClaw maintainer 搜索）

OpenClaw Maintainer

PR review + prep + merge automation with selective parallelism. Uses role-specific models:

• opus (anthropic/claude-opus-4-6): orchestration requiring parallel sub-subagent fanout (reviewpr only)
• gpt (openai-codex/gpt-5.3-codex, 60 TPS): serial workflows (preparepr, mergepr), sub-subagent code execution, GitHub searches, CI checks, and cleanup actions

maxSpawnDepth Requirement

This skill requires maxSpawnDepth >= 2 in the OpenClaw session configuration. Depth 2 is used in two places:

1. /reviewpr fanout, 2-3 parallel read-only sub-subagents
2. /mergepr one cleanup sub-subagent after merge

/preparepr is intentionally single-agent and fully serial.

If maxSpawnDepth is less than 2, review fanout and merge cleanup delegation cannot run. The parent subagent can still execute those tasks serially, but it loses the fresh-context parallel read benefit in review.

SAFETY: NEVER PUSH TO MAIN

Subagents have full disk access. The one inviolable rule:

• NEVER force-push, push, or directly commit to main or origin/main.
• All pushes go to PR head branches only.
• The only way code reaches main is through gh pr merge --squash.
• If gates (lint/build/test) have not passed, do NOT merge.

LOCAL-ONLY FILES RULE

Non-source files (testing checklists, notes, TODOs, temp files, debug outputs, scratch plans) must NEVER be committed to PR branches. They go in .local/ which is gitignored. The preparepr command enforces this automatically, but all commands should be aware of it.

Token Optimization

Build, test, and lint commands are run as background processes to avoid burning tokens watching real-time output. Only read output on failure.

• Tests: sleep 60 then poll every 10s. Average runtime ~60s.
• Build: sleep 40 then poll every 10s. Average runtime ~30-40s.
• Lint: fast enough to run inline.

This pattern is enforced in preparepr.md. See the "Token-Optimized Command Execution" section in preparepr.md for the exact commands.

Changelog Awareness

Before committing prep fixes, the workflow checks the project changelog:

• Determines the latest release version via gh release list --limit 1 --exclude-drafts --exclude-pre-releases
• Reads CHANGELOG.md and adds entries under an "Unreleased" section
• Never puts entries into already-released version sections
• Summarizes what the PR does concisely

Docs Awareness

After applying code fixes, the workflow scans for user-facing changes (new parameters, changed behavior, new config options, removed features). If found, relevant docs are updated. Internal-only changes skip this step.

Test Quality Enforcement

During review (/reviewpr), test code is specifically analyzed for:

• sleep, setTimeout, artificial delays, or polling patterns in tests (should use fake timers)
• Missing test coverage for new functionality
• Tests reimplementing the function they test
• Test isolation issues (reading/writing real user directories)

GitHub Identity Awareness

Before commenting on any issue or PR, the workflow checks gh api user to determine the logged-in GitHub username. If the current user is also the PR/issue author, comments use self-referential language ("Closing this out, superseded by #X") instead of thanking yourself. For other people's contributions, polite acknowledgment is used. This applies across mergepr and any cleanup sub-subagents that comment on issues or close PRs.

.local/ Artifacts (Cross-Step Coordination)

The .local/ folder inside the worktree is the thread that connects parallel work across steps:

• /reviewpr saves both review.md and related.md
• /preparepr reads review.md and related.md, writes prep.md
• /mergepr reads all three to verify state and drive post-merge cleanup

Command Files

The actual command files live in this skill's commands/ folder. Subagents read these directly (they do NOT read this SKILL.md file).

• commands/reviewpr.md - review only (parallelized: code analysis, CI/related scan, test coverage)
• commands/preparepr.md - single-agent serial prep: install deps, scan lint/fixes, apply fixes, rebase, changelog/docs, gates, push (do NOT merge)
• commands/mergepr.md - merge only (serial merge, then single cleanup sub-subagent)

Workflow Overview (3 step)

1. User: "review PR #2403"
2. Main agent: spawns opus subagent (high thinking) via sessions_spawn. Subagent reads commands/reviewpr.md and executes.
3. Opus subagent: sets up worktree, spawns 3 parallel sub-subagents (code analysis, CI/related scan, test coverage), combines results into structured review, saves .local/review.md and .local/related.md, pings back findings.
4. Main agent: summarizes for user (ready for prep, needs work, concerns)
5. User: "ok prep it" / "fix X first" / "don't merge"
6. Main agent: if approved, spawns gpt subagent (xhigh thinking) via sessions_spawn. Subagent reads commands/preparepr.md.
7. GPT subagent: runs /preparepr fully serially in one agent, installs dependencies, scans lint and review concerns, applies fixes, rebases, updates changelog/docs when needed, runs token-optimized gates, pushes, verifies push. Saves .local/prep.md.
8. User: "merge it"
9. Main agent: spawns gpt subagent (xhigh thinking) via sessions_spawn. Subagent reads commands/mergepr.md.
10. GPT subagent: verifies state, checks GitHub identity, merges via gh pr merge --squash, then spawns a single cleanup sub-subagent (close superseded PRs, close related issues, clean worktree). Pings back merge SHA.
11. Main agent: confirms to user with merge SHA

ALWAYS USE SUBAGENT

Review, prep, and merge are long running tasks. NEVER run in the main thread. Always use sessions_spawn to create a subagent.

Model

Use different models by role:

• /reviewpr uses model:opus (orchestrates 2-3 parallel sub-subagents)
• /preparepr uses model:gpt thinking:xhigh (fully serial, no sub-subagents)
• /mergepr uses model:gpt thinking:xhigh (serial merge + one cleanup sub-subagent)

If a model is not available, fall back to session default model.

Review Workflow (/reviewpr)

sessions_spawn task:"Review PR #<number> in openclaw repo. Read commands/reviewpr.md and follow its instructions exactly." model:opus thinking:high runTimeoutSeconds:0 label:"pr-<number>-review"

Use opus for the top-level review orchestrator. Sub-subagents inside reviewpr.md stay on gpt as defined in the command file.

Prep Workflow (/preparepr)

sessions_spawn task:"Prepare PR #<number> in openclaw repo. Read commands/preparepr.md and follow its instructions exactly." model:gpt thinking:xhigh runTimeoutSeconds:0 label:"pr-<number>-prep"

Merge Workflow (/mergepr)

sessions_spawn task:"Merge PR #<number> in openclaw repo. Read commands/mergepr.md and follow its instructions exactly." model:gpt thinking:xhigh runTimeoutSeconds:0 label:"pr-<number>-merge"

Parallelism Design

Each command file documents when and how to spawn sub-subagents, including which model to use. All command files include the EXACT sessions_spawn call syntax so sub-subagents know precisely what to invoke.

/reviewpr parallelism

2-3 parallel read-only sub-subagents after worktree setup (default is 3):

• Subagent A (Code Analysis) model:gpt thinking:xhigh: reads diff, analyzes quality, correctness, edge cases, security
• Subagent B (CI & Related Scan) model:gpt: checks CI status, searches for related issues/PRs, scans for duplicates
• Subagent C (Test Coverage) model:gpt thinking:xhigh: checks test coverage gaps, test quality (flags sleep/setTimeout/polling), docs, changelog
• Then the parent (opus) combines all results into one structured review

/preparepr execution model (gpt, xhigh thinking)

Fully serial in a single parent subagent, no parallel scan fanout:

• Install + Scan Phase: parent installs dependencies, runs lint and review-driven fix identification inline
• Fix Phase: parent applies blockers/importants, handles local files, updates code/tests
• Rebase + Docs/Changelog Phase: parent rebases onto latest main once, then updates docs/changelog if user-facing
• Gates + Push Phase: run lint inline, build/test token-optimized in background, push and verify

Why this is serial now: prep scan fanout only saved a couple minutes but added orchestration complexity. Fewer moving parts means fewer announce chains to break, and fresh parallel context is better spent on review where readers are truly independent.

/mergepr execution model (gpt, xhigh thinking)

Serial merge (parent, gpt) with identity-aware commenting, then single cleanup sub-subagent:

• Serial Phase: verify state, check GitHub identity, merge via gh pr merge --squash, post identity-aware comment
• Single Cleanup Sub-subagent model:gpt: close superseded PRs (identity-aware), close related issues (identity-aware), clean worktree and branches. All done serially in one agent since it's ~5 API calls total.

Important Notes

• Subagents read the command file directly, they do NOT read this SKILL.md
• Each command file is self-contained with all setup, steps, and safety rules
• Sub-subagents spawned by command subagents are always read-only or cleanup-only
• If checks or gates fail, report failure and stop, do not force merge
• If merge fails, report and do NOT retry in a loop
• PR must end in MERGED state, never CLOSED
• Code only reaches main through gh pr merge --squash, never through direct push
• Token optimization: build/test run in background, only read output on failure
• Changelog is updated during prep if the PR has user-facing changes
• GitHub identity is checked before any PR/issue comments to avoid self-thanking
• maxSpawnDepth must be >= 2 for parallel sub-subagents to work