Structured code review through the shared OpenClaw agent-skills installation.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Use the canonical autoreview skill.
Read the complete installed ~/.agents/skills/autoreview/SKILL.md and the references
it requires before reviewing. If installed at another operator-selected location,
use that installation. This repository entrypoint contains no review implementation.
If the skill is missing, reuse or clone https://github.com/openclaw/agent-skills
and run python3 scripts/install-skills autoreview from that checkout. On Windows,
use python; --mode copy is available when symlinks are unavailable.
Run the shared scripts/autoreview helper with Python from the repository being
reviewed, preserving its working directory. Follow this repository's review
requirements in addition to the canonical skill.
Keep shared fixes and instructions upstream in openclaw/agent-skills. Update
the shared checkout once for all symlinked consumers; reinstall copy-mode skills
after updating their source. Do not add repository-local helpers or test copies.
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Assess designs, public interfaces, state ownership, readability, and refactor proposals when the task calls for design review or structural simplification; not for unrelated routine edits.
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Clean only the current Enterprise diff before autoreview, preserving behavior, security boundaries, required TODOs, and useful test-intent comments.
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Select proportional Enterprise validation and diagnose exact CI runs, distinguishing product, harness, infrastructure, and credential failures.
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Develop OpenClaw Enterprise changes with proportional verification and source-backed flow documentation for non-trivial behavior.
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Create or refine readable Mermaid diagrams for changes, architecture, lifecycles, and dependencies in Markdown documents and PR descriptions.
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Run named OpenClaw Enterprise end-to-end integration scenarios against real supported installations. Use only when explicitly invoked.
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