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code-review
Review changed code for reuse, quality, and efficiency, then fix any issues found.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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Review changed code for reuse, quality, and efficiency, then fix any issues found.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
| name | code-review |
| description | Review changed code for reuse, quality, and efficiency, then fix any issues found. |
| context | fork |
| disable-model-invocation | true |
| user-invocable | true |
| model_role | critique |
Review all changed files for reuse, quality, and efficiency. Fix any issues found.
Run git diff (or git diff HEAD if there are staged changes) to see what changed. If there are no git changes, review the most recently modified files that the user mentioned or that you edited earlier in this conversation.
Use the delegate tool to launch all three agents concurrently in a single message. Pass each agent the full diff so it has the complete context.
For each change:
Review the same changes for hacky patterns:
Review the same changes for efficiency:
If $ARGUMENTS is provided, all three agents should also pay special attention to: $ARGUMENTS
Wait for all three agents to complete. Aggregate their findings and fix each issue directly. If a finding is a false positive or not worth addressing, note it and move on — do not argue with the finding, just skip it.
When done, briefly summarize what was fixed (or confirm the code was already clean).
Convene the persona panel on the CURRENT conversation / work-in-progress — the plan, design, or decision you've been building in this session. The INLINE counterpart to /council (which forks and runs isolated, so it cannot see the chat). Use when you want the council to critique what we're working on right now.
Convene the persona panel (six orthogonal review lenses) on a target — cold independent fan-out, debate-to-consensus, synthesized verdict with recorded dissent and a roster manifest.
Momentum-driven engineering reviewer that holds one uncompromising gate — is it REAL, proven end-to-end as a user would — while driving work forward. Demands proof over claims, plumbing before polish, fail-loud over fallbacks, trust in the model over instructions, and protects the critical path so good-but-costly ideas don't stall the work. Warm, blunt, forward-driving — not a curmudgeon. A lens for any checkpoint — brainstorm, design, plan, implement, debug, or ship — not just the finish. Use when: pressure-testing whether an idea/design/plan is provable and on the critical path, whether you're building in the right order, whether a fix is real or a band-aid, or whether work is actually done/ready — any time the worry is "are we fooling ourselves about what's real?"
Use when building an Amplifier-powered workflow or automation tool and deciding how to expose it — as standalone .dot attractor pipelines (incl. inside the Resolve dot-graph resolver), an importable Python lib, agent-callable tool modules, or a CLI. Covers the four leverage levels, the DRY rule that keeps logic in ONE home, the judgment for which levels a real consumer actually needs (and when adding a level is just ceremony), and the maximally-DRY attractor-only specialization where the .dot pipeline is the sole logic home.
Hard-won patterns for probing, building, troubleshooting, and iterating against Microsoft Graph API endpoints -- especially from a browser SPA using delegated MSAL.js auth calling Graph directly with no backend (lessons generalize to any Graph integration). Covers the throwaway-probe-file methodology for de-risking before building, OData/query quirks, permission and admin-consent sequencing, recordings/transcripts access patterns (SharePoint REST, not Graph), CSP requirements for a pure-browser SPA, retry/pagination/backoff patterns, and the MSAL/EasyAuth auth-redirect-loop debugging saga. Use when integrating with Microsoft Graph, Teams APIs, MSAL.js, or EasyAuth; when hitting an unexpected Graph error (400/403/429), a silent missing-scope failure, an auth redirect loop, or a CSP violation that only appears in production; or when deciding how to validate a new Graph capability before committing it to a codebase.
Analyze images using LLM vision APIs (Anthropic Claude, OpenAI GPT-4, Google Gemini, Azure OpenAI). Use when tasks require: (1) Understanding image content, (2) Describing visual elements, (3) Answering questions about images, (4) Comparing images, (5) Extracting text from images (OCR). Provides ready-to-use scripts - no custom code needed for simple cases.