一键导入
perf-review
Review code for performance issues — complexity, batching, caching, memory, and concurrency.
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。
菜单
Review code for performance issues — complexity, batching, caching, memory, and concurrency.
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
Ruthlessly tear apart code design — naming, abstractions, coupling, complexity, and everything else.
Generate Mermaid diagrams in the README to visualise architecture, flows, or relationships from the codebase.
Analyse test coverage gaps and report uncovered code before making changes.
Run tests, fix failures, and re-run until the suite passes.
Write and improve tests — reuse existing patterns, ensure consistency, and maintain quality.
Isolate a function or code block into a self-contained, runnable script for study and manual testing.
| name | perf-review |
| description | Review code for performance issues — complexity, batching, caching, memory, and concurrency. |
| argument-hint | <files> [instructions] |
| user-invocable | true |
| disable-model-invocation | true |
| context | fork |
| agent | Explore |
| allowed-tools | Read, Grep, Glob |
Analyse the specified files for performance issues and provide actionable improvement suggestions.
Files and instructions: $ARGUMENTS
The arguments are free-form and flexible. They may contain:
@file.ts, file.py, main.go, utils.go, script.sh handler.rbParse the arguments to identify which files to review and what additional instructions apply. When additional instructions reference related files (e.g. imports, dependents), follow those instructions to identify and review those files as well.
/perf-review @service.ts @handler.ts — review these two files/perf-review utils.py, helpers.py — comma-separated, no @ prefix/perf-review @app.go and also review the files it imports — review with extended scope/perf-review @api.ts focus only on database query performance — targeted review/perf-review @processor.rb we're seeing high memory usage in production — review with context