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Agent Skill · 研究检索

compound-eng-code-review(复合工程代码审查)

compound-eng-code-review 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

compound-eng-code-review 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为需要联网;许可证为 MIT-0,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 13,815。

总安装 9,072周安装 378GitHub Stars 公开资料未说明下载 3,024核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
3.0.5

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:compound-eng-code-review(复合工程代码审查)
来源仓库:https://github.com/iliaal/compound-eng-code-review
安装命令:
openclaw skills install compound-eng-code-review
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install compound-eng-code-review

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

compound-eng-code-review 提供结构化代码审查流程,按严重程度排序问题并支持多代理协同分析。适用于 OpenClaw 中执行代码质量审核、PR 评审或系统性技术债务排查。支持深度模式运行,可识别潜在漏洞、性能瓶颈与可维护性问题。安装需确认项目代码库访问权限,建议在非生产分支先行验证审查结果。

涉及自动修复建议时,应人工复核变更影响,避免引入新风险。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install compound-eng-code-review;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

compound-eng-code-review 怎么安装?

在终端执行 openclaw skills install compound-eng-code-review。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

compound-eng-code-review 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

compound-eng-code-review 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

compound-eng-code-review 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

compound-eng-code-review 用的人多吗?

公开数据是累计安装 9,072 次,近一周安装 378 次,下载 3,024 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

compound-eng-code-review 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。

先想清楚的地方

  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
compound-eng-code-review

复合工程代码审查 · 本页

9,072—需要联网MIT-0有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
ia-code-review
class
discipline
description
>-

Code Review

Two-Stage Review

Stage 1 -- Spec compliance (do this FIRST): verify the changes implement what was intended. Check against the PR description, issue, or task spec. Identify missing requirements, unnecessary additions, and interpretation gaps. If the implementation is wrong, stop here -- reviewing code quality on the wrong feature wastes effort.

Stage 2 -- Code quality: only after Stage 1 passes, review for correctness, maintainability, security, and performance.

Scope Resolution

Pre-flight: verify git rev-parse --git-dir exists before anything else. If not in a git repo, ask for explicit file paths.

When no specific files are given, resolve scope via this fallback chain:

  1. User-specified files/directories (explicit request)
  2. Session-modified files (git diff --name-only for unstaged + staged)
  3. All uncommitted files (git diff --name-only HEAD)
  4. Untracked files (git ls-files --others --exclude-standard) -- new files are often most review-worthy
  5. Zero files → stop. Ask what to review.

Exclude: lockfiles, minified/bundled output, vendored/generated code.

Review Mode Selection

Run this BEFORE reading the full diff. Use metadata only (git diff --stat, file list from scope resolution) to count signals. Reading the diff first creates analysis momentum that bypasses mode selection.

SignalThresholdDetect from
Lines changed>300git diff --stat insertion + deletion totals, excluding test files
Files touched>8File count from scope resolution, excluding test files
Modules/directories spanned>3Unique top-level directories from non-test file list
Security-sensitive files (auth, crypto, payments, permissions)anyFile path matching
Database migrations presentanyFile path matching
API surface changes (public endpoints, exported interfaces)anyFile path matching

Test file exclusion: test files inflate complexity signals without adding review risk -- they're boilerplate-heavy and follow repetitive patterns. Exclude paths matching tests/, test/, __tests__/, *.test.*, *.spec.*, *_test.* from the lines, files, and directories signals. Use git diff --stat -- ':!tests/' ':!test/' ':!__tests__/' ':!*.test.*' ':!*.spec.*' ':!*_test.*' for the filtered count. Report both totals for transparency: "450 lines changed (280 excluding tests)."

3+ signals → deep review. Inform the user, then dispatch parallel specialist agents per deep-review.md. Pass the diff to agents -- do NOT read it first. Reading and analyzing the diff yourself before dispatching agents defeats the purpose of deep review. Stop here -- do not proceed to the Review Process section.

2 signals → suggest: "This touches N files across M modules. Deep review? (y/n)"

0-1 signals → standard review. Proceed to Review Process below.

Before auto-switching to deep review, check the exceptions list in deep-review.md -- certain change types (pure docs, mechanical refactors, single-file <50 lines) override signal count.

Override: deep forces multi-agent, quick forces single-pass.

Review Process

Standard reviews only. If mode selection triggered deep review, specialist agents handle the review per deep-review.md -- do not run these steps yourself.

  1. Context — do these before reading code:

- Scope Drift Check: compare git diff --stat against the PR's stated intent. Classify as CLEAN / DRIFT DETECTED / REQUIREMENTS MISSING. If DRIFT, note the drifted files and ask the author: ship as-is, split, or remove unrelated changes? - Read the intent: PR description, linked issue, or task spec. If the code does something the intent doesn't describe, or fails to do something the intent promises, flag as a finding — correct code that solves the wrong problem is still wrong. - Fetch existing discussions (when present): project reviews and comments to a presence flag first to avoid spawning empty work. gh pr view <pr> --json reviews,comments --jq '(((.reviews // []) | map(select(.state != "APPROVED" or .body != "")) | length) > 0) or (((.comments // []) | length) > 0)' returns true only when at least one substantive review or issue comment exists (approval-only clicks excluded; null-defensive on PRs with no review array). On false, skip the prior-comments pass entirely. On true, fetch the bodies via gh api repos/{owner}/{repo}/pulls/{pr}/comments and reconcile before raising findings — prior reviewers may have already resolved issues you'd otherwise re-raise. - Run automated gates: execute the project's test/lint suite if available (check CI config for the canonical commands) to catch failures before manual review.

  1. Structural scan -- architecture, file organization, API surface changes. Flag breaking changes. For files marked as added (A) in the diff, use the diff content directly -- don't attempt to read them from the working tree when reviewing a remote branch.
  2. Line-by-line -- correctness, edge cases, error handling, naming, readability. Use question-based feedback ("What happens if input is empty here?") instead of declarative statements to encourage author thinking.
  3. Security -- input validation, auth checks, secrets exposure, injection vectors (SQL, XSS, CSRF, SSRF, command, path traversal, unsafe deserialization). Flag race conditions (TOCTOU, check-then-act). Use security-patterns.md for grep-able detection patterns across 11 vulnerability classes.
  4. Test coverage -- verify new code paths have tests. Flag untested error paths, edge cases, and behavioral changes without corresponding test updates. Flag tests coupled to implementation details (mocking internals, testing private methods) -- test behavior, not wiring.
  5. Reliability -- error handling completeness, timeout/retry logic, resource cleanup on error paths, graceful degradation. Use reliability-patterns.md for detection patterns and grep-able signals.
  6. Removal candidates -- identify dead code, unused imports, feature-flagged code that can be cleaned up. Distinguish safe-to-delete (no references) from defer-with-plan (needs migration).
  7. Verify -- run formatter/lint/tests on touched files. State what was skipped and why. If code changes affect features described in README/ARCHITECTURE/CONTRIBUTING, note doc staleness as informational.
  8. Summary -- present findings grouped by severity with verdict: Ready to merge / Ready with fixes / Not ready.

Large diffs (>500 lines): Review by module/directory rather than file-by-file. Summarize each module's changes first, then drill into high-risk areas. Flag if the PR should be split.

Change sizing: Ideal PRs are ~100-300 lines of meaningful changes (excluding generated code, lockfiles, snapshots). PRs beyond this range have slower review cycles and higher defect rates. When a PR exceeds this, suggest splitting using one of these strategies: (a) Stack -- sequential PRs where each builds on the previous, merged in order; (b) By file group -- group related files (e.g., model + migration + tests) into separate PRs; (c) Horizontal -- split by layer (frontend, API, database); (d) Vertical -- split by feature slice (each PR delivers one user-visible behavior end-to-end).

Severity and Confidence

Four severity tiers (Critical / Important / Medium / Minor) and a 5-band confidence rubric (0.0-1.0 → Report / Report-if-actionable / Suppress) govern what lands in the report. Full rules, false-positive suppression categories, and the LLM-specific prompt-injection exception in severity-and-confidence.md.

Tie every finding to concrete code evidence (file path, line number, specific pattern). Never fabricate references.

What to Check

For category checklists (Correctness, Maintainability & Readability, Performance, Adversarial red-team pass, AI-generated code lens), load check-categories.md. It's the structured checklist for the line-by-line review step.

Language-specific checks live in language-profiles.md — load the profile matching the file extensions in the diff (TypeScript/React, Python, PHP, Shell/CI, Configuration, Data Formats, Security, LLM Trust Boundaries).

Action Routing

For every finding, classify the fix into one of four tiers: safe_auto / gated_auto / manual / advisory. Full decision rules and conflict-resolution policy in action-routing.md. When in doubt, escalate to gated_auto — never promote toward safe_auto on disagreement.

Comment Labels

Prefix inline review comments so authors know what requires action:

  • *(no prefix)* -- required change (maps to Critical or Important severity), blocks merge
  • Nit: -- style preference, optional
  • Consider: -- suggestion worth evaluating, not blocking
  • FYI: -- informational, no action expected

Anti-Patterns in Reviews

  • Nitpicking style when linters exist -- defer to automated tools instead
  • "While you're at it..." scope creep -- open a separate issue instead
  • Blocking on personal preference -- approve with a Minor comment instead
  • Rubber-stamping without reading -- always verify at least Stage 1
  • Reviewing code quality before verifying spec compliance -- do Stage 1 first
  • Recommending fix patterns without checking currency -- verify the pattern is current for the project's framework version before suggesting it. Prefer built-in alternatives from newer versions
  • Fighting documented overrides -- if the project's CLAUDE.md, AGENTS.md, or an inline comment documents a deliberate bypass of a general rule (e.g., "we intentionally allow X because Y"), honor it. Do not re-raise the underlying concern; do not "just to be safe" around it. If the override lacks rationale, suggest documenting the reason — don't argue the rule.

When to Stop and Ask

  • Fixing the issues would require an API redesign beyond the PR's scope
  • Intent behind a change is ambiguous -- ask rather than assume
  • Missing validation tooling (no linter, no tests) -- flag the gap, don't guess

Output Format

## Review: [brief title]

### Critical
- **CR-001.** [file:line] `quoted code` -- [issue]. Score: [0.0-1.0]. [What happens if not fixed]. Fix: [concrete suggestion].

### Important
- **CR-002.** [file:line] `quoted code` -- [issue]. Score: [0.0-1.0]. [Why it matters]. Consider: [alternative approach].

### Medium
- **CR-003.** [file:line] -- [issue]. Score: [0.0-1.0]. [Why it matters].

### Minor
- **CR-004.** [file:line] -- [observation].

### What's Working Well
- [specific positive observation with why it's good]

### Residual Risks
- [unresolved assumptions, areas not fully covered, open questions]

### Verdict
Ready to merge / Ready with fixes / Not ready -- [one-sentence rationale]

Number findings as CR-001, CR-002... sequentially across all severity levels so they can be referenced by ID in discussions, PR comments, and follow-up todos. Limit to 10 findings per severity. If more exist, note the count and show the highest-impact ones.

For multi-agent consolidation (deep review, parallel specialists), apply the merge algorithm in deep-review.md — it handles same-line dedupe, conflicting severity, NEEDS DECISION flagging, and cross-lens confidence boosting.

Clean review (no findings): If the code is solid, say so explicitly. Summarize what was checked and why no issues were found. A clean review is a valid outcome, not an indication of insufficient effort.

References

DocumentWhen to loadWhat it covers
security-patterns.mdSecurity review step or deep review security agentGrep-able detection patterns across 11 vulnerability classes
security-test-coverage.mdFull security audit deliverable (used by ia-security-sentinel agent)Auth edge cases, authorization, input boundary, concurrency, session hygiene, output boundary checklist
language-profiles.mdLanguage-specific checks stepTypeScript/React, Python, PHP, Shell/CI, Config, Security, LLM Trust
deep-review.mdWhen mode selection triggers deep reviewSpecialist agents, prompt template, merge algorithm, model selection
review-traps-catalog.mdStarting any non-trivial review; writing findings with "should"/"could"/"what if"Reachability-before-severity, docs-idiom smoke test, convention-from-3-files, speculative future-design, paired-enum drift, cross-repo contract staleness, language-version gotchas
check-categories.mdLine-by-line step, structuring the readCorrectness, Maintainability, Performance, Adversarial, AI-generated-code lens
action-routing.mdClassifying fix-application for each finding4-tier split (safe_auto / gated_auto / manual / advisory), conflict resolution
severity-and-confidence.mdClassifying severity + confidence for each findingCritical/Important/Medium/Minor tiers, 5-band confidence rubric, FP suppression categories
false-positive-suppression.mdDetailed FP categories with framework-idiom and test-specific examplesLinked from severity-and-confidence; covers overridable patterns

Integration

  • ia-receiving-code-review -- the inbound side (processing review feedback received from others). Action-routing terminology maps across: safe_auto ≈ AUTO-FIX, gated_auto ≈ ESCALATE-for-approval, manual ≈ ESCALATE, advisory ≈ FYI (no-op).
  • ia-kieran-reviewer agent -- persona-driven Python/TypeScript deep quality review (type safety, naming, modern patterns)
  • /ia-review -- full ceremony review (worktrees, ultra-thinking, multi-agent). Deep review is lighter: no worktrees, no plan verification, just parallel specialist agents on the same diff.
  • /resolve-pr-parallel command -- batch-resolve PR comments with parallel agents
  • ia-security-sentinel agent -- deep security audit beyond the security step in this skill. Also supports threat-model mode for architectural security analysis when the diff introduces new trust boundaries, auth flows, or external API surfaces.

平台分布

OpenClaw

89.13%
按下载量换算2,695

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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