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grahama1970/review-agent

以只读、缺陷优先的方式审查指定的代码变更,并返回所有可操作的问题。适用于其他代理委托审查未提交的变更、基础分支差异、一次提交或自定义审查指令时。

review-agent 是什么?

Review Agent 是一个专业的只读代码审查工具,专为 AI 代理或开发者设计,用于审查代码变更并返回每个有实际修复价值的问题。其核心原则是“缺陷优先”——只报告作者在知情后很可能修复的回归问题。该工具支持审查未提交的变更、基础分支差异、一次提交以及自定义审查指令,且不修改任何文件、不创建提交、不推送分支或发布评论。工作流程包括读取项目特定的 AGENTS.md 指令、检查完整差异及其上下文、在发现首个问题后继续遍历整个差异,并验证相关测试和调用点以确认每个问题是真实且可操作的。最终结果按严重性(P0 到 P3)排序呈现,每个问题包含标题、文件路径和行号、简短解释,并附上整体评估和任何重要的测试覆盖缺口。该工具可在 Claude Code、Cursor、Codex 等执行 shell 命令的代理平台上使用。

兼容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/grahama1970/agent-skills/tree/main/skills/.system/review-agent

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Review Agent

直接检查请求的目标,并返回每个作者可能会修复的问题。 请勿修改文件、创建提交、推送分支、发布审查评论或将审查委托给其他代理。

审查变更

  1. 阅读适用的 AGENTS.md 指令。
  2. 检查请求目标的完整差异以及足够的周边代码,以理解每个变更路径。
  3. 识别变更引入的具体回归问题。继续检查整个差异,即使已发现第一个问题。
  4. 检查相关的测试和调用点,以确认每个问题是真实且可操作的。

对于基础分支审查,比较实际会合并的变更,而不是直接与分支顶端对比差异。将比较引用解析为分支的上游(当该上游存在且领先于本地分支时);否则使用本地分支。运行 git merge-base HEAD <comparison-ref>,然后检查 git diff <merge-base-sha>。如果无法解析本地分支,请在其配置的上游中显式尝试,然后再报告目标不可用。

仅在以下所有条件成立时标记问题:

  • 问题在正确性、安全性、性能或可维护性方面具有显著影响。
  • 问题是离散且可操作的。
  • 问题是由被审查的变更引入的。
  • 受影响场景或调用路径可以从代码中演示。
  • 如果作者知道该问题,很可能会修复它。

不要标记推测性问题、预先存在的问题、有意的行为变更或不影响代码的格式风格问题。

编写结果

首先呈现问题,按严重性排序。每个问题使用以下格式的一个条目:

[P1] 强制性问题标题 — path/to/file.rs:line

在标题后跟一个简短的段落,解释受影响的情景以及行为错误的原因。保持引用的范围尽可能小,并确保它与审查的差异重叠。

使用以下优先级:

  • P0:普遍性发布阻断或严重失败。
  • P1:应立即修复的紧急缺陷。
  • P2:应修复的普通缺陷。
  • P3:仍值得修复的低影响问题。

如果没有符合条件的问题,则输出 No findings. 不要为了填充结果而编造问题。在问题之后,添加一个简短的总体评估,并提及任何重要的测试缺口或残留风险。

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

grahama1970/acceptance-contract

Turn a client brief, zip bundle, directory, or single requirements file into a typed acceptance-contract bundle with extracted requirements, acceptance checks, open questions, an immutable-goal draft, and a create-report-backed decision report. Use when users say acceptance contract, brief to requirements, freeze the goal, create immutable goal, amend immutable goal, build a Battle requirements bundle, or extract requirements from this bundle.

grahama1970/agent-ecosystem

Canonical map and shared contracts for the agent-governance ecosystem: the pi.receipt_envelope.v1 boundary envelope, the component graph, and the rules for which component owns which schema. Use when wiring a skill or extension into the shared receipt world, when asking how shame, triage-error, tau, ask, project-watchdog, ops-herdr, ponytail, and Memory fit together, or when validating an envelope.

grahama1970/agentic-evals

Agentic evaluation of skills using multi-trial fixtures, deterministic command assertions, trajectory checks, safety constraints, and evidence-backed readiness scoring. Use when users ask for agentic evals, multi-trial skill evaluation, skill trajectory validation, or readiness scoring for a skill workflow.

grahama1970/agent-inbox

File-based inter-agent messaging with headless dispatch. Check inbox, send bugs/requests to other projects, automatically spawn headless agents to fix bugs, and track progress via task-monitor.

grahama1970/agents-registry

Generate and query the centralized agent identity registry. Scans .pi/agents/*/AGENTS.md, parses frontmatter, outputs agents-registry.json and optionally syncs to /memory for semantic search.

grahama1970/agent-status

Artifact-driven status surfaces for long-running project-agent work. Maintains status.json, events.jsonl, proof manifests, and a stale-aware STATUS.html so humans can tell where the agent is, what passed, what is still unproven, and what decision or action is next — without dashboard theater.

grahama1970/align

Round-based context alignment before execution. Use when the human, project agent, WebGPT, scillm, ask, dogpile, memory, or project-knowledge may each hold different facts about a task; especially before ambiguous design, infographic, product workflow, high-stakes implementation, plan-iterate, project-infographic, or multi-review work.

grahama1970/analytics

Flexible data science analytics for any dataset. Auto-discovers schema, recommends charts, exports to create-figure. Works with JSONL, JSON, CSV from any source.

grahama1970/analyze-chatterbox-emotions

Evaluate generated Chatterbox voice files as voice-quality artifacts: affect match, arousal/valence proxies, pause placement, intelligibility inputs, clipping, loudness, and discontinuity flags. Use when reviewing Chatterbox emotional tags, pauses, Turbo/base affect delivery, Persona Dream utterance renders, or whether generated speech matches an intended product-facing affect.

grahama1970/analyze-elf

Reverse-engineer features from ELF binaries. Extracts CLI commands, state machines, protocols, Zod schemas, and data models. Automatically generates a /create-walkthrough prosecution brief with Mermaid diagrams. Uses /treesitter for AST analysis of bundled JS/TS source.

grahama1970/animation-vocabulary

Reverse-lookup glossary that turns a vague description of a web animation or motion effect into its exact term ("the bouncy thing when a popover opens" → Pop in; "the iOS rubber-band scroll" → Rubber-banding). Use when the user asks "what's it called when…", or describes a motion effect without knowing its name and wants the right word to prompt an AI or designer with. For naming an effect, not designing or building one.

grahama1970/anonymize-data

Anonymize supported CSV, JSON, UTF-8 text, and SQLite files using an explicit policy through the oai-trial project. Use for anonymize data, pseudonymize exports, redact policy literals, or discover and explicitly approve fuzzy name aliases. The skill is a thin CLI/Docker interface, not another engine.

grahama1970/anvil

Heavy-duty "No-Vibes" debugging and hardening orchestrator. Use this for complex, stubborn bugs where `review-code` has failed, or for "Red Teaming" (hardening) a codebase. Runs multiple agents in parallel (Thunderdome) using git worktree isolation.

grahama1970/apple-design

Apple's approach to interface design and fluid, physical motion, translated for the web. Use when building or reviewing gesture-driven UI, spring animations, drag/swipe/sheet interactions, momentum and interruptible transitions, translucent materials and depth, typography (optical sizing, tracking, leading), reduced-motion, or the design foundations (feedback, spatial consistency, restraint) behind Apple-style interfaces.

grahama1970/argue

Multi-persona structured debate orchestrator. Personas research via /dogpile, consult colleagues via /ask, and argue toward nuanced synthesis on complex questions.

grahama1970/arxiv

Search arXiv for papers and extract knowledge into memory. Use `search` to find papers, `learn` to extract knowledge.

grahama1970/ask

Use when the user asks to query project memory, ask an oracle, use supported browser-backed reviewers, run Tau roundtable/single-handler workflows, ask Pi-native subagents from within Pi, run persona/deep-review workflows, generate image prompts, check OS/project health through composed skills, or run an ask DAG. This skill is the executable /ask runtime; do not replace it with an informal subagent, plain web search, or hand-written review; inside Pi, explicit Pi-native subagent targets are routed through the pi-subagents tool as an Ask target type.

grahama1970/assess

Step back and critically reassess project state. Use when asked to "assess", "step back", "fresh eyes", "check alignment", "sanity check", "health check", "prune documentation", or "evaluate what's working". Offers documentation pruning and doc-code alignment analysis. Offer to run after major changes (don't auto-run).

grahama1970/assistant

Shared GPT + classifier inference gateway for persona monitor tasks. Routes validation and classification through a 4-tier cascade: heuristic → classifier → local GPT → scillm.

grahama1970/assistant-lab

Self-improvement workbench for /assistant. All the tools needed to diagnose, train, evaluate, and promote models in a continuous loop. The "warm pond" where /assistant evolves its own inference stack.

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