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grahama1970/review-agent

對指定的程式碼變更執行唯讀、以缺陷為優先的審查,並回傳每個可採取行動的發現。當另一個代理委託審查未提交的變更、基底分支差異、提交或自訂審查指示時使用。

review-agent 是什麼?

此技能(skill)是一個專為 AI 代理(如 Claude Code、Cursor 或 Codex)設計的程式碼審查工具。它被命名為「審查代理」,其核心功能是以唯讀且缺陷優先的方式,對指定的程式碼變更執行深度檢查,並回傳所有作者可能會修復的、可採取行動的發現。技能明確禁止對檔案進行修改、建立提交、推送分支或發布審查意見,確保審查過程的純粹性與安全性。支援的審查範圍包括未提交的變更、基底分支差異(base-branch diff)、特定的提交(commit)以及自訂的審查指示。在執行時,它會先讀取專案的 `AGENTS.md` 指示,接著檢查完整的差異並結合足夠的上下文程式碼,以識別由該變更引入的實際回歸問題。技能還提供了嚴格的發現標準與輕重緩急分級(P0 至 P3),以確保只回報真正有意義、可示範且作者會想要修復的問題。最終輸出會按嚴重程度排序,並附上簡短的整體評估,同時指出測試上的缺口或殘餘風險。

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

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說明文件

審查代理

直接檢查請求的目標,並回傳所有作者可能會修復的發現。 請勿修改檔案、建立提交、推送分支、發布審查意見,或將審查委託給另一個代理。

審查變更

  1. 讀取適用的 AGENTS.md 指示。
  2. 檢查請求目標的完整差異,以及足夠的上下文程式碼,以了解每個變更的路徑。
  3. 識別由變更引入的具體回歸問題。即使找到第一個問題後,也要繼續檢查整個差異。
  4. 檢查相關的測試和呼叫點,以確認每個發現都是真實的且可採取行動的。

對於基底分支審查,請比較實際上會合併的變更,而不是直接與分支尖端進行差異比對。當分支的上游存在且領先於本地分支時,請將比較參考解析到分支的上游;否則使用本地分支。執行 git merge-base HEAD <comparison-ref>,然後檢查 git diff <merge-base-sha>。如果本地分支無法解析,請嘗試其明確配置的上游,然後再報告目標不可用。

僅在滿足以下所有條件時才標記問題:

  • 它以有意義的方式影響正確性、安全性、效能或可維護性。
  • 它是離散且可採取行動的。
  • 它是由被審查的變更引入的。
  • 受影響的情境或呼叫路徑可以從程式碼中示範。
  • 作者如果知道這個問題,很可能會修復它。

請勿標記推測性的問題、預先存在的問題、故意的行為變更或不會使程式碼變得模糊的風格小問題。

撰寫結果

首先呈現發現,按嚴重程度排序。每個問題使用一個條目,格式如下:

[P1] 關鍵發現的標題 — path/to/file.rs:line

在標題後,用一段簡短的段落解釋受影響的情境以及行為為何錯誤。盡量縮小引用的範圍,並確保它與審查的差異重疊。

使用以下優先級:

  • P0:普遍的發佈阻斷或關鍵失敗。
  • P1:應該立即修復的緊急缺陷。
  • P2:應該修復的一般缺陷。
  • P3:影響較低但仍值得修復的問題。

如果沒有合格的發現,請說 無發現。 請不要為了填充結果而發明一個發現。 在發現之後,加入簡短的整體評估,並提及任何重要的測試缺口或殘餘風險。

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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