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ce-doc-reviewCE 文件审查

Agent Skill

用于辅助文档、README、Markdown、说明文和内容稿件的整理与改写。它适合让 Agent 提炼结构、补齐章节、统一术语、检查链接或把零散材料整理成可读文档。使用时应保留项目已有事实、命令和路径,不要把未确认的信息写成确定结论;涉及对外文案时,还需要控制语气,避免过度营销或夸大能力。

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CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

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:ce-doc-review(CE 文件审查)
来源仓库:https://github.com/everyinc/compound-engineering-plugin
仓库路径:skills/ce-doc-review
安装命令:
npx skills add https://github.com/everyinc/compound-engineering-plugin --skill ce-doc-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 npx skills 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。

skills.shnpx skills
npx skills add https://github.com/everyinc/compound-engineering-plugin --skill ce-doc-review

简介

用于辅助文档、README 和 Markdown 内容的整理与改写。

  • 适合提炼结构、补齐章节、统一术语或检查链接有效性。
  • 使用时需保留项目已有事实,避免将未确认信息写成确定结论。
  • 涉及对外文案时应控制语气,防止过度营销或夸大能力描述。
  • ce-doc-review 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Document Review

Review requirements or plan documents through multi-persona analysis. Dispatches specialized reviewer agents in parallel, auto-applies safe_auto fixes, and routes remaining findings through a four-option interaction (per-finding walk-through, auto-resolve with best judgment, Append-to-Open-Questions, Report-only) for user decision.

Interactive mode rules

  • Pre-load the platform question tool before any question fires. In Claude Code, AskUserQuestion is a deferred tool — its schema is not available at session start. At the start of Interactive-mode work (before the routing question, per-finding walk-through questions, bulk-preview Proceed/Cancel, and Phase 5 terminal question), call ToolSearch with query select:AskUserQuestion to load the schema. Load it once, eagerly, at the top of the Interactive flow — do not wait for the first question site. On Codex, Gemini, and Pi this preload is not required.
  • The numbered-list fallback applies only when the harness genuinely lacks a blocking question toolToolSearch returns no match, the tool call explicitly fails, or the runtime mode does not expose it (e.g., Codex edit modes where request_user_input is unavailable). A pending schema load is not a fallback trigger; call ToolSearch first per the pre-load rule. In genuine-fallback cases, present options as a numbered list and wait for the user's reply — never silently skip the question. Rendering a question as narrative text because the tool feels inconvenient, because the model is in report-formatting mode, or because the instruction was buried in a long skill is a bug. A question that calls for a user decision must either fire the tool or fall back loudly.

Phase 0: Detect Mode

Check the skill arguments for mode:headless. Arguments may contain a document path, mode:headless, or both. Tokens starting with mode: are flags, not file paths — strip them from the arguments and use the remaining token (if any) as the document path for Phase 1.

If mode:headless is present, set headless mode for the rest of the workflow.

Headless mode changes the interaction model, not the classification boundaries. ce-doc-review still applies the same judgment about which tier each finding belongs in. The only difference is how non-safe_auto findings are delivered:

  • safe_auto fixes are applied silently (same as interactive)
  • gated_auto, manual, and FYI findings are returned as structured text for the caller to handle — no blocking-question prompts, no interactive routing
  • Phase 5 returns immediately with "Review complete" (no routing question, no terminal question)

The caller receives findings with their original classifications intact and decides what to do with them.

Callers invoke headless mode by including mode:headless in the skill arguments, e.g.:

Skill("ce-doc-review", "mode:headless docs/plans/my-plan.md")

If mode:headless is not present, the skill runs in its default interactive mode with the routing question, walk-through, and bulk-preview behaviors documented in references/walkthrough.md and references/bulk-preview.md.

Phase 1: Get and Analyze Document

If a document path is provided: Read it, then proceed.

If no document is specified (interactive mode): Ask which document to review, or find the most recent in docs/brainstorms/ or docs/plans/ using a file-search/glob tool (e.g., Glob in Claude Code).

If no document is specified (headless mode): Output "Review failed: headless mode requires a document path. Re-invoke with: Skill("ce-doc-review", "mode:headless ")" without dispatching agents.

Classify Document Type

After reading, classify the document:

  • requirements -- from docs/brainstorms/, focuses on what to build and why
  • plan -- from docs/plans/, focuses on how to build it with implementation details

Select Conditional Personas

Analyze the document content to determine which conditional personas to activate. Check for these signals:

product-lens -- activate when the document makes challengeable claims about what to build and why, or when the proposed work carries strategic weight beyond the immediate problem. The system's users may be end users, developers, operators, maintainers, or any other audience -- the criteria are domain-agnostic. Check for either leg:

*Leg 1 — Premise claims:* The document stakes a position on what to build or why that a knowledgeable stakeholder could reasonably challenge -- not merely describing a task or restating known requirements:

  • Problem framing where the stated need is non-obvious or debatable, not self-evident from existing context
  • Solution selection where alternatives plausibly exist (implicit or explicit)
  • Prioritization decisions that explicitly rank what gets built vs deferred
  • Goal statements that predict specific user outcomes, not just restate constraints or describe deliverables

*Leg 2 — Strategic weight:* The proposed work could affect system trajectory, user perception, or competitive positioning, even if the premise is sound:

  • Changes that shape how the system is perceived or what it becomes known for
  • Complexity or simplicity bets that affect adoption, onboarding, or cognitive load
  • Work that opens or closes future directions (path dependencies, architectural commitments)
  • Opportunity cost implications -- building this means not building something else

design-lens -- activate when the document contains:

  • UI/UX references, frontend components, or visual design language
  • User flows, wireframes, screen/page/view mentions
  • Interaction descriptions (forms, buttons, navigation, modals)
  • References to responsive behavior or accessibility

security-lens -- activate when the document contains:

  • Auth/authorization mentions, login flows, session management
  • API endpoints exposed to external clients
  • Data handling, PII, payments, tokens, credentials, encryption
  • Third-party integrations with trust boundary implications

scope-guardian -- activate when the document contains:

  • Multiple priority tiers (P0/P1/P2, must-have/should-have/nice-to-have)
  • Large requirement count (>8 distinct requirements or implementation units)
  • Stretch goals, nice-to-haves, or "future work" sections
  • Scope boundary language that seems misaligned with stated goals
  • Goals that don't clearly connect to requirements

adversarial -- activate when the document contains:

  • More than 5 distinct requirements or implementation units
  • Explicit architectural or scope decisions with stated rationale
  • High-stakes domains (auth, payments, data migrations, external integrations)
  • Proposals of new abstractions, frameworks, or significant architectural patterns

Phase 2: Announce and Dispatch Personas

Announce the Review Team

Tell the user which personas will review and why. For conditional personas, include the justification:

Reviewing with:
- ce-coherence-reviewer (always-on)
- ce-feasibility-reviewer (always-on)
- ce-scope-guardian-reviewer -- plan has 12 requirements across 3 priority levels
- ce-security-lens-reviewer -- plan adds API endpoints with auth flow

Build Agent List

Always include:

  • ce-coherence-reviewer
  • ce-feasibility-reviewer

Add activated conditional personas:

  • ce-product-lens-reviewer
  • ce-design-lens-reviewer
  • ce-security-lens-reviewer
  • ce-scope-guardian-reviewer
  • ce-adversarial-document-reviewer

Dispatch

Dispatch agents using bounded parallelism with the platform's subagent primitive (e.g., Agent in Claude Code, spawn_agent in Codex, subagent in Pi via the pi-subagents extension). Omit the mode parameter so the user's configured permission settings apply. Respect the current harness's active-subagent limit: queue selected reviewers, dispatch only as many as the harness accepts, and fill freed slots as reviewers complete. Treat active-agent/thread/concurrency-limit spawn errors as backpressure, not reviewer failure: leave the reviewer queued and retry after a slot frees. Record a reviewer as failed only after a successful dispatch times out/fails, or when dispatch fails for a non-capacity reason.

Each agent receives the prompt built from the subagent template included below with these variables filled:

VariableValue
{persona_file}Full content of the agent's markdown file
{schema}Content of the findings schema included below
{document_type}"requirements" or "plan" from Phase 1 classification
{document_path}Path to the document
{document_content}Full text of the document
{decision_primer}Cumulative prior-round decisions in the current session, or an empty <prior-decisions> block on round 1. See "Decision primer" below.

Pass each agent the full document — do not split into sections.

Decision primer

On round 1 (no prior decisions), set {decision_primer} to:

<prior-decisions>
Round 1 — no prior decisions.
</prior-decisions>

On round 2+ (after one or more prior rounds in the current interactive session), accumulate prior-round decisions and render them as:

<prior-decisions>
Round 1 — applied (N entries):
- {section}: "{title}" ({reviewer}, {confidence})
  Evidence: "{evidence_snippet}"

Round 1 — rejected (M entries):
- {section}: "{title}" — Skipped because {reason}
  Evidence: "{evidence_snippet}"
- {section}: "{title}" — Deferred to Open Questions because {reason or "no reason provided"}
  Evidence: "{evidence_snippet}"
- {section}: "{title}" — Acknowledged without applying because {reason or "no suggested_fix — user acknowledged"}
  Evidence: "{evidence_snippet}"

Round 2 — applied (N entries):
...
</prior-decisions>

Each entry carries an Evidence: line because synthesis R29 (rejected-finding suppression) and R30 (fix-landed verification) both use an evidence-substring overlap check as part of their matching predicate — without the evidence snippet in the primer, the orchestrator cannot compute the >50% overlap test and has to fall back to fingerprint-only matching, which either re-surfaces rejected findings or suppresses too aggressively. The {evidence_snippet} is the first evidence quote from the finding, truncated to the first ~120 characters (preserving whole words at the boundary) and with internal quotes escaped. If a finding has multiple evidence entries, use the first one; the rest live in the run artifact and are not needed for the overlap check.

Accumulate across all rounds in the current session. Skip, Defer, and Acknowledge actions all count as "rejected" for suppression purposes — each signals the user decided the finding wasn't worth actioning this round (Acknowledge is the no-fix-guard variant: the user saw a finding with no suggested_fix, chose not to defer or skip explicitly, and recorded acknowledgement instead; for round-to-round suppression that is semantically equivalent to Skip). Applied findings stay on the applied list so round-N+1 personas can verify fixes landed (see R30 in references/synthesis-and-presentation.md).

Cross-session persistence is out of scope. A new invocation of ce-doc-review on the same document starts with a fresh round 1 and no carried primer, even if prior sessions deferred findings into the document's Open Questions section.

Error handling: If an agent fails or times out, proceed with findings from agents that completed. Note the failed agent in the Coverage section. Do not block the entire review on a single agent failure.

Dispatch limit: Even at maximum (7 agents), use bounded parallel dispatch. If the harness cap is lower than the selected team size, queue the remainder and launch them as active reviewers complete.

Phases 3-5: Synthesis, Presentation, and Next Action

After all dispatched agents return, read references/synthesis-and-presentation.md for the synthesis pipeline (validate, anchor-based gate, dedup, cross-persona agreement promotion, resolve contradictions, auto-promotion, route by three tiers with FYI subsection), safe_auto fix application, headless-envelope output, and the handoff to the routing question.

For the four-option routing question and per-finding walk-through (interactive mode), read references/walkthrough.md. For the bulk-action preview used by best-judgment routing, Append-to-Open-Questions, and walk-through Auto-resolve with best judgment on the rest, read references/bulk-preview.md. Do not load these files before agent dispatch completes.


Included References

Subagent Template

@./references/subagent-template.md

Findings Schema

@./references/findings-schema.json

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

37.55%
按下载量换算266

Claude

27.57%
按下载量换算195

Cursor

17.99%
按下载量换算128

Gemini CLI

9.37%
按下载量换算66

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

未通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/everyinc/compound-engineering-plugin --skill ce-doc-review 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

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 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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