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Agent Skill · 研究检索

ln-005-multi-agent-context-review(ln 005 多主体上下文审查)

ln-005-multi-agent-context-review 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 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。

ln-005-multi-agent-context-review 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为调用外部服务;许可证信息还不明确,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 72,323。

总安装 188周安装 8GitHub Stars 441下载 66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
unknown

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:ln-005-multi-agent-context-review(ln 005 多主体上下文审查)
来源仓库:https://github.com/levnikolaevich/claude-code-skills
仓库路径:skills/ln-005-multi-agent-context-review
安装命令:
npx skills add https://github.com/levnikolaevich/claude-code-skills --skill ln-005-multi-agent-context-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/levnikolaevich/claude-code-skills --skill ln-005-multi-agent-context-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。

用于查找和筛选与多 Agent 上下文审查相关的信息。适合根据任务需求快速定位协作模式或上下文管理方案。通过 GitHub 安装,需结合原始 README 验证功能。建议在使用前确认是否具备访问多 Agent 日志的权限。注意上下文一致性,避免信息碎片化导致决策偏差。

ln-005-multi-agent-context-review 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/ln-005-multi-agent-context-review

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 Codex、Claude、Cursor、Gemini CLI;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 npx skills add https://github.com/levnikolaevich/claude-code-skills --skill ln-0…;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

    它需要调用外部服务,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

ln-005-multi-agent-context-review 怎么安装?

在终端执行 npx skills add https://github.com/levnikolaevich/claude-code-skills --skill ln-005-multi-a…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

ln-005-multi-agent-context-review 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。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。

ln-005-multi-agent-context-review 需要什么权限?

权限级别是调用外部服务。该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

ln-005-multi-agent-context-review 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

ln-005-multi-agent-context-review 用的人多吗?

公开数据是累计安装 188 次,近一周安装 8 次,下载 66 次,GitHub 441 星。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。

ln-005-multi-agent-context-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

  • 同时适配 4 种工具,换客户端不用重新找替代品。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 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 Codex,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

  • 需要明确授权才能用到公司项目里的场景。
  • 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
ln-005-multi-agent-context-review

ln 005 多主体上下文审查 · 本页

188441调用外部服务未明确有提醒

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

Paths: File paths (shared/, references/, ../ln-*) are relative to skills repo root. If not found at CWD, locate this SKILL.md directory and go up one level for repo root.

Multi-Agent Context Review (Universal)

Runs parallel external agent reviews on arbitrary context, critically verifies suggestions, returns filtered improvements.

Purpose & Scope

  • Standalone utility in 0XX category (like ln-003, ln-004)
  • Delegate any context to codex-review + gemini-review as background tasks in parallel
  • Context always passed via file references (never inline in prompt)
  • Process results as they arrive (first-finished agent processed immediately)
  • Critically verify each suggestion; debate with agent if Claude disagrees
  • Return filtered, deduplicated, verified suggestions

When to Use

  • Manual invocation by user for independent review of any artifact
  • Called by any skill needing external second opinion on plans, decisions, documents
  • NOT tied to Linear, NOT tied to any pipeline
  • Works with any context that can be saved to a file

Plan Mode Support

Follows shared/references/plan_mode_pattern.md (Workflow B) and shared/references/agent_review_workflow.md Plan Mode Behavior. Step 7e (Compare & Correct): output findings to chat, apply edits only after user approval.

Parameters

ParameterValue
review_typecontextreview
skill_group005
prompt_templateshared/agents/prompt_templates/context_review.md
verdict_acceptableCONTEXT_ACCEPTABLE

Inputs

InputRequiredDescription
context_filesYesList of file paths containing context to review (relative to CWD)
identifierNoShort label for file naming (default: review_YYYYMMDD_HHMMSS)
focusNoList of areas to focus on (default: all 6)
review_titleNoHuman-readable title (default: "Context Review")
tech_stackNoTechnology stack override (e.g., "Python FastAPI", "C# ASP.NET Core"). Auto-detected if not provided.

Context delivery rule: Context is ALWAYS passed via files.

  • If context already exists as files (plans, docs, code) -> pass file paths directly
  • If context is a statement/decision from chat -> caller creates a temporary file in .agent-review/context/ with the content, then passes the file path

Workflow

MANDATORY READ: Load shared/references/agent_review_workflow.md for Health Check, Ensure.agent-review/, Load Review Memory, Run Agents, Critical Verification + Debate, Aggregate + Return, Save Review Summary, Fallback Rules, Critical Rules, and Definition of Done. Load shared/references/agent_delegation_pattern.md for Reference Passing Pattern, Review Persistence Pattern, Agent Timeout Policy, and Debate Protocol.

Unique Steps (before shared workflow)

  1. Health check: per shared workflow, filter by skill_group = 005.
  2. Resolve identifier: If identifier not provided, generate review_YYYYMMDD_HHMMSS. Sanitize: lowercase, replace spaces with hyphens, ASCII only.
  3. Ensure.agent-review/: per shared workflow. Additionally create .agent-review/context/ subdir if it doesn't exist (for materialized context files).
  4. Materialize context (if needed): If context is from chat/conversation (not an existing file):

- Write content to .agent-review/context/{identifier}_context.md - Add this path to context_files list

  1. Build prompt: Read template shared/agents/prompt_templates/context_review.md.

- Replace {review_title} with title or "Context Review" - Replace {context_refs} with bullet list: - {path} per context file - Replace {focus_areas} with filtered subset or "All default areas" if no focus specified - Save to .agent-review/{identifier}_contextreview_prompt.md (single shared file -- both agents read the same prompt)

Shared Workflow Steps

  1. Launch Agents (background) — MANDATORY: before any foreground research:

Per shared workflow "Step: Run Agents". Prompt file is ready (step 5). Launch BOTH agents as background tasks NOW.

  • {review_type} in challenge template = review_title or "Context Review"
  • {story_ref} in challenge template = identifier
  1. Foreground Research (while agents are running in background):

Agents are already thinking. Use this time for Review Memory + MCP Ref research.

Agents (background)                    Claude (foreground)
  codex-review ──┐                       7a) Load Review Memory
  gemini-review ─┤                       7b) Applicability Check
                 │                       7c) Stack Detection
                 │                       7d) Extract Topics
                 │                       7e) MCP Ref Research
                 │                       7f) Compare & Correct
                 ├── first completes ──→ 7g) Save Findings
                 └── second completes    8) Critical Verification (informed by memory + findings)

MANDATORY READ: Load shared/references/research_tool_fallback.md

7a) Load Review Memory

Per shared workflow "Step: Load Review Memory". Not passed to agents — used only in step 8 (Critical Verification).

7b) Applicability Check

Scan context_files for technology decision signals (skip 7c-7e if no signals found):

Signal TypeWeightExamples
Infrastructure choice5Redis, PostgreSQL, K8s, Docker, RabbitMQ
API/protocol decision4REST vs GraphQL, WebSocket, gRPC, OAuth 2.0
Security mechanism4JWT, PKCE, CORS, rate limiting, OWASP
Library/framework choice3FastAPI, Polly, SQLAlchemy, Pydantic
Architectural pattern3CQRS, event sourcing, middleware chain, DI
Configuration/tooling1ESLint, Prettier, CI config
  • No signals found → skip MCP Ref research, log "MCP Ref skipped: no technology decisions detected"
  • Fewer than 3 topics with weight >= 3 → skip
7c) Stack Detection

Priority order:

  1. tech_stack input parameter → use directly as query_prefix
  2. docs/tools_config.md Research section → extract stack hints
  3. Glob for indicator files:
IndicatorStackQuery Prefix
*.csproj, *.sln.NET"C# ASP.NET Core"
package.json + tsconfig.jsonNode.js"TypeScript Node.js"
requirements.txt, pyproject.tomlPython"Python"
go.modGo"Go Golang"
Cargo.tomlRust"Rust"
build.gradle, pom.xmlJava"Java"
  1. Parse context_files for technology mentions (fallback heuristic)

Output: detected_stack = {query_prefix} or empty (generic queries)

7d) Extract Topics (3-5)
  • Parse all context_files for technology decisions
  • Score each by weight from 7b table
  • Take top 3-5 with weight >= 3
  • Format: {topic_name, plan_statement, file_path, line_ref}
7e) MCP Ref Research

Per research_tool_fallback.md chain: Ref -> Context7 -> WebSearch -> built-in knowledge.

For each topic:

  • Query: "{query_prefix} {topic} RFC standard best practices {current_year}"
  • Collect: {official_position, source, matches_plan: bool}
  • Run queries in parallel where possible
7f) Compare & Correct

For each topic where matches_plan == false (high confidence):

  • Apply surgical Edit to plan file (single-line or minimal multi-line change)
  • Add inline rationale: "(per {RFC/standard}:...)"
  • Record correction in findings

For each topic where finding is ambiguous:

  • Record as "REVIEW NEEDED" (not auto-corrected)

IF Plan Mode -> corrections applied to plan-mode file directly.

Safety rules:

  • Max 5 corrections per run
  • Each correction must cite specific RFC/standard/doc
  • Only correct when official docs directly contradict plan statement
7g) Save Findings

Write to .agent-review/context/{identifier}_mcp_ref_findings.md (per references/mcp_ref_findings_template.md).

IF Plan Mode -> output findings to chat, skip file write.

Display: "MCP Ref: {N} topics validated, {M} corrections, {K} confirmed"


  1. Critical Verification + Debate: per shared workflow, with MCP Ref enhancement:

- If MCP Ref research completed (7b-7g), use findings to inform AGREE/DISAGREE decisions - Agent suggestion contradicts MCP Ref finding → DISAGREE with RFC/standard citation - Agent suggestion aligns with MCP Ref finding → AGREE with higher confidence - Agent suggests something MCP Ref didn't cover → standard verification (no enhancement)

  1. Aggregate + Return: per shared workflow. Merge agent suggestions + MCP Ref corrections into unified output.
  2. Save Review Summary: per shared workflow "Step: Save Review Summary". IF Plan Mode → output to chat, skip file save.

Output Format

verdict: CONTEXT_ACCEPTABLE | SUGGESTIONS | SKIPPED
mcp_ref_corrections:
  count: 2
  topics_validated: 5
  corrections:
    - topic: "OAuth 2.0"
      file: "plan.md"
      line: 42
      before: "Use implicit flow"
      after: "Use Authorization Code + PKCE (RFC 6749)"
      source: "ref_search_documentation"
  findings_file: ".agent-review/context/{identifier}_mcp_ref_findings.md"
suggestions:
  - area: "logic | feasibility | completeness | consistency | best_practices | risk"
    issue: "What is wrong or could be improved"
    suggestion: "Specific actionable change"
    confidence: 95
    impact_percent: 15
    source: "codex-review"
    resolution: "accepted | accepted_after_debate | accepted_after_followup | rejected"
  • mcp_ref_corrections: present only when MCP Ref research ran. Omitted when skipped.
  • Agent stats and debate log per shared workflow output schema.

Verdict Escalation

  • No escalation. Suggestions are advisory only.
  • Caller decides how to apply accepted suggestions.

Reference Files

  • Shared workflow: shared/references/agent_review_workflow.md
  • Agent delegation pattern: shared/references/agent_delegation_pattern.md
  • Prompt template (review): shared/agents/prompt_templates/context_review.md
  • Review schema: shared/agents/schemas/context_review_schema.json
  • Research fallback: shared/references/research_tool_fallback.md
  • MCP Ref findings template: references/mcp_ref_findings_template.md

Version: 1.1.0 Last Updated: 2026-03-06

平台分布

Codex

32.74%
按下载量换算22

Claude

29.11%
按下载量换算19

Cursor

18.93%
按下载量换算12

Gemini CLI

9.53%
按下载量换算6

安全审计

Gen Agent Trust Hub

可疑

Socket

通过

Snyk

可疑

权限和风险

调用外部服务

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

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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