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ln-005-multi-agent-context-reviewln 005 多主体上下文审查

Agent Skill

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。

总安装

188

周安装

8

GitHub Stars

441

下载量

66
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: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

简介

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。

SKILL.md

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

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

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

external-service

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 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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