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ln-311-review-research-workerln 311 评审研究员

Agent Skill

ln-311-review-research-worker 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

541

周安装

23

GitHub Stars

441

下载量

190
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-311-review-research-worker(ln 311 评审研究员)
来源仓库:https://github.com/levnikolaevich/claude-code-skills
仓库路径:skills/ln-311-review-research-worker
安装命令:
npx skills add https://github.com/levnikolaevich/claude-code-skills --skill ln-311-review-research-worker
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-311-review-research-worker

简介

用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中快速定位候选结果。

  • 支持基于关键词、任务场景或来源线索进行信息检索与筛选。
  • 通过 npx skills add 命令从指定 GitHub 仓库安装使用。
  • 安装前需确认权限范围、维护状态及是否涉及联网或文件操作。
  • ln-311-review-research-worker 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.

Type: L3 Worker Category: 3XX Planning

Review Research Worker

Structured research worker for validation, audit, and review flows.

Mandatory Read

MANDATORY READ: Load shared/references/evaluation_worker_runtime_contract.md, shared/references/evaluation_summary_contract.md, shared/references/evaluation_research_contract.md, shared/references/epistemic_protocol.md

Purpose

  • gather official documentation or standards
  • gather MCP Ref evidence
  • gather Context7 evidence when a library or framework is involved
  • gather current web best-practice evidence
  • emit a compact machine-readable research summary instead of prose-only notes

Runtime

Runtime family:

  • evaluation-worker-runtime

Required manifest fields:

  • identifier
  • phase_order
  • summary_kind=review-research
  • operation=research

Recommended phase_order:

  1. PHASE_0_CONFIG
  2. PHASE_1_RESOLVE_STACK
  3. PHASE_2_OFFICIAL_DOCS
  4. PHASE_3_MCP_REF
  5. PHASE_4_CONTEXT7
  6. PHASE_5_WEB_BEST_PRACTICES
  7. PHASE_6_ANTI_HALLUCINATION
  8. PHASE_7_WRITE_SUMMARY
  9. PHASE_8_SELF_CHECK

Workflow

Phase 0: Config

  1. Load runtime manifest.
  2. Resolve review target, stack hints, and output location.
  3. Fail if target context is missing.

Phase 1: Resolve Stack

  1. Detect language, framework, libraries, and domain.
  2. Build a bounded research topic list.
  3. Keep topic count small and evidence-oriented.

Phase 2: Official Docs

  1. Read official docs or standards first.
  2. Record source URLs and the exact topic each source supports.

Phase 3: MCP Ref

  1. Query MCP Ref for the same bounded topics.
  2. Prefer primary documentation over tertiary commentary.

Phase 4: Context7

  1. If a library or framework is involved, resolve the Context7 library id.
  2. Query only the libraries actually used by the target.
  3. If no relevant library exists, record that explicitly.

Phase 5: Web Best Practices

  1. Perform current web research for best practices and recent changes.
  2. Use current sources, not frozen heuristics.
  3. Record only evidence that changes a conclusion or adds decision value.

Phase 6: Anti-Hallucination Verification

  1. Scan target artifact for factual claims across all trigger categories per epistemic_protocol.md Section B.
  2. For each claim, check against research evidence gathered in Phases 2-5:

- Has MCP Ref/Context7/Web evidence → mark VERIFIED - No tool evidence but claim is plausible → mark FROM_TRAINING - Contradicts tool evidence → mark FLAGGED (CRITICAL)

  1. This step verifies against existing research. It does NOT run new searches.
  2. Include verification status in summary metadata.

Phase 7: Write Summary

Emit summary_kind=review-research.

Payload must include:

  • worker=ln-311
  • status
  • operation=research
  • warnings

Prefer these fields when available:

  • findings
  • metrics.research_sources
  • metrics.anti_hallucination_status (VERIFIED | FLAGGED)
  • metrics.flagged_claims_count
  • artifact_path
  • report_path
  • metadata

Phase 8: Self-Check

  1. Verify all four research lanes were attempted.
  2. Verify anti-hallucination verification was executed.
  3. Verify skipped lanes are justified in machine-readable form.
  4. Record pass=true only after the summary is written.

Definition of Done

  • Official-doc evidence recorded
  • MCP Ref evidence recorded
  • Context7 evidence recorded or justified as not applicable
  • Current web best-practice evidence recorded
  • Anti-hallucination verification executed (claims marked VERIFIED/FROM_TRAINING/FLAGGED)
  • review-research summary written
  • Self-check passed

Version: 1.0.0 Last Updated: 2026-04-10

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

34.23%
按下载量换算65

Claude

30.16%
按下载量换算57

Cursor

22.2%
按下载量换算42

Gemini CLI

9.92%
按下载量换算19

安全审计

Gen Agent Trust Hub

通过

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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