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Agent Skill · 研究检索

ln-510-quality-coordinator(ln 510 质量协调员)

ln-510-quality-coordinator 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-510-quality-coordinator 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 18,685。

总安装 5,964周安装 251GitHub Stars 437下载 2,088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-510-quality-coordinator(ln 510 质量协调员)
来源仓库:https://github.com/levnikolaevich/claude-code-skills
仓库路径:skills/ln-510-quality-coordinator
安装命令:
npx skills add https://github.com/levnikolaevich/claude-code-skills --skill ln-510-quality-coordinator
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-510-quality-coordinator

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。

用于协调多阶段质量评估流程,包含代码审查、清理验证等环节。支持归一化质量判定计算(通过/担忧/失败)。适用于大规模代理协作项目的质量控制。安装前建议确认权限范围、维护状态及是否触发多工人协同操作。ln-510-quality-coordinator 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-510-quality-coordinator

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-5…;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-510-quality-coordinator 怎么安装?

在终端执行 npx skills add https://github.com/levnikolaevich/claude-code-skills --skill ln-510-quality…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

ln-510-quality-coordinator 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-510-quality-coordinator 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-510-quality-coordinator 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-510-quality-coordinator 用的人多吗?

公开数据是累计安装 5,964 次,近一周安装 251 次,下载 2,088 次,GitHub 437 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-510-quality-coordinator 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-510-quality-coordinator

ln 510 质量协调员 · 本页

5,964437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.

Type: L2 Coordinator Category: 5XX Quality

Quality Coordinator

Evaluation-platform coordinator for story quality review.

Mandatory Read

MANDATORY READ: Load shared/references/evaluation_coordinator_runtime_contract.md, shared/references/evaluation_summary_contract.md, shared/references/evaluation_research_contract.md, shared/references/loop_health_contract.md MANDATORY READ: Load shared/references/agent_review_workflow.md, shared/references/agent_delegation_pattern.md MANDATORY READ: Load references/criteria_validation.md, references/gate_levels.md

Purpose

  • invoke ln-511-code-quality-checker
  • invoke ln-512-tech-debt-cleaner
  • invoke ln-513-regression-checker
  • invoke ln-514-test-log-analyzer
  • run inline agent review in parallel with read-only evidence gathering
  • keep merge, refinement, and verdict sequential
  • return normalized quality results

Inputs

Primary input:

  • storyId
  • --previous-cycle-focus (optional, from ln-500): comma-separated blocking categories from prior FAIL cycle

Status filter:

  • To Review

Critical Rule

Fast-track paths that skip research are not allowed.

Every quality run must include:

  1. official documentation or standards
  2. MCP Ref
  3. Context7 when a framework or library is involved
  4. current web best-practice research

Runtime Contract

Runtime family:

  • evaluation-runtime

Identifier:

  • quality-{storyId}

Phase order:

  1. PHASE_0_CONFIG
  2. PHASE_1_DISCOVERY
  3. PHASE_2_READ_ONLY_EVIDENCE
  4. PHASE_3_CLEANUP
  5. PHASE_4_AGENT_BARRIER
  6. PHASE_5_MERGE
  7. PHASE_7_REFINEMENT
  8. PHASE_8_VERDICT
  9. PHASE_9_SELF_CHECK

Worker Invocation (MANDATORY)

Use the Skill tool for delegated workers. Do not inline worker logic inside the coordinator.

TodoWrite format (mandatory):

  • Resolve Story and build runtime manifest
  • Load Story metadata and detect changed files
  • Run quality checkers and research in parallel
  • Apply safe tech-debt cleanup
  • Sync agents and wait for all evidence
  • Merge and deduplicate all findings
  • Run bounded refinement loop
  • Compute quality verdict and score
  • Verify runtime cleanup and self-check

Representative invocations:

Skill(skill: "ln-311-review-research-worker", args: "{storyId} quality research")
Skill(skill: "ln-511-code-quality-checker", args: "{storyId}")
Skill(skill: "ln-512-tech-debt-cleaner", args: "{storyId}")
Skill(skill: "ln-513-regression-checker", args: "{storyId}")
Skill(skill: "ln-514-test-log-analyzer", args: "{storyId}")

Workflow

Phase 0: Config

  1. Resolve storyId.
  2. Build evaluation runtime manifest with required_research=true.
  3. Start evaluation-runtime.

Phase 1: Discovery

  1. Load Story metadata and completed implementation task scope.
  2. Detect changed files and project stack.
  3. Index semantic graph when available.

Phase 2: Read-Only Evidence

Parallel work allowed in this phase:

  • ln-311-review-research-worker
  • ln-511-code-quality-checker
  • ln-513-regression-checker
  • ln-514-test-log-analyzer
  • external agent launch

Rules:

  • research is mandatory
  • worker summaries are the only completion signal
  • no merge or mutation occurs in this phase

When previous_cycle_focus is provided:

  • Prioritize evidence collection for the listed blocking categories.
  • ln-511 code quality checker should focus on the specified areas first.
  • This does not exclude other evidence — it reorders priority.

Phase 3: Cleanup

  1. Run ln-512-tech-debt-cleaner only after read-only evidence is collected.
  2. Cleanup remains sequential because it mutates files.
  3. Record the worker summary and any cleanup evidence.

Phase 4: Agent Barrier

  1. Sync agents through evaluation-runtime.
  2. Do not cross this barrier until all required agents are resolved or explicitly skipped.
  3. Treat failure_class from agent results as transport evidence:

- rate_limited, tool_missing, auth_missing, permission_denial, and asked_question are not quality FAIL findings by themselves. - timeout_productive can continue to merge/review only when output/log/session evidence exists. - repeated identical worker/agent failure without new artifacts pauses through loop health before another cycle.

Phase 5: Merge

Merge inputs:

  • research summary
  • ln-511 summary
  • ln-512 summary
  • ln-513 summary
  • ln-514 summary
  • agent findings

Rules:

  • deduplicate before scoring
  • unsupported claims are rejected
  • security and correctness issues remain high priority

Phase 6: Refinement

Refinement uses a 2-stage state machine (see ln-316-review-refinement-worker):

  • Stage 1 (parallel): dry_run_executor, new_dev_tester, adversarial_reviewer
  • Stage 2 (after merge): final_sweep

Rules:

  • Stage 1 runs in parallel, Stage 2 after merge
  • cleanup evidence required for spawned processes
  • no research skipping

Phase 7: Verdict

Compute normalized quality verdict using:

  • code quality
  • cleanup result
  • agent review
  • criteria validation
  • linter result
  • regression result
  • log analysis result

Final verdict values:

  • PASS
  • CONCERNS
  • FAIL

Phase 8: Self-Check

Required checks:

  • runtime started
  • mandatory research completed
  • all worker summaries recorded
  • all required agents resolved before merge
  • cleanup verified
  • refinement trace recorded when applicable
  • coordinator summary written

Summary Contract

Write summary_kind=evaluation-coordinator.

Recommended payload:

  • status
  • final_result
  • report_path
  • worker_count
  • agent_count
  • issues_total
  • severity_counts
  • warnings
  • cleanup_verified
  • research_completed

Definition of Done

  • Evaluation runtime started
  • Mandatory research completed
  • Read-only evidence workers completed
  • Cleanup worker completed or justified
  • Agent barrier resolved
  • Merge completed
  • Refinement executed or explicitly justified
  • Final verdict calculated
  • evaluation-coordinator summary written
  • Runtime completed

Meta-Analysis

MANDATORY READ: Load shared/references/meta_analysis_protocol.md

After the coordinator run, analyze the session per protocol section 7 and include the protocol-formatted output with the final quality verdict.

References

  • Runtime: shared/references/evaluation_coordinator_runtime_contract.md, shared/references/evaluation_summary_contract.md
  • Research: shared/references/evaluation_research_contract.md
  • Workers: ../ln-511-code-quality-checker/SKILL.md, ../ln-512-tech-debt-cleaner/SKILL.md, ../ln-513-regression-checker/SKILL.md, ../ln-514-test-log-analyzer/SKILL.md
  • Quality criteria: references/criteria_validation.md, references/gate_levels.md

Version: 7.0.0 Last Updated: 2026-02-09

平台分布

Codex

34.04%
按下载量换算711

Claude

29.71%
按下载量换算620

Cursor

19.82%
按下载量换算414

Gemini CLI

9.53%
按下载量换算199

安全审计

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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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