Token导航 LogoToken导航

Agent Skill · 研究检索

ln-316-review-refinement-worker(ln 316 审查精修工)

ln-316-review-refinement-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。

ln-316-review-refinement-worker 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,权限为只读;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 52,216。

总安装 563周安装 23GitHub Stars 441下载 180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-316-review-refinement-worker(ln 316 审查精修工)
来源仓库:https://github.com/levnikolaevich/claude-code-skills
仓库路径:skills/ln-316-review-refinement-worker
安装命令:
npx skills add https://github.com/levnikolaevich/claude-code-skills --skill ln-316-review-refinement-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-316-review-refinement-worker

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。

用于执行审查细化流程,包含两阶段状态机控制。支持并行顾问会议启动和最终合并后 sweep 操作。适用于复杂任务的迭代优化和质量提升。安装前建议确认权限范围、维护状态及是否涉及多进程调度。ln-316-review-refinement-worker 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-316-review-refinement-worker

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-3…;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

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

ln-316-review-refinement-worker 怎么安装?

在终端执行 npx skills add https://github.com/levnikolaevich/claude-code-skills --skill ln-316-review-…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

ln-316-review-refinement-worker 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-316-review-refinement-worker 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

ln-316-review-refinement-worker 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-316-review-refinement-worker 用的人多吗?

公开数据是累计安装 563 次,近一周安装 23 次,下载 180 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-316-review-refinement-worker 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

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。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 标注为只读权限,试用阶段不容易误改本地文件。

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。
  • 只想先试试效果、不希望它改动本地文件的人。

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-316-review-refinement-worker

ln 316 审查精修工 · 本页

563441只读未明确通过

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: L3 Worker Category: 3XX Planning

Review Refinement Worker

Mandatory Read

MANDATORY READ: Load shared/references/evaluation_worker_runtime_contract.md, shared/references/evaluation_summary_contract.md, shared/references/refinement_trace_contract.md, shared/references/cleanup_evidence_contract.md MANDATORY READ: Load shared/agents/prompt_templates/iterative_refinement.md, shared/agents/prompt_templates/refinement_perspectives.md MANDATORY READ: Load shared/references/monitor_integration_pattern.md, shared/references/agent_review_workflow.md (Step: Iterative Refinement)

Purpose

  • run 2-stage refinement after merge using the non-host advisor agent via agent_runner.mjs
  • Stage 1: 3 parallel independent advisor sessions (dry_run_executor, new_dev_tester, adversarial_reviewer)
  • Stage 2: 1 sequential advisor session (final_sweep) after merging Stage 1 results
  • record refinement trace and cleanup evidence for every advisor session

Critical: refinement launches the advisor externally. Do NOT use host-native sub-agents for this phase.

Runtime

Runtime family:

  • evaluation-worker-runtime

Required manifest fields:

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

Recommended phase_order:

  1. PHASE_0_CONFIG
  2. PHASE_1_STAGE1_PARALLEL
  3. PHASE_2_STAGE2_FINAL_SWEEP
  4. PHASE_3_WRITE_SUMMARY
  5. PHASE_4_SELF_CHECK

Refinement State Machine

Critical: Independent Sessions

Each perspective MUST be a separate node agent_runner.mjs --agent {advisor_agent} invocation. Do NOT combine multiple perspectives into a single advisor prompt or session. Each iter{N}/ subdirectory = independent advisor process with its own PID.

Perspective Classification

StagePerspectiveExecutionPurpose
1dry_run_executorparallelCatch unexecutable steps, sequencing errors
1new_dev_testerparallelCatch implicit knowledge gaps, undefined terms
1adversarial_reviewerparallelCatch guaranteed failures, silent corruption
2final_sweepafter mergeCatch regressions and drift from Stage 1 fixes

All 4 perspectives are MANDATORY. generic_quality is not included — it is covered by the Phase 2 advisor review (review_base.md + mode template).

Stage 1: Parallel Specialized Reviews

  1. Build artifact: Read current state of reviewed artifact (Story+Tasks / plan file / context docs).
  2. For EACH of 3 perspectives, in parallel: a. Load perspective from refinement_perspectives.md matching the perspective name. b. Build prompt: fill iterative_refinement.md placeholders ({artifact_type}, {artifact_content}, {project_context}, {review_perspective}, {iteration_number}, {max_iterations}, {previous_findings_summary}). c. Save prompt to .hex-skills/agent-review/refinement/{identifier}/iter{N}/prompt.md node shared/agents/agent_runner.mjs --agent {advisor_agent} \ --prompt-file.hex-skills/agent-review/refinement/{identifier}/iter{N}/prompt.md \ --output-file.hex-skills/agent-review/refinement/{identifier}/iter{N}/result.md \ --cwd {project_dir}

- iter1/ = dry_run_executor - iter2/ = new_dev_tester - iter3/ = adversarial_reviewer d. Launch independent advisor process:

  1. Wait for ALL 3 via runtime sync-agent; Claude hosts may use Monitor for observability (see Waiting section below).
  2. Parse results from each completed session: extract JSON from ## Structured Data section.
  3. Merge findings: deduplicate by (area, issue), keep higher confidence.
  4. Classify: HIGH (impact_percent >= 20%), MEDIUM (10-19%), LOW (< 10%).
  5. Architecture Gate on each accepted fix: "Does this implement the correct architecture directly, without backward compatibility shims?"
  6. Apply accepted fixes.
  7. Kill all 3 processes: node agent_runner.mjs --verify-dead {pid} per session. MANDATORY on Windows.
  8. Record cleanup evidence per cleanup_evidence_contract.md for each session.
  9. Build {previous_findings_summary} for Stage 2.

If ALL 3 advisor sessions fail → EXIT(ERROR), skip Stage 2. If some fail → continue with available results, record partial errors.

Stage 2: Final Sweep

  1. Build artifact: Read post-fix state after Stage 1.
  2. Load final_sweep perspective from refinement_perspectives.md.
  3. Build prompt with {previous_findings_summary} from Stage 1.
  4. Save prompt to .hex-skills/agent-review/refinement/{identifier}/iter4/prompt.md.
  5. Launch advisor (single independent session).
  6. Wait via runtime sync-agent; Claude hosts may use Monitor for observability.
  7. Parse result, apply any accepted fixes (Architecture Gate on each).
  8. Kill process, record cleanup evidence.

Waiting for Advisor Results (MANDATORY)

Use the active runtime sync-agent command before parsing or merge gates. Monitor is optional Claude Code observability only.

For EACH launched advisor process:

When running under Claude Code, optional observability:

Monitor(command="tail -f {agent_log} | grep --line-buffered -E 'Phase|ERROR|DONE'", timeout_ms=120000, description="advisor refinement {perspective_name}")

After each sync/monitor cycle:

  • Check result file for <!-- END_AGENT_REVIEW_RESULT --> marker.
  • Marker present → parse result, proceed.
  • Marker absent, log growing → continue runtime sync or optional monitor cycle.
  • Marker absent, log stale >3 min → run Liveness Protocol (see agent_review_workflow.md).

Do NOT use sleep or manual stat-polling as the primary wait mechanism.

Process Cleanup

After each advisor call (both stages):

  1. Extract pid from runner stdout or metadata.
  2. Run node shared/agents/agent_runner.mjs --verify-dead {pid}.
  3. Record cleanup evidence per cleanup_evidence_contract.md.
  4. CLI advisor processes can accumulate on Windows if not killed.

Exit States

StateMeaning
COMPLETEDBoth stages done, all results merged
PARTIAL_ERRORStage 1 had failures but Stage 2 completed
ERRORAll Stage 1 advisor sessions failed (Stage 2 skipped)
SKIPPEDNo advisor available in health check

Summary

Emit summary_kind=review-refinement.

Payload must include:

  • worker=ln-316
  • status
  • operation=refinement
  • warnings

Prefer these fields:

  • stages_completed (int: 1 or 2)
  • exit_reason (enum: COMPLETED, PARTIAL_ERROR, ERROR, SKIPPED)
  • applied (int: total suggestions applied across all stages)
  • architecture_gate_rejections (count)
  • stage1_perspectives (list of completed perspective names)
  • stage1_failed (list of failed perspective names)
  • metadata.refinement_trace

Definition of Done

  • Stage 1: all 3 advisor sessions launched in parallel
  • Stage 2: final_sweep launched after Stage 1 merge
  • All advisors launched via agent_runner.mjs (not host-native sub-agents)
  • Runtime sync-agent used for waiting; Claude Monitor is optional observability
  • Refinement trace recorded per refinement_trace_contract.md
  • Cleanup evidence recorded for all launched processes
  • review-refinement summary written
  • Self-check passed

Version: 2.0.0 Last Updated: 2026-04-13

平台分布

Codex

39.29%
按下载量换算71

Claude

28.47%
按下载量换算51

Cursor

18.5%
按下载量换算33

Gemini CLI

9.44%
按下载量换算17

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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。目前只有一个公开来源,建议同时核对源仓库。

继续浏览同类 Skills