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Agent Skill · 研究检索

ln-641-pattern-analyzer(ln 641 模式分析仪)

ln-641-pattern-analyzer 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-641-pattern-analyzer 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,第三方扫描有未通过项,按安装量在研究检索类的 73,675 个 Skill 里排第 18,550。

总安装 6,030周安装 259GitHub Stars 437下载 2,113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-641-pattern-analyzer(ln 641 模式分析仪)
来源仓库:https://github.com/levnikolaevich/claude-code-skills
仓库路径:skills/ln-641-pattern-analyzer
安装命令:
npx skills add https://github.com/levnikolaevich/claude-code-skills --skill ln-641-pattern-analyzer
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-641-pattern-analyzer

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、检索和筛选相关信息,适合根据关键词快速定位候选结果。适用于需要结合来源仓库、安装命令和原始 README 核验具体用法的场景。使用时需确认权限范围和维护状态,避免触发不必要的联网或文件读写。安装前建议评估是否会执行命令或访问外部资源,确保符合安全策略。

适用于 Codex、Claude、Cursor、Gemini CLI 等宿主环境中的研究检索任务。

来源仓库内路径:skills/ln-641-pattern-analyzer

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-6…;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-641-pattern-analyzer 怎么安装?

在终端执行 npx skills add https://github.com/levnikolaevich/claude-code-skills --skill ln-641-pattern…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

ln-641-pattern-analyzer 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-641-pattern-analyzer 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-641-pattern-analyzer 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-641-pattern-analyzer 用的人多吗?

公开数据是累计安装 6,030 次,近一周安装 259 次,下载 2,113 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-641-pattern-analyzer 安全吗?

第三方扫描有未通过项,风险较高。除非你确认过源码,否则不建议直接安装到常用环境。

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-641-pattern-analyzer

ln 641 模式分析仪 · 本页

6,030437调用外部服务未明确未通过

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. If not found at CWD, locate this SKILL.md directory and go up one level for repo root. If shared/ is missing, fetch files via WebFetch from https://raw.githubusercontent.com/levnikolaevich/claude-code-skills/master/skills/{path}.

Pattern Analyzer

Type: L3 Worker

L3 Worker that analyzes a single architectural pattern against best practices and calculates 4 scores.

Purpose & Scope

  • Analyze ONE pattern per invocation (receives pattern name, locations, best practices from coordinator)
  • Find all implementations in codebase (Glob/Grep)
  • Validate implementation exists and works
  • Calculate 4 scores: compliance, completeness, quality, implementation
  • Identify gaps and issues with severity and effort estimates
  • Return structured analysis result to coordinator

Out of Scope (owned by ln-624-code-quality-auditor):

  • Cyclomatic complexity thresholds (>10, >20)
  • Method/class length thresholds (>50, >100, >500 lines)
  • Quality Score focuses on pattern-specific quality (SOLID within pattern, pattern-level smells), not generic code metrics

Inputs

- pattern: string          # Pattern name (e.g., "Job Processing")
- locations: string[]      # Known file paths/directories
- bestPractices: object    # Best practices from MCP Ref/Context7/WebSearch
- output_dir: string       # e.g., ".hex-skills/runtime-artifacts/runs/{run_id}/audit-report"
Note: All patterns arrive pre-verified (passed ln-640 Phase 1d applicability gate with >= 2 structural components confirmed).

Workflow

MANDATORY READ: Load shared/references/two_layer_detection.md for detection methodology. MANDATORY READ: Load shared/references/mcp_tool_preferences.md and shared/references/mcp_integration_patterns.md

Use hex-graph first when implementation discovery materially improves confidence. Use hex-line first for local code reads when available. If MCP is unavailable, unsupported, or not indexed, continue with built-in Read/Grep/Glob/Bash and state the fallback in the report.

Phase 1: Find Implementations

MANDATORY READ: Load ../ln-640-pattern-evolution-auditor/references/pattern_library.md -- use "Pattern Detection (Grep)" table for detection keywords per pattern.

IF pattern.source == "adaptive":
  # Pattern discovered by coordinator Phase 1b -- evidence already provided
  files = pattern.evidence.files
  SKIP detection keyword search (already done in Phase 1b)
ELSE:
  # Baseline pattern -- use library detection keywords
  files = Glob(locations)
  additional = Grep("{pattern_keywords}", "**/*.{ts,js,py,rb,cs,java}")
  files = deduplicate(files + additional)

Phase 2: Read and Analyze Code

FOR EACH file IN files (limit: 10 key files):
  Read(file)
  Extract: components, patterns, error handling, logging, tests

Phase 3: Calculate 4 Scores

MANDATORY READ: Load ../ln-640-pattern-evolution-auditor/references/scoring_rules.md -- follow Detection column for each criterion.

ScoreSource in scoring_rules.mdMax
Compliance"Compliance Score" section -- industry standard, naming, conventions, anti-patterns100
Completeness"Completeness Score" section -- required components table (per pattern), error handling, tests100
Quality"Quality Score" section -- method length, complexity, code smells, SOLID100
Implementation"Implementation Score" section -- compiles, production usage, integration, monitoring100

Scoring process for each criterion:

  1. Run the Detection Grep/Glob from scoring_rules.md
  2. If matches found -> add points per criterion
  3. If anti-pattern/smell detected -> subtract per deduction table
  4. Document evidence: file path + line for each score justification

Phase 4: Identify Issues and Gaps

FOR EACH bestPractice NOT implemented:
  issues.append({
    severity: "HIGH" | "MEDIUM" | "LOW",
    category: "compliance" | "completeness" | "quality" | "implementation",
    issue: description,
    suggestion: how to fix,
    effort: "S" | "M" | "L"
  })

# Layer 2 context check (MANDATORY):
# Deviation documented in code comment or ADR? -> downgrade to LOW
# Pattern intentionally simplified for project scale? -> skip

gaps = {
  missingComponents: required components not found in code,
  inconsistencies: conflicting or incomplete implementations
}

Phase 5: Calculate Score

MANDATORY READ: Load shared/references/audit_worker_core_contract.md and shared/references/audit_scoring.md.

Diagnostic sub-scores (0-100 each) are calculated separately and reported in AUDIT-META for diagnostic purposes only:

  • compliance, completeness, quality, implementation

Phase 6: Write Report

MANDATORY READ: Load shared/references/audit_worker_core_contract.md and shared/templates/audit_worker_report_template.md.

Write JSON summary per shared/references/audit_summary_contract.md. In managed mode the caller passes both runId and summaryArtifactPath; in standalone mode the worker generates its own run-scoped artifact path per shared contract.

# Build pattern name slug: "Job Processing" -> "job-processing"
slug = pattern.name.lower().replace(" ", "-")

# Build markdown report in memory with:
# - AUDIT-META (extended: score [penalty-based] + diagnostic score_compliance/completeness/quality/implementation)
# - Checks table (compliance_check, completeness_check, quality_check, implementation_check)
# - Findings table (issues sorted by severity)
# - DATA-EXTENDED: {pattern, codeReferences, gaps, recommendations}

Write to {output_dir}/ln-641--{slug}.md (atomic single Write call)

Phase 7: Return Summary

Report written: .hex-skills/runtime-artifacts/runs/{run_id}/audit-report/ln-641--job-processing.md
Score: 7.9/10 (C:72 K:85 Q:68 I:90) | Issues: 3 (H:1 M:2 L:0)

Critical Rules

MANDATORY READ: Load shared/references/audit_worker_core_contract.md.

  • One pattern only: Analyze only the pattern passed by coordinator
  • Read before score: Never score without reading actual code
  • Detection-based scoring: Use Grep/Glob patterns from scoring_rules.md, not assumptions
  • Effort estimates: Always provide S/M/L for each issue
  • Code references: Always include file paths for findings

Definition of Done

MANDATORY READ: Load shared/references/audit_worker_core_contract.md.

  • All implementations found via Glob/Grep (using pattern_library.md keywords or adaptive evidence)
  • Key files read and analyzed
  • 4 scores calculated using scoring_rules.md Detection patterns
  • Issues identified with severity, category, suggestion, effort
  • Gaps documented (missing components, inconsistencies)
  • Recommendations provided
  • Report written to {output_dir}/ln-641--{slug}.md (atomic single Write call)
  • Summary written per contract

Reference Files

  • Scoring rules: ../ln-640-pattern-evolution-auditor/references/scoring_rules.md
  • Pattern library: ../ln-640-pattern-evolution-auditor/references/pattern_library.md
  • MANDATORY READ: Load shared/references/research_tool_fallback.md

Version: 2.0.0 Last Updated: 2026-02-08

平台分布

Codex

36.09%
按下载量换算763

Claude

29.94%
按下载量换算633

Cursor

19.04%
按下载量换算402

Gemini CLI

10.06%
按下载量换算213

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

未通过

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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