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Agent Skill · 研究检索

ln-624-code-quality-auditor(ln 624 代码质量审核员)

用于辅助安全审计、权限检查、凭据风险、认证流程和常见漏洞排查。它适合让 Agent 梳理敏感配置、检查依赖风险、分析鉴权逻辑或生成安全复核清单。使用时不能把工具输出直接当最终结论,涉及密钥、令牌、用户数据或生产系统时,应先确认最小权限、脱敏方式和操作边界。

ln-624-code-quality-auditor 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;许可证为 MIT,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 13,703。

总安装 9,156周安装 374GitHub Stars 437下载 2,932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
MIT

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-624-code-quality-auditor(ln 624 代码质量审核员)
来源仓库:https://github.com/levnikolaevich/claude-code-skills
仓库路径:skills/ln-624-code-quality-auditor
安装命令:
npx skills add https://github.com/levnikolaevich/claude-code-skills --skill ln-624-code-quality-auditor
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。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/levnikolaevich/claude-code-skills --skill ln-624-code-quality-auditor

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。

用于安全审计、权限检查和常见漏洞排查,适合梳理敏感配置和鉴权逻辑。适用于需要分析凭据风险或生成安全复核清单的场景。使用时不能把工具输出直接当最终结论,需先确认最小权限和操作边界。涉及密钥、令牌或用户数据时应脱敏处理,避免影响生产系统。安装前建议确认权限范围和维护状态,确保不会触发不必要的网络或文件操作。

来源仓库内路径:skills/ln-624-code-quality-auditor

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-624-code-quality-auditor 怎么安装?

在终端执行 npx skills add https://github.com/levnikolaevich/claude-code-skills --skill ln-624-code-qu…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

ln-624-code-quality-auditor 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-624-code-quality-auditor 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-624-code-quality-auditor 是免费的吗?

来源标注的许可证是 MIT。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

ln-624-code-quality-auditor 用的人多吗?

公开数据是累计安装 9,156 次,近一周安装 374 次,下载 2,932 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-624-code-quality-auditor 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。
  • 3 个目录同时收录,说明这个 Skill 流传比较广。
  • 许可证明确为 MIT,商用和分发边界好判断。

先想清楚的地方

  • 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-624-code-quality-auditor

ln 624 代码质量审核员 · 本页

9,156437调用外部服务MIT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. 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}.

Code Quality Auditor (L3 Worker)

Type: L3 Worker

Specialized worker auditing code complexity, method signatures, algorithms, and constants management.

Purpose & Scope

  • Audit code quality (Categories 5+6+NEW: Medium Priority)
  • Check complexity metrics, method signature quality, algorithmic efficiency, constants management
  • Return structured findings with severity, location, effort, recommendations
  • Calculate compliance score (X/10) for Code Quality category

Inputs

MANDATORY READ: Load shared/references/audit_worker_core_contract.md. MANDATORY READ: Load shared/references/mcp_tool_preferences.md and shared/references/mcp_integration_patterns.md

Receives contextStore with: tech_stack, best_practices, principles, codebase_root, output_dir.

Domain-aware: Supports domain_mode + current_domain (see audit_output_schema.md#domain-aware-worker-output).

Use hex-graph first when hotspots, architecture coupling, or semantic relationships materially improve the audit. 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.

Workflow

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

  1. Parse context -- extract fields, determine scan_path (domain-aware if specified), extract output_dir
  2. Scan codebase for violations (Layer 1)

- All Grep/Glob patterns use scan_path (not codebase_root) - Graph acceleration (if available): IF contextStore.graph_indexed OR .hex-skills/codegraph/index.db exists: - Complexity + God classes: audit_workspace(path=scan_path, verbosity="minimal", limit=5) -- use returned hotspots to pre-identify complex functions and god classes. Raise limit only for deliberate drill-down. - Module metrics: analyze_architecture(path=scan_path, verbosity="full") -- use returned coupling metrics for cascade depth and coupling analysis. - Fall back to grep patterns below if graph unavailable. - Outline-first read: outline(file_path) before reading large source files -- understand function/class structure for complexity analysis. - Example: Grep(pattern="if.*if.*if", path=scan_path) for nesting detection

  1. Analyze context per candidate (Layer 2 -- MANDATORY) Layer 1 finding without Layer 2 = NOT a valid finding. Before reporting, ask: "Is this violation intentional or justified by design?"

- Cyclomatic complexity: is complexity from switch/case on enum (valid) or deeply nested conditions (bad)? Enum dispatch -> downgrade to LOW or skip - O(n^2): read context -- what's n? If bounded (n < 100), downgrade severity - N+1: read ORM config -- does it have eager loading configured elsewhere? Admin-only endpoint -> downgrade severity - God class: is it a config/schema/builder class? -> downgrade - Cascade depth: already traces calls (implicit Layer 2). Orchestrator function -> SEB does NOT apply (see Conflict Resolution in ARCH-AI-SEB)

  1. Collect findings with severity, location, effort, recommendation

- Tag each finding with domain: domain_name (if domain-aware)

  1. Calculate score using penalty algorithm
  2. Write Report: Build full markdown report in memory per shared/templates/audit_worker_report_template.md, write to {output_dir}/ln-624--{domain}.md (or 624-quality.md in global mode) in single Write call
  3. Return Summary: Return minimal summary to coordinator (see Output Format)

Audit Rules (Priority: MEDIUM)

1. Cyclomatic Complexity

What: Too many decision points in single function (> 10)

Detection:

  • Count if/else, switch/case, ternary, &&, ||, for, while
  • Use tools: eslint-plugin-complexity, radon (Python), gocyclo (Go)

Severity:

  • HIGH: Complexity > 20 (extremely hard to test)
  • MEDIUM: Complexity 11-20 (refactor recommended)
  • LOW: Complexity 8-10 (acceptable but monitor)
  • Downgrade when: Enum/switch dispatch, state machines, parser grammars -> downgrade to LOW or skip

Recommendation: Split function, extract helper methods, use early returns

Effort: M-L (depends on complexity)

2. Deep Nesting (> 4 levels)

What: Nested if/for/while blocks too deep

Detection:

  • Count indentation levels
  • Pattern: if {if {if {if {if {...}}}}}

Severity:

  • HIGH: > 6 levels (unreadable)
  • MEDIUM: 5-6 levels
  • LOW: 4 levels
  • Downgrade when: Nesting from early-return guard clauses (structurally deep but linear logic) -> downgrade

Recommendation: Extract functions, use guard clauses, invert conditions

Effort: M (refactor structure)

3. Long Methods (> 50 lines)

What: Functions too long, doing too much

Detection:

  • Count lines between function start and end
  • Exclude comments, blank lines

Severity:

  • HIGH: > 100 lines
  • MEDIUM: 51-100 lines
  • LOW: 40-50 lines (borderline)
  • Downgrade when: Orchestrator functions with sequential delegation; data transformation pipelines -> downgrade

Recommendation: Split into smaller functions, apply Single Responsibility

Effort: M (extract logic)

4. God Classes/Modules (> 500 lines)

What: Files with too many responsibilities

Detection:

  • Count lines in file (exclude comments)
  • Check number of public methods/functions

Severity:

  • HIGH: > 1000 lines
  • MEDIUM: 501-1000 lines
  • LOW: 400-500 lines
  • Downgrade when: Config/schema/migration files, generated code, barrel/index files -> skip

Recommendation: Split into multiple files, apply separation of concerns

Effort: L (major refactor)

5. Too Many Parameters (> 5)

What: Functions with excessive parameters

Detection:

  • Count function parameters
  • Check constructors, methods

Severity:

  • MEDIUM: 6-8 parameters
  • LOW: 5 parameters (borderline)
  • Downgrade when: Builder/options pattern constructor; framework-required signatures (middleware, hooks) -> skip

Recommendation: Use parameter object, builder pattern, default parameters

Effort: S-M (refactor signature + calls)

6. O(n^2) or Worse Algorithms

What: Inefficient nested loops over collections

Detection:

  • Nested for loops: for (i) {for (j) {...}}
  • Nested array methods: arr.map(x => arr.filter(...))

Severity:

  • HIGH: O(n^2) in hot path (API request handler)
  • MEDIUM: O(n^2) in occasional operations
  • LOW: O(n^2) on small datasets (n < 100)
  • Downgrade when: Bounded n (n < 100 guaranteed by domain); one-time init/migration code -> downgrade to LOW or skip

Recommendation: Use hash maps, optimize with single pass, use better data structures

Effort: M (algorithm redesign)

7. N+1 Query Patterns

What: ORM lazy loading causing N+1 queries

Detection:

  • Find loops with database queries inside
  • Check ORM patterns: users.forEach(u => u.getPosts())

Severity:

  • CRITICAL: N+1 in API endpoint (performance disaster)
  • HIGH: N+1 in frequent operations
  • MEDIUM: N+1 in admin panel
  • Downgrade when: Admin-only endpoint called <=1x/day -> downgrade to LOW. Eager loading configured elsewhere in ORM -> skip

Recommendation: Use eager loading, batch queries, JOIN

Effort: M (change ORM query)

8. Constants Management (NEW)

What: Magic numbers/strings, decentralized constants, duplicates

Detection:

IssuePatternExample
Magic numbersHardcoded numbers in conditions/calculationsif (status === 2)
Magic stringsHardcoded strings in comparisonsif (role === 'admin')
DecentralizedConstants scattered across filesMAX_SIZE = 100 in 5 files
DuplicatesSame value multiple timesSTATUS_ACTIVE = 1 in 3 places
No central fileMissing constants.ts or config.pyNo single source of truth

Severity:

  • HIGH: Magic numbers in business logic (payment amounts, statuses)
  • MEDIUM: Duplicate constants (same value defined 3+ times)
  • MEDIUM: No central constants file
  • LOW: Magic strings in logging/debugging
  • Downgrade when: HTTP status codes (200, 404, 500) -> skip. Math constants (0, 1, -1) in algorithms -> skip. Test data -> skip

Recommendation:

  • Create central constants file (constants.ts, config.py, constants.go)
  • Extract magic numbers to named constants: const STATUS_ACTIVE = 1
  • Consolidate duplicates, import from central file
  • Use enums for related constants

Effort: M (extract constants, update imports, consolidate)

9. Method Signature Quality

What: Poor method contracts reducing readability and maintainability

Detection:

IssuePatternExample
Boolean flag params>=2 boolean params in signaturedef process(data, is_async: bool, skip_validation: bool)
Too many optional params>=3 optional params with defaultsdef query(db, limit=10, offset=0, sort="id", order="asc")
Inconsistent verb namingDifferent verbs for same operation type in one moduleget_user() vs fetch_account() vs load_profile()
Unclear return type-> dict, -> Any, -> tuple without TypedDict/NamedTupledef get_stats() -> dict instead of -> StatsResponse

Severity:

  • MEDIUM: Boolean flag params (use enum/strategy), unclear return types
  • LOW: Too many optional params, inconsistent naming

Recommendation:

  • Boolean flags: replace with enum, strategy pattern, or separate methods
  • Optional params: group into config/options dataclass
  • Naming: standardize verb conventions per module (get_ for sync, fetch_ for async, etc.)
  • Return types: use TypedDict, NamedTuple, or dataclass instead of raw dict/tuple

Effort: S-M (refactor signatures + callers)

10. Side-Effect Cascade Depth

What: Functions triggering cascading chains of external side-effects (DB writes -> notifications -> metrics -> limits).

Detection: MANDATORY READ: Load shared/references/ai_ready_architecture.md for side-effect markers, false positive exclusions, and opaque sink rules.

  • Glob **/services/**/*.{py,ts,js,cs,java} to find service files
  • For each public function: check body for side-effect markers (per reference)
  • Recursively follow called internal functions for additional markers
  • Calculate max chain depth from entry point

Severity:

  • HIGH: cascade_depth >= 4
  • MEDIUM: cascade_depth = 3
  • OK: depth <= 2
  • Downgrade when: Orchestrator/coordinator functions (imports 3+ services AND delegates sequentially) -> skip. Depth from opaque sinks (logging, metrics) -> skip

Conflict Resolution: IF function is an orchestrator/coordinator (imports 3+ services AND delegates to them sequentially) -> ARCH-AI-SEB does NOT apply. Orchestrators are EXPECTED to have multiple side-effect categories. Only flag SEB for leaf functions.

Recommendation: Refactor to flat orchestration -- extract side-effects into independent sink functions. See reference.

Effort: M-L

Output: Also generate summary Pipe/Sink table per module:

ModuleSinks (0-1)Shallow Pipes (2)Deep Pipes (3+)Sink Ratio

Scoring Algorithm

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

Output Format

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.

Write report to {output_dir}/ln-624--{domain}.md (or 624-quality.md in global mode) with category: "Code Quality" and checks: cyclomatic_complexity, deep_nesting, long_methods, god_classes, too_many_params, quadratic_algorithms, n_plus_one, magic_numbers, method_signatures, cascade_depth.

Return summary per shared/references/audit_summary_contract.md.

When summaryArtifactPath is absent, write the standalone runtime summary under .hex-skills/runtime-artifacts/runs/{run_id}/evaluation-worker/{worker}--{identifier}.json and optionally echo the same summary in structured output.

Report written: .hex-skills/runtime-artifacts/runs/{run_id}/audit-report/ln-624--orders.md
Score: X.X/10 | Issues: N (C:N H:N M:N L:N)

Critical Rules

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

  • Do not auto-fix: Report only
  • Domain-aware scanning: If domain_mode="domain-aware", scan ONLY scan_path (not entire codebase)
  • Tag findings: Include domain field in each finding when domain-aware
  • Context-aware: Small functions (n < 100) with O(n^2) may be acceptable
  • Constants detection: Exclude test files, configs, examples
  • Metrics tools: Use existing tools when available (ESLint complexity plugin, radon, gocyclo)

Definition of Done

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

  • contextStore parsed (including domain_mode, current_domain, output_dir)
  • scan_path determined (domain path or codebase root)
  • All 10 checks completed (scoped to scan_path):

- complexity, nesting, length, god classes, parameters, O(n^2), N+1, constants, method signatures, cascade depth

  • Findings collected with severity, location, effort, recommendation, domain
  • Score calculated
  • Report written to {output_dir}/ln-624--{domain}.md (atomic single Write call)
  • Summary written per contract

Reference Files

  • Audit output schema: shared/references/audit_output_schema.md

Version: 3.0.0 Last Updated: 2025-12-23

平台分布

Claude Code

30.56%
按下载量换算896

Gemini CLI

24.84%
按下载量换算728

Codex

18.74%
按下载量换算549

OpenCode

12.66%
按下载量换算371

Antigravity

8.19%
按下载量换算240

windsurf

3.94%
按下载量换算116

安全审计

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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。

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