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Agent Skill · 研究检索

ln-651-query-efficiency-auditor(ln 651 查询效率审核员)

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-651-query-efficiency-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 17,600。

总安装 6,512周安装 266GitHub Stars 437下载 2,085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-651-query-efficiency-auditor(ln 651 查询效率审核员)
来源仓库:https://github.com/levnikolaevich/claude-code-skills
仓库路径:skills/ln-651-query-efficiency-auditor
安装命令:
npx skills add https://github.com/levnikolaevich/claude-code-skills --skill ln-651-query-efficiency-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。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-651-query-efficiency-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。

用于辅助安全审计、权限检查和常见漏洞排查。适合梳理敏感配置、检查依赖风险或分析鉴权逻辑。使用时不能直接采信工具输出,需结合最小权限和脱敏要求确认操作边界。涉及密钥或生产系统时,应先评估权限范围和影响范围。建议配合人工复核生成安全复核清单。

ln-651-query-efficiency-auditor 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-651-query-efficiency-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-651-query-efficiency-auditor 怎么安装?

在终端执行 npx skills add https://github.com/levnikolaevich/claude-code-skills --skill ln-651-query-e…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

ln-651-query-efficiency-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-651-query-efficiency-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-651-query-efficiency-auditor 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-651-query-efficiency-auditor 用的人多吗?

公开数据是累计安装 6,512 次,近一周安装 266 次,下载 2,085 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-651-query-efficiency-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。

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-651-query-efficiency-auditor

ln 651 查询效率审核员 · 本页

6,512437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. 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}.

Query Efficiency Auditor (L3 Worker)

Type: L3 Worker

Specialized worker auditing database query patterns for redundancy, inefficiency, and misuse.

Purpose & Scope

  • Audit query efficiency (Priority: HIGH)
  • Check redundant fetches, batch operation misuse, caching scope problems
  • Write structured findings to file with severity, location, effort, recommendations
  • Calculate compliance score (X/10) for Query Efficiency 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, db_config (database type, ORM settings), codebase_root, output_dir.

Domain-aware: Supports domain_mode + current_domain.

Use hex-graph first when reference chains or call paths materially improve query analysis. Use hex-line first for local code/config 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 from contextStore

- Extract tech_stack, best_practices, db_config, output_dir - Determine scan_path (same logic as ln-624)

  1. Scan codebase for violations

- All Grep/Glob patterns use scan_path - Trace call chains for redundant fetches (requires reading caller + callee)

  1. Collect findings with severity, location, effort, recommendation
  2. Calculate score using penalty algorithm
  3. Write Report: Build full markdown report in memory per shared/templates/audit_worker_report_template.md, write to {output_dir}/ln-651--global.md in single Write call
  4. Return Summary: Return minimal summary to coordinator (see Output Format)

Audit Rules (Priority: HIGH)

1. Redundant Entity Fetch

What: Same entity fetched from DB twice in a call chain

Detection:

  • Find function A that calls repo.get(id) or session.get(Model, id), then passes id (not object) to function B
  • Function B also calls repo.get(id) or session.get(Model, id) for the same entity
  • Common pattern: acquire_next_pending() returns job, but _process_job(job_id) re-fetches it

Detection patterns (Python/SQLAlchemy):

  • Grep for repo.*get_by_id|session\.get\(|session\.query.*filter.*id in service/handler files
  • Trace: if function receives entity_id: int/UUID AND internally does repo.get(entity_id), check if caller already has entity object
  • Check expire_on_commit setting: if False, objects remain valid after commit

Severity:

  • HIGH: Redundant fetch in API request handler (adds latency per request)
  • MEDIUM: Redundant fetch in background job (less critical)
  • Downgrade when: Fetch in initialization/migration code (runs once) -> LOW. Admin-only endpoint with low traffic -> downgrade one level

Recommendation: Pass entity object instead of ID, or remove second fetch when expire_on_commit=False

Effort: S (change signature to accept object instead of ID)

2. N-UPDATE/DELETE Loop

What: Loop of individual UPDATE/DELETE operations instead of single batch query

Detection:

  • Pattern: for item in items: await repo.update(item.id,...) or for item in items: await repo.delete(item.id)
  • Pattern: for item in items: session.execute(update(Model).where(...))

Detection patterns:

  • Grep for for.* in.*: followed by repo\.(update|delete|reset|save|mark_) within 1-3 lines
  • Grep for for.* in.*: followed by session\.execute\(.*update\( within 1-3 lines

Severity:

  • HIGH: Loop over >10 items (N separate round-trips to DB)
  • MEDIUM: Loop over <=10 items
  • Downgrade when: Loop in bootstrap/migration code (runs once) -> LOW. Admin-only endpoint -> downgrade one level

Recommendation: Replace with single UPDATE... WHERE id IN (...) or session.execute(update(Model).where(Model.id.in_(ids)))

Effort: M (rewrite query + test)

3. Unnecessary Resolve

What: Re-resolving a value from DB when it is already available in the caller's scope

Detection:

  • Method receives profile_id and resolves engine from it, but caller already determined engine
  • Method receives lang_code and looks up dialect_id, but caller already has both lang and dialect
  • Pattern: function receives X_id, does get(X_id), extracts .field, when caller already has field

Severity:

  • MEDIUM: Extra DB query per invocation, especially in high-frequency paths

Recommendation: Split method into two variants: with_known_value(value,...) and resolving_value(id,...); or pass resolved value directly

Effort: S-M (refactor signature, update callers)

4. Over-Fetching

What: Loading full ORM model when only few fields are needed

Detection:

  • session.query(Model) or select(Model) without .options(load_only(...)) for models with >10 columns
  • Especially in list/search endpoints that return many rows
  • Pattern: loading full entity but only using 2-3 fields

Severity:

  • MEDIUM: Large models (>15 columns) in list endpoints
  • LOW: Small models (<10 columns) or single-entity endpoints

Recommendation: Use load_only(), defer(), or raw select(Model.col1, Model.col2) for list queries

Effort: S (add load_only to query)

5. Missing Bulk Operations

What: Sequential INSERT/DELETE/UPDATE instead of bulk operations

Detection:

  • for item in items: session.add(item) instead of session.add_all(items)
  • for item in items: session.delete(item) instead of bulk delete
  • Pattern: loop with single INSERT per iteration

Severity:

  • MEDIUM: Any sequential add/delete in loop (missed batch optimization)

Recommendation: Use session.add_all(), session.execute(insert(Model).values(list_of_dicts)), bulk_save_objects()

Effort: S (replace loop with bulk call)

6. Wrong Caching Scope

What: Request-scoped cache for data that rarely changes (should be app-scoped)

Detection:

  • Service registered as request-scoped (e.g., via FastAPI Depends()) with internal cache (_cache dict, _loaded flag)
  • Cache populated by expensive query (JOINs, aggregations) per each request
  • Data TTL >> request duration (e.g., engine configurations, language lists, feature flags)

Detection patterns:

  • Find classes with _cache, _loaded, _initialized attributes
  • Check if class is created per-request (via DI registration scope)
  • Compare: data change frequency vs cache lifetime

Severity:

  • HIGH: Expensive query (JOINs, subqueries) cached only per-request
  • MEDIUM: Simple query cached per-request

Recommendation: Move cache to app-scoped service (singleton), add TTL-based invalidation, or use CacheService with configurable TTL

Effort: M (change DI scope, add TTL logic)

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-651--global.md with category: "Query Efficiency" and checks: redundant_fetch, n_update_delete_loop, unnecessary_resolve, over_fetching, missing_bulk_ops, wrong_caching_scope.

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-651--global.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
  • Trace call chains: Rules 1 and 3 require reading both caller and callee
  • ORM-aware: Check expire_on_commit, autoflush, session scope before flagging redundant fetches
  • Context-aware: Small datasets or infrequent operations may justify simpler code
  • Exclude tests: Do not flag test fixtures or setup code

Definition of Done

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

  • contextStore parsed successfully (including output_dir)
  • scan_path determined (domain path or codebase root)
  • All 6 checks completed:

- redundant fetch, N-UPDATE loop, unnecessary resolve, over-fetching, bulk ops, caching scope

  • Findings collected with severity, location, effort, recommendation
  • Score calculated using penalty algorithm
  • Report written to {output_dir}/ln-651--global.md (atomic single Write call)
  • Summary written per contract

Reference Files

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

Version: 1.0.0 Last Updated: 2026-02-04

平台分布

Codex

34.49%
按下载量换算719

Claude

31.45%
按下载量换算656

Cursor

21%
按下载量换算438

Gemini CLI

9.88%
按下载量换算206

安全审计

Gen Agent Trust Hub

通过

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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