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codeprobe-performance代码探针性能

Agent Skill

codeprobe-performance 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。

总安装

674

周安装

27

GitHub Stars

4

下载量

218
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

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:codeprobe-performance(代码探针性能)
来源仓库:https://github.com/nishilbhave/codeprobe
仓库路径:skills/codeprobe-performance
安装命令:
npx skills add https://github.com/nishilbhave/codeprobe --skill codeprobe-performance
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/nishilbhave/codeprobe --skill codeprobe-performance

简介

codeprobe-performance 审计 N+1 查询、缺失索引与无界数据加载等性能瓶颈。

  • 适用于数据库访问层、API 接口与批量处理逻辑的效率优化。
  • 提供索引建议、分页策略与缓存提示,降低响应时间开销。
  • 安装前应核实是否允许读取 SQL 语句与 ORM 映射配置。
  • 建议在测试环境先行验证优化效果,防止生产环境出现意外降级。

SKILL.md

Standalone Mode

If invoked directly (not via the orchestrator), you must first:

  1. Read ../codeprobe/shared-preamble.md for the output contract, execution modes, and constraints.
  2. Load applicable reference files from ../codeprobe/references/ based on the project's tech stack.
  3. Default to full mode unless the user specifies otherwise.

Performance & Scalability Auditor

Domain Scope

This sub-skill detects performance and scalability issues across these categories:

  1. N+1 Queries — Lazy-loading relationships inside loops
  2. Missing Indexes — WHERE/ORDER BY on non-indexed columns
  3. Unbounded Queries — Model::all() without pagination/limit
  4. Memory — Loading entire files into memory, array accumulation in loops
  5. Caching — Repeated identical queries, missing TTL, stale cache after writes
  6. Algorithmic Efficiency — O(n^2) in hot paths, nested loops, sorting in loops
  7. Concurrency — Race conditions, non-idempotent queue jobs, shared mutable state
  8. Frontend Performance — Unnecessary re-renders, bundle size, missing lazy loading

What It Does NOT Flag

  • Premature optimization in non-hot-path code — proportional design matters. A utility function called once at startup doesn't need the same optimization as a request handler.
  • Development-only debug queries — queries in seeders, dev-only commands, or debug endpoints.
  • Batch processing scripts — scripts intentionally designed to process everything (migrations, data backfills) where unbounded queries may be appropriate.
  • O(n^2) on small bounded collections (<100 items) — nested loops on small known-size arrays are fine.
  • Frontend SSR/build-time code — server components and build scripts have different performance profiles than client-side code.

Detection Instructions

N+1 Queries

ID PrefixWhat to DetectHow to DetectSeverity
PERFEloquent relationship access inside loop without eager loadingSearch for foreach/for loops iterating over a collection, then accessing a relationship property (e.g., $order->items, $user->profile) inside the loop body. Check whether the query that produced the collection includes with() or load() for that relationship.Critical
PERFAny ORM lazy-loading inside iterationLook for patterns where a database query is implicitly triggered inside a loop: Django querysets accessed per-iteration, SQLAlchemy lazy loads, Prisma relation access in .map().Critical
PERFTemplate/view triggering queriesBlade templates, Jinja2 templates, or React components calling relationship properties that trigger queries during rendering.Major

Missing Indexes

ID PrefixWhat to DetectHow to DetectSeverity
PERFWHERE/ORDER BY on non-indexed columnsCross-reference query conditions (where(), orderBy(), WHERE, ORDER BY) with migration files or schema definitions to check for matching indexes.Major
PERFForeign keys without indexesCheck migration files for foreignId(), foreign(), references() without corresponding index definitions. Most ORMs add these automatically, but raw migrations may miss them.Minor
PERFComposite queries needing compound indexesMultiple where() conditions on different columns in the same query, or where() + orderBy() combinations that would benefit from a compound index.Minor

Unbounded Queries

ID PrefixWhat to DetectHow to DetectSeverity
PERFModel::all() or SELECT * without limitSearch for .all(), ::all(), findAll(), SELECT * FROM without LIMIT, paginate(), take(), or limit().Major
PERFMissing cursor/chunk for large dataset processingOperations that get() or load entire collections when processing large datasets. Should use cursor(), chunk(), lazy(), or equivalent streaming approach.Major

Memory

ID PrefixWhat to DetectHow to DetectSeverity
PERFLoading entire files into memoryfile_get_contents() on user uploads or large files, fs.readFileSync() on variable-size files, Python open().read() without size limits.Major
PERFArray accumulation in loopsArrays that grow inside loops without bounds or cleanup — collecting results in memory that could be streamed or yielded.Minor
PERFLarge collections instead of generatorsReturning full arrays/lists where generators (yield), lazy collections, or iterators would be more memory-efficient for large datasets.Minor

Caching

ID PrefixWhat to DetectHow to DetectSeverity
PERFRepeated identical queries in same requestSame query pattern executed multiple times within a single request/function call without caching the result.Minor
PERFCache without TTLCache::put(), cache.set(), Redis SET without expiration. Data cached forever risks staleness.Minor
PERFCache not invalidated after writesWrite operations (create/update/delete) that don't clear or update related cache entries. Stale cache served after mutation.Major

Algorithmic Efficiency

ID PrefixWhat to DetectHow to DetectSeverity
PERFO(n^2) or worse in hot pathsNested loops iterating over the same or related collections. array_search/in_array/includes() inside loops (linear search in a loop = O(n^2)).Major
PERFSorting inside loopssort(), usort(), array_sort(), .sort() called inside a loop body.Major
PERFHashmap-replaceable linear searchin_array(), .includes(), .indexOf(), list.index() used repeatedly on the same array where building a Set/dict/hashmap first would be O(1) per lookup.Minor

Concurrency

ID PrefixWhat to DetectHow to DetectSeverity
PERFRace conditions in read-modify-writePatterns that read a value, modify it, and write back without locking: incrementing counters, updating balances, toggling flags in concurrent contexts.Critical
PERFQueue jobs without idempotencyQueue/job handlers that don't check for duplicate execution. Jobs that create resources without checking if already created. Missing unique constraints on job-created data.Major
PERFShared mutable state in async contextsGlobal/module-level mutable variables accessed in async handlers, request-scoped data stored in module scope.Major

Frontend Performance

ID PrefixWhat to DetectHow to DetectSeverity
PERFUnnecessary re-rendersMissing React.memo, useMemo, useCallback on expensive computations or components receiving new object/array references on every render. Objects/arrays created inline in JSX props.Minor
PERFLarge bundle importsimport _ from 'lodash' (imports entire library), import moment from 'moment' (large library where date-fns or dayjs suffice), import * as icons from 'icon-library'.Minor
PERFMissing lazy loadingNo React.lazy() / dynamic import() for route-level code splitting. Heavy components loaded eagerly on initial page load.Minor

Optional Script Integration

When scripts/complexity_scorer.py output is available (run by the orchestrator during /codeprobe audit), use it to identify high-complexity functions as performance hot-spot candidates. Functions rated "high" or "very_high" that also appear in hot paths (request handlers, loop bodies, frequently-called utilities) are strong signals for algorithmic efficiency findings.


ID Prefix & Fix Prompt Examples

All findings use the PERF- prefix, numbered sequentially: PERF-001, PERF-002, etc.

Fix Prompt Examples

  • "In OrderController@index (line 22), add ->with('items', 'customer') to the Order::query() call to fix the N+1 problem — currently loading 2 relations lazily inside the Blade loop at orders/index.blade.php:15."
  • "Replace Product::all() at line 30 of CatalogService.php with Product::query()->paginate(25) or Product::cursor() if processing all records. The current query loads all products into memory."
  • "In ReportGenerator@aggregate (lines 45-60), the nested loop iterating $orders inside $customers is O(n*m). Build a lookup hashmap before the outer loop: $ordersByCustomerId = collect($orders)->groupBy('customer_id')."
  • "Replace import _ from 'lodash' at line 3 of src/utils/helpers.ts with specific imports: import debounce from 'lodash/debounce' to reduce bundle size."

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

37.73%
按下载量换算82

Claude

31.69%
按下载量换算69

Cursor

16.59%
按下载量换算36

Gemini CLI

10%
按下载量换算22

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

可写文件

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安装前确认

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来源信息

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