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n-plus-one-detectorn 加 1 探测器

Agent Skill

n-plus-one-detector 用于查找、检索和筛选相关信息,适合在 OpenClaw 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

1,001

周安装

43

GitHub Stars

公开资料未说明

下载量

351
OpenClaw

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

MIT-0

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:n-plus-one-detector(n 加 1 探测器)
来源仓库:https://github.com/charlie-morrison/n-plus-one-detector
安装命令:
openclaw skills install n-plus-one-detector
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 OpenClaw 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。

ClawHubOpenClaw
openclaw skills install n-plus-one-detector

简介

n-plus-one-detector 检测代码与 ORM 中的 N+1 查询问题。

  • 分析数据库调用模式并提出优化建议。n-plus-one-detector 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 适用于性能瓶颈排查与高并发系统调优。
  • 需接入应用日志或 SQL 记录方可有效识别问题。
  • 修复方案应结合具体框架与数据模型评估。

SKILL.md

name
n-plus-one-detector
description
Detect N+1 query problems in application code and ORM usage. Analyze database query patterns, find loops that generate excessive queries, and recommend fixes using eager loading, joins, batch fetching, and DataLoader patterns.

N+1 Query Detector

Find the N+1 queries silently killing your application performance. Analyze ORM usage, spot loops generating redundant database queries, measure query counts per request, and recommend specific fixes — eager loading, joins, batch fetching, or DataLoader patterns.

Use when: "find N+1 queries", "why is this endpoint slow", "too many database queries", "ORM performance", "optimize queries", "database query count", or when a page makes hundreds of similar queries.

Commands

1. detect — Find N+1 Patterns in Code

Step 1: Identify ORM and Query Patterns

# Detect ORM in use
rg "from sqlalchemy|from django\.db|ActiveRecord|prisma|typeorm|sequelize|mongoose|gorm|ent\." \
  --type-not binary -g '!node_modules' -g '!vendor' --stats 2>&1

# Find model definitions
rg "class.*Model|@Entity|schema\.|model\s+\w+\s*\{" \
  --type-not binary -g '!node_modules' -g '!vendor' 2>/dev/null | head -30

Step 2: Static Analysis — Find Loop + Query Patterns

# Python (Django/SQLAlchemy) — access related objects in loops
rg -U "for\s+\w+\s+in\s+\w+.*:\s*\
.*\.\w+\.(all|filter|get|first|objects)" \
  --type py -g '!migrations' 2>/dev/null

# JavaScript/TypeScript (Prisma/TypeORM/Sequelize) — await in loop
rg -U "for.*of.*\{[\s\S]*?await.*\.(find|query|get|fetch)" \
  --type ts --type js -g '!node_modules' 2>/dev/null

# Ruby (ActiveRecord) — accessing association in loop
rg -U "\.each\s+do.*\
.*\.\w+\.(where|find|pluck)" \
  --type ruby 2>/dev/null

# Go (GORM/ent) — query in range loop
rg -U "for.*range.*\{[\s\S]*?\.Find\(|\.Where\(|\.First\(" \
  --type go 2>/dev/null

Step 3: Runtime Detection (if tests/dev server available)

# Django — enable query logging
DJANGO_DEBUG=1 python3 -c "
import django; django.setup()
from django.db import connection
from django.test.utils import override_settings

# Run the suspect view/function
# ...

queries = connection.queries
print(f'Total queries: {len(queries)}')

# Group by similar query pattern
from collections import Counter
patterns = Counter()
for q in queries:
    # Normalize: remove specific IDs
    import re
    pattern = re.sub(r'= \d+', '= ?', q['sql'])
    patterns[pattern] += 1

for pattern, count in patterns.most_common(10):
    if count > 1:
        print(f'  ⚠️  {count}x: {pattern[:120]}')
"

# Node.js — enable Prisma query logging
# Set DEBUG=prisma:query or use prisma.$on('query')

# Rails — enable query logging
# ActiveSupport::Notifications.subscribe("sql.active_record")

Step 4: Classify and Fix

For each N+1 found:

Pattern 1: Lazy-loaded relationship in loop

# BAD — N+1: 1 query for posts + N queries for authors
for post in Post.objects.all():
    print(post.author.name)  # Each .author triggers a query

# FIX — Eager load with select_related (FK) or prefetch_related (M2M)
for post in Post.objects.select_related('author').all():
    print(post.author.name)  # 1 query total

Pattern 2: Async query in loop

// BAD — N+1: awaiting individual queries
for (const userId of userIds) {
    const user = await prisma.user.findUnique({ where: { id: userId } });
}

// FIX — Batch query
const users = await prisma.user.findMany({ where: { id: { in: userIds } } });

Pattern 3: GraphQL resolver N+1

// BAD — resolver called per parent item
resolve(parent) {
    return db.query('SELECT * FROM comments WHERE post_id = ?', [parent.id]);
}

// FIX — DataLoader pattern
const commentLoader = new DataLoader(async (postIds) => {
    const comments = await db.query('SELECT * FROM comments WHERE post_id IN (?)', [postIds]);
    return postIds.map(id => comments.filter(c => c.post_id === id));
});
resolve(parent) { return commentLoader.load(parent.id); }

Step 5: Report

# N+1 Query Report

## Summary
- Files scanned: 45
- N+1 patterns found: 6
- Estimated excess queries per request: ~200-500

## Critical (high-traffic endpoints)
1. `api/views/orders.py:34` — Order list loads customer for each order
   - Current: 1 + N queries (N = page size, typically 50)
   - Fix: `Order.objects.select_related('customer')`
   - Impact: 50 queries → 1 query

2. `api/resolvers/post.ts:18` — Post resolver loads comments individually
   - Current: 1 + N queries per post listing
   - Fix: DataLoader for comments
   - Impact: N queries → 1 batched query

## Recommendations
1. Add `select_related`/`prefetch_related` to all list views
2. Implement DataLoader for GraphQL resolvers
3. Add query count assertions to integration tests:

with self.assertNumQueries(3): response = self.client.get('/api/orders/')

2. monitor — Add Query Count Guards

Generate test assertions or middleware that counts queries per request and fails when count exceeds threshold:

# Django middleware
class QueryCountMiddleware:
    def __call__(self, request):
        from django.db import connection
        initial = len(connection.queries)
        response = self.get_response(request)
        count = len(connection.queries) - initial
        if count > 20:  # threshold
            logger.warning(f'{request.path}: {count} queries')
        response['X-Query-Count'] = str(count)
        return response

3. benchmark — Measure Query Reduction Impact

Before and after applying fixes, measure:

  • Total query count per request
  • Response time improvement
  • Database load reduction

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

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

补充不同宿主或平台的使用分布数据

能力 5

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

OpenClaw

85.06%
按下载量换算299

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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