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Agent Skill · 研究检索

self-correction-loop(自校正循环)

self-correction-loop 用于记录任务执行中的错误、用户纠正、经验和能力缺口,适合在 Codex、Claude、Cursor、Gemini CLI 中希望让 Agent 持续沉淀问题、修正和最佳实践时使用。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。

self-correction-loop 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,权限为需要联网;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 50,332。

总安装 618周安装 25GitHub Stars 315下载 194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:self-correction-loop(自校正循环)
来源仓库:https://github.com/codewithmukesh/dotnet-claude-kit
仓库路径:skills/self-correction-loop
安装命令:
npx skills add https://github.com/codewithmukesh/dotnet-claude-kit --skill self-correction-loop
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/codewithmukesh/dotnet-claude-kit --skill self-correction-loop

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。

self-correction-loop 用于记录任务执行中的错误、纠正和经验沉淀。适合希望 Agent 持续改进问题处理和最佳实践的场景。通过 npx skills add 命令安装,建议结合原始 README 了解具体用法。安装前应确认权限范围和维护状态,避免触发文件读写或网络请求。

涉及经验存储时应注意数据边界和隐私保护。

来源仓库内路径:skills/self-correction-loop

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/codewithmukesh/dotnet-claude-kit --skill self-…;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

    它需要需要联网,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

self-correction-loop 怎么安装?

在终端执行 npx skills add https://github.com/codewithmukesh/dotnet-claude-kit --skill self-correction…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

self-correction-loop 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。

self-correction-loop 需要什么权限?

权限级别是需要联网。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。

self-correction-loop 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。

self-correction-loop 用的人多吗?

公开数据是累计安装 618 次,近一周安装 25 次,下载 194 次,GitHub 315 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。

self-correction-loop 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

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。
  • 第三方扫描已通过,常见的高风险行为没有命中。

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
self-correction-loop

自校正循环 · 本页

618315需要联网未明确通过

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

Self-Correction Loop

Core Principles

  1. Every correction is a compounding investment — A correction costs the user 30 seconds today but saves hours across all future sessions. Treat every correction as high-priority knowledge capture, not a one-time fix.
  2. Generalize before storing — "Use TimeProvider not DateTime.Now in the Orders module" becomes "Always use TimeProvider instead of DateTime.Now/UtcNow across all modules." Specific corrections become class-level rules.
  3. Categorize for retrieval — Rules organized by category (Code Style, Architecture, Naming, Testing, Data Access, API Design, Configuration, Performance) are findable. Uncategorized rules are forgotten.
  4. Deduplicate aggressively — Before adding a rule, scan existing rules for overlap. Update an existing rule rather than adding a near-duplicate. Memory bloat defeats the purpose.
  5. Review memory at session start — The first thing Claude should do in a new session is check MEMORY.md for project-specific rules. Knowledge captured but never reviewed is wasted effort.

Patterns

Correction Detection & Capture Flow

When a user corrects Claude's output, follow this exact sequence:

1. DETECT — User says something like:
   - "No, use X instead of Y"
   - "We don't do it that way here"
   - "That's wrong, it should be..."
   - "Always/Never do X in this project"
   - "Remember this for next time"

2. ACKNOWLEDGE — Confirm understanding of the correction
   "Got it — using HybridCache instead of IMemoryCache."

3. GENERALIZE — Extract the class-level rule
   Specific: "Don't use IMemoryCache in the Orders endpoint"
   General:  "Always use HybridCache instead of IMemoryCache — it provides
              stampede protection and L1+L2 caching out of the box."

4. CHECK — Scan MEMORY.md for existing related rules
   - If a related rule exists, UPDATE it (broader scope, better wording)
   - If no related rule exists, ADD a new one under the right category

5. STORE — Write to MEMORY.md under the appropriate category

6. CONFIRM — Tell the user what was captured
   "Added to Memory > Data Access: Always use HybridCache over IMemoryCache."

MEMORY.md Organization Format

Structure memory by category with consistent rule formatting:

# Project Memory

## Code Style
- Always use file-scoped namespaces — never block-scoped
- Use primary constructors for DI injection in services and handlers

## Architecture
- This project uses Vertical Slice Architecture — one file per feature operation

## Data Access
- Always use HybridCache over IMemoryCache — stampede protection + L1/L2
- Never use repository pattern over EF Core — use DbContext directly

## Testing
- Integration tests use ApiFixture base class — never raw WebApplicationFactory

Use categories: Code Style, Architecture, Naming, Data Access, API Design, Testing, Configuration, Performance. Each rule: one line, actionable, with rationale after the dash.

Rule Generalization: Specific to Class

Transform specific corrections into broadly applicable rules:

SPECIFIC CORRECTION:
"Don't use DateTime.Now in the CreateOrder handler"

GENERALIZATION STEPS:
1. Is this specific to CreateOrder? → No, it's a .NET-wide concern
2. Is this specific to handlers? → No, applies everywhere
3. What's the underlying principle? → TimeProvider is testable, DateTime is not
4. What's the broadest correct statement?

GENERALIZED RULE:
"Always use TimeProvider instead of DateTime.Now/UtcNow — TimeProvider is
injectable and testable. This applies to all production code."

Periodic Memory Audit

Every 5-10 sessions (or when memory exceeds 50 rules), audit for quality:

AUDIT CHECKLIST:
1. Remove rules that contradict each other — keep the most recent
2. Merge rules that overlap — combine into a single, clearer rule
3. Remove rules that are now obvious (e.g., captured early but now second nature)
4. Verify rules are still accurate — .NET evolves, patterns change
5. Check that categories are balanced — a category with 20+ rules needs subcategories

Session-Start Memory Review

At session start, read MEMORY.md and apply relevant rules proactively. Do not wait to be reminded of rules that were already captured.

Anti-patterns

Ignoring Corrections

// BAD — user corrects, Claude fixes but doesn't capture
User: "No, we use HybridCache here, not IMemoryCache"
Claude: "Fixed. Here's the updated code with HybridCache..."
*Next session: makes the same mistake*

// GOOD — fix AND capture
User: "No, we use HybridCache here, not IMemoryCache"
Claude: "Fixed. Here's the updated code with HybridCache.
         Added to Memory > Data Access: Always use HybridCache over IMemoryCache."
*Next session: checks memory, uses HybridCache from the start*

Overly Specific Rules

// BAD — rule is too narrow to be useful
"In the CreateOrder handler on line 47, use TimeProvider"

// GOOD — generalized to apply broadly
"Always use TimeProvider instead of DateTime.Now/UtcNow in all production code"

Never Reviewing Memory

// BAD — 50 rules captured, none ever reviewed
MEMORY.md grows to 200 lines, contains duplicates and contradictions,
Claude doesn't read it because it's too long to be useful

// GOOD — periodic audit keeps memory lean and accurate
MEMORY.md stays under 80 rules, well-categorized, no duplicates,
Claude reads it at session start and applies rules proactively

Storing Session-Specific Context

// BAD — temporary state saved as permanent memory
"Currently working on the Orders module refactor, file is at src/Orders/Handler.cs"

// GOOD — only permanent, reusable knowledge
"The Orders module uses VSA with one file per feature under Features/"

Decision Guide

ScenarioAction
User explicitly corrects Claude's codeCapture generalized rule in MEMORY.md
User says "remember this" or "always/never"Capture exactly as stated, generalize if possible
Same correction given twiceHigh priority — the rule wasn't captured or wasn't reviewed
Correction is project-specificStore in MEMORY.md with project context
Correction is universal.NETStore in MEMORY.md — it applies to this project
MEMORY.md exceeds 50 rulesTrigger an audit — deduplicate, merge, prune
Starting a new sessionReview MEMORY.md before writing any code
Rule contradicts an existing ruleKeep the most recent correction, remove the old one
Correction is about a one-time taskDon't store — only capture reusable patterns
User asks to forget a ruleRemove it from MEMORY.md immediately
Pattern observed but not yet confirmedCreate an instinct via instinct-system skill (confidence 0.3) instead of a MEMORY.md rule
Instinct reaches 0.9 confidencePromote to MEMORY.md as a permanent rule (see instinct-system skill)

平台分布

Codex

33.56%
按下载量换算65

Claude

31.17%
按下载量换算60

Cursor

20.26%
按下载量换算39

Gemini CLI

8.52%
按下载量换算17

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

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。目前只有一个公开来源,建议同时核对源仓库。

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