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Agent Skill · 研究检索

self-reflecting-chain(自反射链)

self-reflecting-chain 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-reflecting-chain 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,权限为只读;许可证为 MIT,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 35,370。

总安装 2,095周安装 90GitHub Stars 4下载 734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:self-reflecting-chain(自反射链)
来源仓库:https://github.com/kimasplund/claude_cognitive_reasoning
仓库路径:skills/self-reflecting-chain
安装命令:
npx skills add https://github.com/kimasplund/claude_cognitive_reasoning --skill self-reflecting-chain
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/kimasplund/claude_cognitive_reasoning --skill self-reflecting-chain

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。

提供逐步推理与深度自我反思的系统方法论。适用于 Codex、Claude、Cursor 等宿主中需顺序依赖的正确答案场景。支持错误检测、回溯与完整推理路径追踪。

安装命令:npx skills add https://github.com/kimasplund/claude_cognitive_reasoning --skill self-reflecting-chain。整体置信度取决于最弱环节,需重点加强薄弱环节。

来源仓库内路径:skills/self-reflecting-chain

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/kimasplund/claude_cognitive_reasoning --skill…;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

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

self-reflecting-chain 怎么安装?

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

self-reflecting-chain 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-reflecting-chain 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

self-reflecting-chain 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。

self-reflecting-chain 用的人多吗?

公开数据是累计安装 2,095 次,近一周安装 90 次,下载 734 次,GitHub 4 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-reflecting-chain 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。
  • 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。
  • 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

  • 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。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

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
self-reflecting-chain

自反射链 · 本页

2,0954只读MIT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-Reflecting Chain Reasoning Methodology

Purpose: Sequential step-by-step reasoning with deep self-reflection at each step. Unlike parallel exploration (ToT/BoT), this follows a single logical chain, reflects on each step's validity, and backtracks when errors detected.

When to Use Self-Reflecting Chain

✅ Use when:

  • Steps have dependencies (Step N depends on Step N-1)
  • Logical reasoning required (mathematical, causal, deductive)
  • Need to trace exact reasoning path
  • Error detection and correction critical
  • Sequential planning (Step A must complete before Step B)
  • Debugging (trace bug through execution flow)

❌ Don't use when:

  • Multiple independent solution paths exist → Use ToT or BoT
  • Need to explore many options in parallel → Use BoT
  • Steps can execute in any order → Don't need sequential reasoning

Examples:

  • "Debug this race condition by tracing execution step-by-step" ✅
  • "Prove this mathematical theorem" ✅
  • "Plan project where each phase depends on previous" ✅
  • "Choose between 5 architectures" (parallel problem - use BoT) ❌

Core Methodology: Chain-Reflect-Backtrack

Step 1: Problem Decomposition

Objective: Break problem into sequential logical steps

Actions:

  1. Identify starting state
  2. Define goal state
  3. List steps to get from start to goal (sequential order)
  4. Number steps clearly (Step 1, 2, 3...)
  5. Identify dependencies between steps

Example (Debugging):

Start: System crashes when user clicks "Submit"
Goal: Identify root cause

Steps:
1. Trace user action to event handler
2. Check event handler for errors
3. Trace data flow to backend
4. Check backend validation logic
5. Inspect database query execution
6. Identify exact failure point

Step 2: Execute Step N with Deep Reflection

For each step:

  1. Execute: Perform the reasoning/action for this step
  2. State Result: What did you learn/discover?
  3. Self-Reflect: Is this step correct?
  4. Check Validity: Does logic hold? Any assumptions?
  5. Confidence: How confident in this step (0-100%)?

Step Template:

## Step [N]: [Action]

### Execution
[Perform the reasoning or analysis]

### Result
[What was discovered/concluded]

### Self-Reflection
- **Confidence**: [0-100]%
- **Assumptions**: [What assumptions does this step make?]
- **Logic Check**: [Is the reasoning sound?]
- **Dependencies**: [Does this depend on previous steps being correct?]
- **Potential Errors**: [What could be wrong with this step?]

### Decision
- ✅ **Proceed** to Step [N+1] (confidence ≥70%)
- ⚠️ **Low Confidence** but proceeding (60-69%)
- ❌ **Backtrack** to Step [N-X] (confidence <60%)

Step 3: Backtracking Protocol

Trigger backtracking when:

  • Step confidence <60%
  • Logic error detected
  • Assumption proven false
  • Result contradicts known facts
  • Dead end reached

Backtracking Process:

  1. Identify error point: Which step was wrong?
  2. Return to that step: Go back to Step N-X
  3. Try alternative: Take different reasoning path
  4. Mark failed path: Document why previous path failed
  5. Resume forward: Continue from corrected step

Backtracking Example:

## Step 5: [Attempted reasoning]
→ Result: Contradiction detected
→ Confidence: 25% (contradicts Step 3 result)

**Backtrack Decision**: Return to Step 3, try alternative interpretation

## Step 3 (Revised): [Alternative reasoning]
→ Result: New interpretation consistent
→ Confidence: 80%
→ Proceed to Step 4 with revised understanding...

Step 4: Chain Validation

At each step, validate the entire chain so far:

  1. Forward Consistency: Does Step N follow logically from Step N-1?
  2. Backward Consistency: Do all previous steps still hold given new information?
  3. Assumption Check: Have any assumptions been violated?
  4. Alternative Paths: Should we backtrack and try different approach?

Validation Checklist:

  • Each step's confidence ≥70%
  • No logical contradictions
  • All assumptions explicitly stated
  • Dependencies satisfied
  • No better alternative path obvious

Step 5: Final Synthesis

After completing chain:

  1. Trace complete path: List all steps from start to goal
  2. Confidence per step: Show confidence for each step
  3. Overall confidence: Minimum confidence across all steps
  4. Alternative paths explored: Document backtracks and why
  5. Final answer: Clear conclusion with reasoning trace

Synthesis Template:

## Reasoning Chain Complete

### Complete Path
1. [Step 1] → Result: [X] (Confidence: 85%)
2. [Step 2] → Result: [Y] (Confidence: 90%)
3. [Step 3] → Result: [Z] (Confidence: 75%)
4. [Step 4] → Result: [A] (Confidence: 88%)
5. [Step 5] → Result: [B] (Confidence: 82%)

### Overall Confidence
**Minimum**: 75% (Step 3 was lowest)
**Chain Confidence**: 75% (limited by weakest link)

### Backtracks
- Backtracked from Step 4 to Step 2 (logic error)
- Alternative path tried at Step 3 (failed, original was correct)

### Final Conclusion
[Answer based on complete reasoning chain]

**Reasoning Trace**: Step 1 → Step 2 → Step 3 → Step 4 → Step 5 → Conclusion

Self-Critique Checklist

  • Step Independence: Is each step clearly defined?
  • Logical Flow: Does each step follow from previous?
  • Reflection Depth: Did I genuinely reflect on each step (not boilerplate)?
  • Backtracking Used: Did I backtrack when confidence low?
  • Assumptions Explicit: Are all assumptions stated clearly?
  • Weakest Link: Is chain confidence based on weakest step?
  • Alternative Paths: Did I consider other approaches when stuck?

Common Mistakes

  1. Skipping Reflection: Moving to next step without genuine self-reflection
  2. Ignoring Low Confidence: Proceeding when confidence <60%
  3. Missing Dependencies: Not checking if later steps depend on earlier ones
  4. No Backtracking: Never questioning previous steps when contradictions arise
  5. False Confidence: High confidence without justification
  6. Hidden Assumptions: Not explicitly stating what you're assuming

Sequential vs Parallel Decision Guide

Problem TypeUse Self-Reflecting ChainUse ToT/BoT
DependenciesSequential stepsIndependent paths
GoalSingle logical conclusionExplore options
MethodStep-by-step reasoningParallel branches
BacktrackingReturn to previous stepPrune branches
OutputReasoning traceMultiple solutions

Summary

Self-Reflecting Chain is systematic methodology for:

  1. Sequential reasoning (step-by-step)
  2. Deep self-reflection (confidence at each step)
  3. Error detection (validate logic continuously)
  4. Backtracking (correct errors when found)
  5. Traceability (clear reasoning path)

Use it when order matters, dependencies exist, and you need one correct answer with full reasoning trace.

Remember: Chain confidence = minimum step confidence. A 95% confident chain with one 60% step has 60% overall confidence. Strengthen the weakest link.

平台分布

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权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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。

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