什么是 Reflect？

Reflect 践行“一次纠正，终身受益”的理念，将您的 AI 助手从静态工具转变为持续进化的伙伴。通过分析用户反馈、纠正信号和成功模式，这一 Openclaw Skills 插件确保您的智能体能从每次交互中学习，有效减少重复性错误，并根据您的特定项目需求量身定制行为。

该技能弥补了短期会话上下文与长期行为演变之间的鸿沟。它充当桥梁，将修正意见合成为持久指令，并延续到未来的所有会话中。对于希望通过 Openclaw Skills 获得真正个性化 AI 编程体验的开发者来说，它是必不可少的组件。

下载入口:https://github.com/openclaw/skills/tree/main/skills/stevengonsalvez/self-reflect

安装与下载

1. ClawHub CLI

从源直接安装技能的最快方式。

2. 手动安装

将技能文件夹复制到以下位置之一

优先级：工作区 > 本地 > 内置

3. 提示词安装

将此提示词复制到 OpenClaw 即可自动安装。

Reflect 应用场景

• 在完成复杂的开发任务后，保存成功的模式和逻辑。
• 当用户使用“永远不要做 X”或“务必做 Y”等指令明确纠正智能体行为时。
• 在会话结束或上下文压缩之前，确保关键见解不会丢失。
• 当发现非显而易见的调试方案时，将其保存为新的 Openclaw Skills 以供将来使用。

1. 扫描对话历史，寻找诸如“从不”、“总是”或“精确”等高置信度信号，以识别学习机会。
2. 将检测到的信号分类为代码风格、架构、流程或工具等类别。
3. 将学习成果与特定的智能体文件匹配，或确定该模式是否值得创建一个全新的技能。
4. 生成结构化提案和差异对比（diff），精确显示将对智能体定义进行的更改。
5. 通过人工干预的审查流程将批准的更改应用到相关文件中，以确保安全性和准确性。

Reflect 配置指南

要将 Reflect 集成到您的环境中，请按照以下步骤确保您的 Openclaw Skills 配置正确：

# 创建所需的各种状态目录
mkdir -p ~/.reflect

# （可选）设置自定义状态目录路径
export REFLECT_STATE_DIR="~/.reflect"

目录准备就绪后，您可以在任何会话期间使用 reflect 命令触发该技能。它将自动开始扫描当前上下文中的改进信号。

Reflect 数据架构与分类体系

Reflect 在全局和项目特定位置管理数据，以确保您的学习成果在 Openclaw Skills 生态系统中持久且可移植。

name: reflect
description: Self-improvement through conversation analysis. Extracts learnings from corrections and success patterns, permanently encoding them into agent definitions. Philosophy - Correct once, never again.
version: "2.0.0"
user-invocable: true
triggers:
  - reflect
  - self-reflect
  - review session
  - what did I learn
  - extract learnings
  - analyze corrections
allowed-tools:
  - Read
  - Write
  - Edit
  - Grep
  - Glob
  - Bash
metadata:
  clawdbot:
    emoji: "??"
    config:
      stateDirs: ["~/.reflect"]

Reflect - Agent Self-Improvement Skill

Transform your AI assistant into a continuously improving partner. Every correction becomes a permanent improvement that persists across all future sessions.

Quick Reference

When to Use

• After completing complex tasks
• When user explicitly corrects behavior ("never do X", "always Y")
• At session boundaries or before context compaction
• When successful patterns are worth preserving

Workflow

Step 1: Scan Conversation for Signals

Analyze the conversation for correction signals and learning opportunities.

Signal Confidence Levels:

See signal_patterns.md for full detection rules.

Step 2: Classify & Match to Target Files

Map each signal to the appropriate target:

See agent_mappings.md for mapping rules.

Step 3: Check for Skill-Worthy Signals

Some learnings should become new skills rather than agent updates:

Skill-Worthy Criteria:

• Non-obvious debugging (>10 min investigation)
• Misleading error (root cause different from message)
• Workaround discovered through experimentation
• Configuration insight (differs from documented)
• Reusable pattern (helps in similar situations)

Quality Gates (must pass all):

• Reusable: Will help with future tasks
• Non-trivial: Requires discovery, not just docs
• Specific: Can describe exact trigger conditions
• Verified: Solution actually worked
• No duplication: Doesn't exist already

Step 4: Generate Proposals

Present findings in structured format:

# Reflection Analysis

## Session Context
- **Date**: [timestamp]
- **Messages Analyzed**: [count]

## Signals Detected

| # | Signal | Confidence | Source Quote | Category |
|---|--------|------------|--------------|----------|
| 1 | [learning] | HIGH | "[exact words]" | Code Style |

## Proposed Changes

### Change 1: Update [agent-name]
**Target**: `[file path]`
**Section**: [section name]
**Confidence**: HIGH

```diff
+ New rule from learning

Review Prompt

Apply these changes? (Y/N/modify/1,2,3)

### Step 5: Apply with User Approval

**On `Y` (approve):**
1. Apply each change using Edit tool
2. Commit with descriptive message
3. Update metrics

**On `N` (reject):**
1. Discard proposed changes
2. Log rejection for analysis

**On `modify`:**
1. Present each change individually
2. Allow editing before applying

**On selective (e.g., `1,3`):**
1. Apply only specified changes
2. Commit partial updates

## State Management

State is stored in `~/.reflect/` (configurable via `REFLECT_STATE_DIR`):

```yaml
# reflect-state.yaml
auto_reflect: false
last_reflection: "2026-01-26T10:30:00Z"
pending_reviews: []

Metrics Tracking

# reflect-metrics.yaml
total_sessions_analyzed: 42
total_signals_detected: 156
total_changes_accepted: 89
acceptance_rate: 78%
confidence_breakdown:
  high: 45
  medium: 32
  low: 12
most_updated_agents:
  code-reviewer: 23
  backend-developer: 18
skills_created: 5

Safety Guardrails

Human-in-the-Loop

• NEVER apply changes without explicit user approval
• Always show full diff before applying
• Allow selective application

Incremental Updates

• ONLY add to existing sections
• NEVER delete or rewrite existing rules
• Preserve original structure

Conflict Detection

• Check if proposed rule contradicts existing
• Warn user if conflict detected
• Suggest resolution strategy

Output Locations

Project-level (versioned with repo):

• .claude/reflections/YYYY-MM-DD_HH-MM-SS.md - Full reflection
• .claude/skills/{name}/SKILL.md - New skills

Global (user-level):

• ~/.reflect/learnings.yaml - Learning log
• ~/.reflect/reflect-metrics.yaml - Aggregate metrics

Examples

Example 1: Code Style Correction

User says: "Never use var in TypeScript, always use const or let"

Signal detected:

• Confidence: HIGH (explicit "never" + "always")
• Category: Code Style
• Target: frontend-developer.md

Proposed change:

## Style Guidelines
+ * Use `const` or `let` instead of `var` in TypeScript

Example 2: Process Preference

User says: "Always run tests before committing"

Signal detected:

• Confidence: HIGH (explicit "always")
• Category: Process
• Target: CLAUDE.md

Proposed change:

## Commit Hygiene
+ * Run test suite before creating commits

Example 3: New Skill from Debugging

Context: Spent 30 minutes debugging a React hydration mismatch

Signal detected:

• Confidence: HIGH (non-trivial debugging)
• Category: New Skill
• Quality gates: All passed

Proposed skill: react-hydration-fix/SKILL.md

Troubleshooting

No signals detected:

• Session may not have had corrections
• Check if using natural language corrections

Conflict warning:

• Review the existing rule cited
• Decide if new rule should override
• Can modify before applying

Agent file not found:

• Check agent name spelling
• May need to create agent file first