Token导航 LogoToken导航TokenDH.com
研究检索需要联网clawhub未标认证来源可访问clear审计提醒

agent-reflect-tempAgent 反映温度

Agent Skill

agent-reflect-temp 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 OpenClaw 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。

总安装

6,209

周安装

264

GitHub Stars

公开资料未说明

下载量

2,175
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:agent-reflect-temp(Agent 反映温度)
来源仓库:https://github.com/mmyg11/agent-reflect-temp
安装命令:
openclaw skills install agent-reflect-temp
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 agent-reflect-temp

简介

通过对话分析提取学习内容,并永久编码到代理定义中以实现自我完善。

  • 适合在需要持续优化代理决策逻辑和响应模式的场景中启用。
  • 自动从成功与失败案例中归纳策略,提升后续任务表现。
  • 安装命令:openclaw skills install agent-reflect-temp,仅限 OpenClaw 环境。
  • 需注意学习内容的适用范围,避免过度泛化导致行为偏差。

SKILL.md

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
allowed-tools
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

CommandAction
reflectAnalyze conversation for learnings
reflect onEnable auto-reflection
reflect offDisable auto-reflection
reflect statusShow state and metrics
reflect reviewReview pending learnings

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:

ConfidenceTriggersExamples
HIGHExplicit corrections"never", "always", "wrong", "stop", "the rule is"
MEDIUMApproved approaches"perfect", "exactly", "that's right", accepted output
LOWObservationsPatterns that worked but not explicitly validated

See signal_patterns.md for full detection rules.

Step 2: Classify & Match to Target Files

Map each signal to the appropriate target:

CategoryTarget Files
Code Stylecode-reviewer, backend-developer, frontend-developer
Architecturesolution-architect, api-architect, architecture-reviewer
ProcessCLAUDE.md, orchestrator agents
DomainDomain-specific agents, CLAUDE.md
ToolsCLAUDE.md, relevant specialists
New SkillCreate new skill file

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

+ 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):

# 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

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

84.28%
按下载量换算1,833

安全审计

VirusTotal

通过

ClawScan

可疑

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。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。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

继续浏览同类 Skills