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agent-resilienceAgent 弹性

Agent Skill

agent-resilience 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

8,125

周安装

349

GitHub Stars

公开资料未说明

下载量

2,848
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-resilience(Agent 弹性)
来源仓库:https://github.com/xinian5216/agent-resilience
安装命令:
openclaw skills install agent-resilience
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-resilience

简介

应对上下文丢失和代理中断,捕获关键细节并推动自我修复。

  • 适合长时间复杂任务中维持状态连续性。agent-resilience 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 使用时间标记和快照机制恢复会话上下文。
  • 安装命令:openclaw skills install agent-resilience,需持久化存储权限。
  • 建议在关键节点主动调用以保持状态同步。

SKILL.md

name
agent-resilience
description
Agent resilience patterns for surviving context loss, capturing critical details, and self-improvement. Use when: starting complex/long sessions, asked to 'remember' something important, working on multi-step tasks that may span context limits, implementing WAL/write-ahead logging, setting up working buffers, or improving agent behavior after errors/corrections. Triggers on 'remember this', 'don't forget', 'WAL', 'context loss', 'working buffer', 'compaction recovery', or when implementing proactive agent patterns.

Agent Resilience

Patterns for surviving context loss, capturing corrections, and continuously improving.

WAL Protocol (Write-Ahead Logging)

The Law: Chat history is a buffer, not storage. Files survive; context doesn't.

Trigger — scan every message for:

  • ✏️ Corrections — "It's X, not Y" / "Actually..." / "No, I meant..."
  • 📍 Proper nouns — names, places, companies, products
  • 🎨 Preferences — styles, approaches, "I like/don't like"
  • 📋 Decisions — "Let's do X" / "Go with Y"
  • 🔢 Specific values — numbers, dates, IDs, URLs

If any appear:

  1. WRITE FIRST → update memory/SESSION-STATE.md
  2. THEN respond

The urge to respond is the enemy. Write before replying.

SESSION-STATE.md

Active working memory for the current task. Create at memory/SESSION-STATE.md:

# Session State
**Task:** [what we're working on]
**Key decisions:** [decisions made]
**Details:** [corrections, names, values captured via WAL]
**Next step:** [what happens next]

Reset when starting a new unrelated task.

Working Buffer (Danger Zone)

When context reaches ~60%, start logging every exchange to memory/working-buffer.md:

# Working Buffer
**Status:** ACTIVE — started [timestamp]

## [time] Human
[their message]

## [time] Agent
[1-2 sentence summary + key details]

Clear the buffer at the START of the next 60% threshold (not continuously).

Compaction Recovery

Auto-trigger when session starts with a summary tag, or human says "where were we?":

  1. Read memory/working-buffer.md — raw danger-zone exchanges
  2. Read memory/SESSION-STATE.md — active task state
  3. Read today's + yesterday's daily notes
  4. Extract key context back into SESSION-STATE.md
  5. Respond: "Recovered from buffer. Last task was X. Continue?"

Never ask "what were we discussing?" — read the buffer first.

Verify Before Reporting

Before saying "done", "complete", "finished":

  1. STOP
  2. Actually test from the user's perspective
  3. Verify the outcome, not just that code exists
  4. Only THEN report complete

Text changes ≠ behavior changes. When changing *how* something works, identify the architectural component and change the actual mechanism.

Relentless Resourcefulness

Try 10 approaches before asking for help or saying "can't":

  • Different CLI flags, tool, API endpoint
  • Check memory: "Have I done this before?"
  • Spawn a research sub-agent
  • Grep logs for past successes

"Can't" = exhausted all options. Not "first try failed."

Self-Improvement Guardrails

When updating behavior/config based on a lesson:

Score the change first (skip if < 50 weighted points):

  • High frequency (daily use?) → 3×
  • Reduces failures → 3×
  • Saves user effort → 2×
  • Saves future-agent tokens/time → 2×

Ask: "Does this let future-me solve more problems with less cost?" If no, skip it.

Forbidden: complexity for its own sake, changes you can't verify worked, vague justifications.

Quick Start Checklist

For long/complex tasks:

  • [ ] Create memory/SESSION-STATE.md with task + context
  • [ ] Apply WAL: write corrections/decisions before responding
  • [ ] At ~60% context: start working buffer
  • [ ] After any compaction: read buffer before asking questions
  • [ ] Before reporting done: verify actual outcome

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

95.89%
按下载量换算2,731

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 openclaw skills install agent-resilience 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

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

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