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compaction-survival压实生存

Agent Skill

compaction-survival 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。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。

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下载量

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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:compaction-survival(压实生存)
来源仓库:https://github.com/rustyorb/compaction-survival
安装命令:
openclaw skills install compaction-survival
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 compaction-survival

简介

防止 LLM 压缩期间因上下文丢失导致临界状态中断。

  • 使用预写日志 (WAL) 和工作缓冲区实现自动恢复机制。
  • 保障关键操作在压缩前后的一致性与完整性。
  • 安装命令:openclaw skills install compaction-survival。
  • 需配合定期快照策略以降低数据丢失风险。compaction-survival 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

name
compaction-survival
version
1.0.0
description
Prevent context loss during LLM compaction via Write-Ahead Logging (WAL), Working Buffer, and automatic recovery. Three mechanisms that ensure critical state — decisions, preferences, values, paths — survives when the context window compresses. Always-active behavioral skill, not a one-time tool.
author
rustyorb
keywords
[memory, compaction, context, wal, write-ahead-log, session-state, persistence, survival, long-context, agent-memory]
metadata
openclaw
emoji
🛡️

Compaction Survival System

Compaction destroys specifics: file paths, exact values, config details, reasoning chains. This skill ensures critical state survives.

The problem: When your context window fills up, OpenClaw compacts older messages into a summary. Summaries lose precision — exact numbers become "approximately," file paths vanish, decisions lose their rationale. Your agent wakes up dumber after every compaction.

The fix: Three mechanisms that capture critical state before compaction hits, and recover it after.

Three Mechanisms

1. WAL Protocol (Write-Ahead Logging)

On EVERY incoming message, scan for:

  • ✏️ Corrections — "It's X, not Y" / "Actually..."
  • 📍 Proper nouns — names, places, companies, products
  • 🎨 Preferences — styles, approaches, "I like/don't like"
  • 📋 Decisions — "Let's do X" / "Go with Y"
  • 📝 Draft changes — edits to active work
  • 🔢 Specific values — numbers, dates, IDs, URLs, paths

If ANY appear:

  1. STOP — do not compose response yet
  2. WRITE — update SESSION-STATE.md with the detail
  3. THEN — respond to the human

The trigger fires on the human's INPUT, not your memory. Write what they said, not what you think.

2. Working Buffer (Danger Zone)

At 60% context utilization (check via session_status):

  1. Create/clear memory/working-buffer.md, write header:
   # Working Buffer (Danger Zone)
   **Status:** ACTIVE
   **Started:** [timestamp]
  1. Every exchange after 60%: append human's message + your response summary
  2. Buffer is a file — it survives compaction
  3. Leave buffer as-is until next 60% threshold in a new session

Location: memory/working-buffer.md

3. Compaction Recovery

Auto-trigger when:

  • Session starts with <summary> tag in context
  • You should know something but don't
  • Human says "where were we?" / "continue" / "what were we doing?"

Recovery steps (in order):

  1. Read memory/working-buffer.md — raw danger-zone exchanges
  2. Read SESSION-STATE.md — active task state
  3. Read today's + yesterday's memory/YYYY-MM-DD.md
  4. Run memory_search if still missing context
  5. Extract important context from buffer → update SESSION-STATE.md
  6. Report: "Recovered context. Last task was X. Continuing."

NEVER ask "what were we discussing?" — the buffer has the answer.

SESSION-STATE.md Format

# Session State — Active Working Memory

## Current Task
[What we're actively working on]

## Key Details
- [Specific values, paths, configs captured via WAL]

## Decisions Made
- [Decisions with rationale]

## Pending
- [What's waiting/blocked]

## Last Updated
[timestamp]

Update this file frequently. It's your RAM — the only place specifics survive between compaction events.

How It Works Together

                    ┌──────────────────────────┐
                    │    Human sends message    │
                    └────────────┬─────────────┘
                                 │
                    ┌────────────▼─────────────┐
                    │  WAL: Scan for specifics  │
                    │  Found? Write first.      │
                    └────────────┬─────────────┘
                                 │
               ┌─────────────────▼─────────────────┐
               │  Context > 60%? Buffer everything  │
               └─────────────────┬─────────────────┘
                                 │
                    ┌────────────▼─────────────┐
                    │    Respond to human       │
                    └────────────┬─────────────┘
                                 │
                        ┌────────▼────────┐
                        │  COMPACTION HIT  │
                        └────────┬────────┘
                                 │
                    ┌────────────▼─────────────┐
                    │  Recovery: Read buffer,   │
                    │  SESSION-STATE, daily log  │
                    │  → Full context restored   │
                    └──────────────────────────┘

Integration

  • Works alongside MEMORY.md (long-term) and memory/YYYY-MM-DD.md (daily logs)
  • SESSION-STATE.md = working memory for current task
  • Working buffer = emergency capture for the danger zone
  • All three layers stack: WAL → Buffer → Recovery
  • No dependencies. No API keys. Pure behavioral patterns.

Why This Works

Most "memory" solutions try to store everything forever. That's the wrong problem. The real problem is precision loss during compaction. You don't need to remember everything — you need to remember the RIGHT things at the RIGHT time.

WAL catches specifics the moment they appear. The buffer captures the danger zone. Recovery restores context after the reset. Three layers, zero dependencies, zero data leakage.


*Built by @rustyorb + S1nthetta ⚡ — Battle-tested across 30+ compaction events.*

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。

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按下载量换算6,933

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安装前确认

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