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Rocky Know-How

在 OpenClaw 中通过 bootstrap、memoryFlush 等事件钩子自动提取并记录经验,支持草稿缓冲、HOT/WARM/COLD 三层存储、标签晋升与降级、关键词与向量检索,提供记录、搜索、统计、归档等 Bash 脚本,并包含正则注入防护、路径穿越过滤与并发写锁等机制。

rockytian-top
rockytian-top
热度 56.69
最近更新 2026-04-30 19:51:47
分类:知识依赖:代码执行 / 读写文件 / 内部调用大模型 / 第三方 API应用场景:笔记整理 / 工作流 / 自动化脚本 / 摘要总结 ...

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add rocky-know-how
Metadata
name
rocky-know-how
slug
rocky-know-how
version
3.4.9
homepage
https://clawhub.ai/skills/rocky-know-how
description
Learning knowledge skill v3.1.0 — memoryFlush-driven experience recording, bootstrap injection, draft buffering for multi-round debugging, triple-layer storage (HOT/WARM/COLD), auto-promotion/demotion. Tested & verified: deepseek-v4, glm-5.1, minimax-m2.7. Safety: regex injection prevention, path traversal filtering, concurrent write lock.
metadata
{"openclaw":{"emoji":"📚","requires":{"bins":["bash"]},"os":["darwin","linux"]}}

rocky-know-how v3.1.0

Experience & Knowledge Auto-Learning System for OpenClaw Agents

让 AI Agent 从失败中学习,在成功后自动记录,形成经验闭环。


🎯 When to Use / 适用场景

Scenario 场景 Action 操作
Failed 2+ times 失败≥2次 bash scripts/search.sh "关键词"
Solved after failures 解决后 bash scripts/record.sh "问题" "踩坑" "方案" "预防" "tags" "area"
Auto on compaction 压缩时 memoryFlush driven — auto extract → judge → write or draft
Auto on reset 重置时 Fully automatic — Hook saves pending context
Tag used 3x/7days 晋升 bash scripts/promote.sh
Tag unused 30days 降级 bash scripts/demote.sh

🏗️ Architecture / 架构

Triple-Layer Storage / 三层存储

~/.openclaw/.learnings/
├── experiences.md    ← Main data (v1 compatible, all experiences)
├── memory.md         ← HOT layer (≤100 lines, always loaded)
├── domains/          ← WARM layer (by area: infra, code, wx...)
│   ├── infra.md
│   ├── code.md
│   └── global.md
├── projects/         ← WARM layer (by project)
├── archive/          ← COLD layer (90+ days)
├── drafts/           ← Auto-generated drafts (LLM judged)
├── pending/          ← Session context (before processing)
└── corrections.md    ← Correction log (auto-dedup)

Event-Driven Integration / 事件驱动

Event 事件 Trigger 触发 Function 功能
agent:bootstrap Agent startup 启动 Inject experience reminder into systemPrompt + process old drafts
memoryFlush Before compaction (config required) Core: extract experience → judge → write formal or draft
before_reset Before session reset 重置前 Save context as pending (fallback)

⚠️ Note: before_compaction / after_compaction hooks are gated by ownsCompaction in OpenClaw's source code and are not currently functional. We use memoryFlush instead.


🔄 Experience Recording Flow / 经验记录流程

memoryFlush-Driven (Core) / memoryFlush 驱动(核心)

Requires config — see Setup section below.

memoryFlush triggers (token threshold reached)
    │
    ▼
① Process old drafts 处理旧草稿
    ├─ Old draft + current NOT mentioning same issue → Resolved → record.sh (formal)
    └─ Old draft + current still working on it → Keep draft
    │
    ▼
② Extract new experience 提取新经验
    ├─ No draft + solved in 1 round → record.sh (formal directly)
    ├─ Same issue has draft (multi-round) → Write/update draft
    └─ Nothing worth recording → Skip
    │
    ▼
③ Write memory file 写记忆文件
    └─ Append to memory/YYYY-MM-DD.md
    │
    ▼
④ Reply NO_REPLY → Compaction begins

bootstrap (Startup) / 启动时

agent:bootstrap triggers
    ├─ Load domains/*.md → inject experience reminder into systemPrompt
    └─ Check drafts/ → process old drafts (fallback)

before_reset (Fallback) / 重置兜底

/reset or /new triggers
    └─ handler.js saves context → pending/*.json

🛡️ Safety & Security / 安全机制

Mechanism 机制 Implementation 实现
Regex injection prevention 正则注入防护 escape_grep() sed escaping
Path traversal filtering 路径穿越过滤 replace(/[^a-zA-Z0-9_-]/g, '')
Concurrent write lock 并发写锁 .write_lock/ directory atomic lock
Tag dedup promotion Tag去重晋升 record.sh dedup + promote.sh threshold
Graceful degradation 降级容错 LLM → keyword → write fallback chain

📊 Scripts Reference / 脚本说明

Script 脚本 Lines 行数 Function 功能
handler.js 1,110 Core hook handler (4 events, LLM integration)
search.sh 539 Search experiences (keyword / preview / all)
record.sh 476 Write new experience (with dedup & lock)
demote.sh 371 Demote HOT tags to WARM
compact.sh 348 Compress layers when exceeding limits
clean.sh 247 Remove test/invalid entries
vectors.sh 232 Vector search via LM Studio embeddings
promote.sh 185 Promote WARM tags to HOT (≥3x/7days)
import.sh 172 Import experiences from other sources
archive.sh 167 Archive old experiences to COLD
install.sh 161 Install skill to workspace
stats.sh 153 Show statistics dashboard
auto-review.sh 136 Auto-review pending drafts
append-record.sh 100 Append solution to existing experience
summarize-drafts.sh 80 Summarize and process drafts
update-record.sh 77 Update existing experience
common.sh 41 Shared utility functions
uninstall.sh 37 Remove skill
Total 共计 4,632

✅ Verified Testing / 测试验证

Models Tested / 已测试模型

Model 模型 Provider Forward Test 正向 Reverse Test 逆向 Status
deepseek-v4 deepseek (api-key) ✅ Pass ✅ Pass (144/150) Verified
glm-5.1 zai (api-key) ✅ Pass ✅ Pass (146/150) Verified
MiniMax-M2.7-highspeed minimax-portal (OAuth) ✅ Pass ✅ Pass (146/150) Verified

Test Coverage / 测试覆盖

Test 测试 Result 结果
agent:bootstrap → systemPrompt injection ✅ 12→952 chars
before_compaction → pending save ✅ task/tools/errors extracted
after_compaction → LLM dual-judge → write ✅ EXP auto-created
before_reset → fallback save ✅ pending saved
record.sh write + search ✅ Write & find
auto-review.sh process draft ✅ Draft → archive
compact.sh dry-run ✅ All layers healthy
promote.sh tag promotion ✅ Threshold check
stats.sh dashboard ✅ Full panel
5 safety mechanisms ✅ All present

🚀 Installation / 安装(下载即用)

一键安装

# Clone
git clone https://gitee.com/rocky_tian/skill.git
cd skill/rocky-know-how

# 一键安装(自动配置 Hook + memoryFlush + 重启网关)
bash scripts/install.sh

# 验证安装
bash scripts/stats.sh

install.sh 自动完成:

  • ✅ 创建经验诀窍目录结构
  • ✅ 初始化存储文件
  • ✅ 配置 Hook(agent:bootstrap / before_compaction / after_compaction / before_reset)
  • ✅ 配置 memoryFlush(softThresholdTokens: 8000)
  • ✅ 重启网关使配置生效

📈 Key Advantages / 核心优势

  1. memoryFlush-driven auto-learning — Experience recording triggered before every compaction
  2. LLM dual-judgment — First judges if worth saving, then decides create vs append
  3. Triple degradation — LLM → keyword → write, never loses data
  4. Multi-provider — Supports OpenAI, Anthropic, OAuth providers (zai/stepfun/minimax)
  5. Production proven — 45+ real experiences, 2.6MB data, stable operation
  6. Safety first — 5 security mechanisms (regex injection, path traversal, write lock, etc.)

Version: 3.1.0 | Tested: 2026-04-24 | License: MIT