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Agent Skill · 研究检索

bulletproof-memory-1-0-0(防弹内存 1 0 0)

bulletproof-memory-1-0-0 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。

bulletproof-memory-1-0-0 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为需要联网;许可证为 MIT-0,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 9,015。

总安装 15,104周安装 617GitHub Stars 公开资料未说明下载 4,837核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.0.0

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:bulletproof-memory-1-0-0(防弹内存 1 0 0)
来源仓库:https://github.com/leowing/bulletproof-memory-1-0-0
安装命令:
openclaw skills install bulletproof-memory-1-0-0
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 bulletproof-memory-1-0-0

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

通过预写日志协议为代理提供防弹内存机制,保障上下文不因重启或压缩丢失。适用于长会话、多步骤任务或对连续性要求高的智能体应用场景。自动记录操作日志至 SESSION-STATE.md,支持状态恢复与历史回溯。涉及频繁磁盘写入,需确认宿主文件系统权限及存储空间充足性。

建议在关键任务前测试日志完整性,避免异常中断导致数据损坏。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install bulletproof-memory-1-0-0;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

bulletproof-memory-1-0-0 怎么安装?

在终端执行 openclaw skills install bulletproof-memory-1-0-0。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

bulletproof-memory-1-0-0 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

bulletproof-memory-1-0-0 需要什么权限?

权限级别是需要联网。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。

bulletproof-memory-1-0-0 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

bulletproof-memory-1-0-0 用的人多吗?

公开数据是累计安装 15,104 次,近一周安装 617 次,下载 4,837 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

bulletproof-memory-1-0-0 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。

先想清楚的地方

  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
bulletproof-memory-1-0-0

防弹内存 1 0 0 · 本页

15,104—需要联网MIT-0有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
bulletproof-memory
version
1.0.0
description
Never lose context again. The Write-Ahead Log (WAL) protocol with SESSION-STATE.md gives your agent bulletproof memory that survives compaction, restarts, and distractions. Part of the Hal Stack 🦞
author
halthelobster

Bulletproof Memory 🦞

By Hal Labs — Part of the Hal Stack

Your agent forgets things. Mid-conversation, after compaction, between sessions — context vanishes. This skill fixes that permanently.

The Problem

Agents lose context in three ways:

  1. Compaction — old messages get summarized/dropped
  2. Session restart — agent wakes up fresh
  3. Distraction — mid-conversation, agent forgets earlier details

Traditional fix: "Remember to save important things."

But agents forget to remember.

The Solution: Write-Ahead Log (WAL) Protocol

The key insight: trigger writes on USER INPUT, not agent memory.

When the user provides a concrete detail, the agent writes it down BEFORE responding. The agent doesn't have to "remember" to save — the rule fires automatically based on what the user says.

Old ApproachWAL Approach
"Remember to save important things""If user gives detail → write before responding"
Triggered by agent memory (unreliable)Triggered by user INPUT (reliable)
Agent forgets to rememberRule fires automatically
Saves after the fact (too late)Saves before responding (never too late)

Quick Setup

1. Create SESSION-STATE.md

This is your agent's "hot RAM" — the active working memory that persists across compactions.

Create SESSION-STATE.md in your workspace root:

# SESSION-STATE.md — Active Working Memory

This file is the agent's "RAM" — the hot transaction log for the current active task.
Chat history is a BUFFER. This file is STORAGE.

---

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

## Immediate Context
[Key details, decisions, corrections from this session]

## Key Files
[Paths to relevant files for this task]

## Last Updated
[Timestamp]

2. Add WAL Protocol to AGENTS.md

Add this to your agent's instructions:

### WRITE-AHEAD LOG (WAL) PROTOCOL

**The Law:** You are a stateful operator. Chat history is a BUFFER, not storage.
`SESSION-STATE.md` is your "RAM" — the ONLY place specific details are safe.

**Trigger:** If the user provides a concrete detail (name, location, correction, decision):
1. You MUST update `SESSION-STATE.md` IMMEDIATELY
2. You MUST write to the file BEFORE you generate your response
3. Only THEN respond to the user

**Example:** User says "It's Doboce Park, not Duboce Triangle"
- WRONG: Acknowledge, keep chatting, maybe write later
- RIGHT: Update SESSION-STATE.md first, then respond

**Why this works:** The trigger is the user's INPUT, not your memory. You don't have 
to remember to check — the rule fires on what the user says.

3. Add Recovery Protocol

When context is lost, don't ask "what were we doing?" — recover it yourself:

### Compaction Recovery Protocol

**Auto-trigger when:**
- Session starts with `<summary>` tag
- Message contains "truncated", "context limits", "Summary unavailable"
- User says "where were we?", "continue", "what were we doing?"
- You should know something but don't

**Recovery steps:**
1. **FIRST:** Read `SESSION-STATE.md` — this has the active task state
2. Read today's + yesterday's daily notes
3. If still missing context, use `memory_search`
4. Present: "Recovered from SESSION-STATE.md. Last task was X. Continue?"

**Do NOT ask "what were we discussing?" if SESSION-STATE.md has the answer.**

4. Add Session Startup Sequence

## Every Session
Before doing anything else:
1. Read `SESSION-STATE.md` — your active working memory (FIRST PRIORITY)
2. Read your identity files (SOUL.md, USER.md, etc.)
3. Read `memory/YYYY-MM-DD.md` (today + yesterday) for recent context

Don't ask permission. Just do it.

5. Add Memory Flush Protocol

Monitor context and flush before you lose it:

### Memory Flush Protocol

Monitor your context usage with `session_status`. Flush important context before compaction:

| Context % | Action |
|-----------|--------|
| < 50% | Normal operation |
| 50-70% | Write key points after substantial exchanges |
| 70-85% | Active flushing — write everything important NOW |
| > 85% | Emergency flush — full summary before next response |

**At >60%:** Update SESSION-STATE.md before every reply
**At >80%:** Write comprehensive handoff to daily notes

**What to flush:**
- Decisions made (what was decided and why)
- Action items (who's doing what)
- Open threads (anything unfinished)
- Corrections (things the user clarified)

Why This Works

The Trigger Insight

Most memory advice fails because it relies on the agent remembering to do something. But forgetting is the problem we're trying to solve!

The WAL protocol succeeds because:

  • Trigger = user input (external, reliable)
  • Not trigger = agent memory (internal, unreliable)

When the user says something concrete, the protocol fires. The agent doesn't need to remember anything — the rule activates based on what comes in.

The SESSION-STATE.md Insight

Daily notes are great for logging what happened. But they're not structured for "what am I doing RIGHT NOW?"

SESSION-STATE.md is:

  • Hot — the current active task, not history
  • Structured — current task, context, key files
  • First priority — read before anything else on startup

It's the difference between a journal and a sticky note on your monitor.

Pre-Compaction Checklist

Before a long session ends or context gets critical:

  • [ ] Current task documented in SESSION-STATE.md?
  • [ ] Key decisions captured?
  • [ ] Action items noted?
  • [ ] User corrections saved?
  • [ ] Could future-me continue from SESSION-STATE.md alone?

Self-Summarization Prompt

When context hits 85%+, ask yourself:

"If my context resets right now, what does future-me absolutely need to know to continue this task? Write it for someone with zero context."

This produces better summaries than mechanical extraction.

The Complete Memory Stack

For comprehensive agent memory, combine this with:

SkillPurpose
Bulletproof Memory (this)Never lose active context
PARA Second BrainOrganize long-term knowledge
Proactive AgentAct without being asked

Together, they create an agent that remembers everything, finds anything, and anticipates needs.

Example SESSION-STATE.md

Here's a real example of what this looks like in practice:

# SESSION-STATE.md — Active Working Memory

## Current Task
Building dashboard for Jordan — Life OS view with goal tracking

## Immediate Context
- Dashboard deployed to: https://halthelobster.github.io/hal-ops-dashboard/
- Added tabs: Operations + Life OS
- Jordan at Moontricks concert @ The Independent tonight
- Correction: It's "Shovelman" (one word), not "Shovel Man"

## Key Files
- Dashboard HTML: /Users/Hal/clawd/dashboard/index.html
- Life OS data: /Users/Hal/clawd/dashboard/life-os.json
- Social events log: notes/areas/social-events.md

## Last Updated
2026-01-29 11:00 PM PST

Principles

  1. Write before responding — The WAL protocol is non-negotiable
  2. Trigger on input — User input fires the rule, not agent memory
  3. SESSION-STATE.md is first — Always read it first on startup
  4. Flush early, flush often — Don't wait for 85% context
  5. Structure for retrieval — Future-you needs to continue, not just read

*Part of the Hal Stack 🦞*

*Pairs well with PARA Second Brain for knowledge organization and Proactive Agent for behavioral patterns.*

平台分布

OpenClaw

70.38%
按下载量换算3,404

安全审计

VirusTotal

可疑

ClawScan

可疑

Static analysis

未展示

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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