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brainmdbrainmd 搜索

Agent Skill

brainmd 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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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:brainmd(brainmd 搜索)
来源仓库:https://github.com/p0lish/brainmd
安装命令:
openclaw skills install brainmd
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 brainmd

简介

实现 AI Agent 自我修改的神经可塑性运行时。

  • 通过交互学习建立反射、习惯与长期记忆路径。
  • 适用于个性化代理能力渐进式进化需求。
  • 需定义学习速率与变更回滚机制。brainmd 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 建议监控内存增长防止无限膨胀风险。适用宿主包括 OpenClaw,接入前应确认版本、权限和运行环境要求。

SKILL.md

name
brainmd
description
Neuroplastic self-modifying runtime for AI agents. Creates a file-based 'brain' that learns from interactions: reflexes (fast-path responses), habits (learned patterns), weighted pathways (reinforcement), and a cortex (self-review loop). Use when: setting up adaptive agent behavior, creating learning loops, building persistent behavioral memory, or making an agent that improves over time.

brainmd

*File-based nervous system for AI agents. Behaviors that work get stronger. Behaviors that fail get weaker. Unused patterns decay. Mistakes leave scars.*


What It Is

brainmd gives your agent a persistent behavioral memory that survives session restarts. It's not a knowledge base — it's muscle memory. It tracks *how* the agent behaves, not *what* it knows.

Three layers complement each other:

LayerFilePurpose
brainmdbrain/weights/pathways.jsonBehavioral reinforcement — what works, what doesn't
Long-term memoryMEMORY.mdSemantic facts — decisions, people, context
Daily logmemory/YYYY-MM-DD.mdEpisodic notes — what happened today

brainmd is the only layer that self-modifies. The others are written by the agent, not evolved by it.


Installation

clawhub install brainmd

Then initialize the brain in your workspace:

cd ~/.openclaw/workspace
./skills/brainmd/scripts/init-brain.sh brain/

This creates:

brain/
├── reflexes/           # Fast-path decision scripts
├── habits/
│   └── preferences.json
├── weights/
│   └── pathways.json   # ← the core state file
├── cortex/
│   └── review.js       # ← the self-review engine
└── mutations/          # Immutable audit log

Wiring Into OpenClaw

Step 1 — AGENTS.md

Add a startup check so the agent reads its neural state at session start:

## 🧠 brainmd — Consult Your Brain

After reading memory files, check your neural state:

node ~/.openclaw/workspace/brain/cortex/review.js status


Before acting on anything non-trivial, scan for relevant pathways:
- Weak pathways (< 0.5) = you've failed here before. Be careful, double-check.
- Strong pathways (> 0.8) = proven patterns. Trust them, act fast.
- Dying pathways (decaying) = you're forgetting something. Re-evaluate.

After notable outcomes, record them:

node brain/cortex/review.js record "pathway-name" true/false "what happened"

Step 2 — HEARTBEAT.md

Wire the review cycle into your heartbeat so it runs automatically:

## 🧠 brainmd Self-Check (every heartbeat)

node ~/.openclaw/workspace/brain/cortex/review.js review node ~/.openclaw/workspace/brain/cortex/review.js status


On each heartbeat, ask yourself:
1. Did I make a mistake since last check? → `record <pathway> false "what happened"`
2. Did something work well? → `record <pathway> true "what worked"`
3. Did a new pattern emerge? → let neurogenesis create it

Step 3 — Seed Initial Pathways

Don't hypothesize — seed from real behavior. Run a few sessions first, then record what you observed:

node brain/cortex/review.js record "reflex:morning-briefing" true "Supplement reminders sent, user confirmed"
node brain/cortex/review.js record "habit:check-files-before-search" true "Read apartment-search.md before googling apartments"
node brain/cortex/review.js record "reflex:safe-file-deletion" false "Used xargs rm with bad grep, deleted workspace files"

Start with 5–10 pathways. Let the system grow from there.


Daily Usage

Check neural state

node brain/cortex/review.js status

Output shows all pathways with visual weight bars, success rate, fire count, and last outcome. Use this to calibrate confidence before non-trivial actions.

Record an outcome

# Something worked
node brain/cortex/review.js record "habit:remote-service-recovery" true "Fixed broken systemd service, used journalctl to diagnose"

# Something failed
node brain/cortex/review.js record "habit:bulk-subagent-spawning" false "Rate limited all 3 models by spawning 2 Opus agents simultaneously"

New pathways are auto-created at weight 0.30 (neurogenesis). Existing pathways update their stats.

Run the review cycle

node brain/cortex/review.js review

The cortex examines all pathways and applies reinforcement rules:

  • Strengthen (+0.05): ≥3 fires, ≥80% success rate
  • Weaken (−0.10): ≥3 fires, <50% success rate
  • Decay (−0.02): unused for 7+ days
  • Prune: weight hits 0 (pathway removed)

All changes are logged to mutations/ with timestamp and reason.


Pathway Naming Conventions

reflex:timing          # Automatic, fast-path behaviors
habit:check-files      # Learned patterns from repeated interaction
skill:osint-workflow   # Acquired capabilities
instinct:safe-delete   # Safety behaviors (start at high weight, floor at 0.8)

Reading pathway weights

WeightMeaningHow to use
0.8–1.0Proven, trustedAct confidently, don't second-guess
0.5–0.8DevelopingUse but verify
0.3–0.5Weak / newProceed carefully, double-check
< 0.3Failing / dyingInvestigate before using; may need rethinking

Tuning the Learning Rate

Edit thresholds in brain/cortex/review.js:

// Strengthen when success rate >= this
const STRENGTHEN_THRESHOLD = 0.8;

// Weaken when success rate < this
const WEAKEN_THRESHOLD = 0.5;

// Days of inactivity before decay starts
const DECAY_ONSET_DAYS = 7;

// Weight change per review cycle
const DECAY_RATE = 0.02;
const STRENGTHEN_DELTA = 0.05;
const WEAKEN_DELTA = 0.10;

Floor weights (prevent over-pruning)

instinct:* pathways should have a minimum weight floor so they can't be trained away:

{
  "id": "instinct:safe-file-deletion",
  "weight": 0.85,
  "floor": 0.80,
  "fires": 1,
  "successes": 0
}

Add a floor field to pathways in pathways.json to protect them from decay.


Architecture Notes

pathways.json — the core state

{
  "version": 42,
  "pathways": {
    "habit:check-files-before-search": {
      "weight": 0.95,
      "fires": 13,
      "successes": 11,
      "lastFired": "2026-03-25T18:00:00.000Z",
      "lastOutcome": "Read AESTHETIC.md before recommending clothes. Saved a web search.",
      "created": "2025-11-01T00:00:00.000Z"
    }
  }
}

mutations/ — the audit log

Every self-modification writes a timestamped JSON file. Never delete these. They're how you trace *why* the agent's behavior changed over time.

{
  "type": "strengthen",
  "target": "habit:check-files-before-search",
  "from": 0.90,
  "to": 0.95,
  "reason": "11/13 success rate",
  "timestamp": "2026-03-26T07:00:00.000Z"
}

Integration With Auto-Dream

If you're using scripts/dream.js for memory consolidation, the two systems complement each other:

  • brainmd answers: *how should I behave?*
  • dream.js answers: *what do I know?*

Neither replaces the other. Wire both into HEARTBEAT.md for full coverage.


Bootstrapping Checklist

  • [ ] Run init-brain.sh to create directory structure
  • [ ] Add brainmd status check to AGENTS.md startup routine
  • [ ] Add heartbeat entry to HEARTBEAT.md
  • [ ] Seed 5–10 pathways from real observed behavior (not theory)
  • [ ] Run one manual review to verify reinforcement logic works
  • [ ] Check mutations/ after first review to confirm logging
  • [ ] Set floor weights on any instinct:* pathways

Design Principles

  1. Everything is mutable — no file is sacred except the mutation log
  2. Use strengthens, disuse weakens — pathways that fire together wire together
  3. Outcomes matter — track what worked, what didn't; guesses don't count
  4. Failures leave scars — the most valuable pathways come from mistakes
  5. Seed from reality — observe first, codify second
  6. Small and composable — one pathway per behavior pattern
  7. The schedule forces honesty — if it's not in HEARTBEAT.md, you'll skip it

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