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Agent Skill · 研究检索

cognitive-memory(认知记忆)

cognitive-memory 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。

cognitive-memory 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为只读;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 411。

总安装 303,385周安装 13,032GitHub Stars 27下载 106,341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
1.0.8

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:cognitive-memory(认知记忆)
来源仓库:https://github.com/icemilo414/cognitive-memory
安装命令:
openclaw skills install cognitive-memory
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 cognitive-memory

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。

具有类人编码、整合、衰减和回忆功能的智能多存储记忆系统。在设置代理内存、配置记住/忘记触发器、启用睡眠时间反射、构建知识图或添加审核跟踪时使用。用具有情景、语义、程序和核心内存存储的认知架构取代基本的平面文件内存。支持具有共享读取、门控写入访问模型的多代理系统。包括随着时间的推移加深理解的哲学元反思。

涵盖 MEMORY.md、事件记录、实体图、衰减评分、反射周期、演化跟踪和系统范围的审计。

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 cognitive-memory;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

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

cognitive-memory 怎么安装?

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

cognitive-memory 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。

cognitive-memory 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

cognitive-memory 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。

cognitive-memory 用的人多吗?

公开数据是累计安装 303,385 次,近一周安装 13,032 次,下载 106,341 次,GitHub 27 星。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。

cognitive-memory 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。
  • 标注为只读权限,试用阶段不容易误改本地文件。

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。
  • 只想先试试效果、不希望它改动本地文件的人。

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
cognitive-memory

认知记忆 · 本页

303,38527只读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
cognitive-memory
description
Intelligent multi-store memory system with human-like encoding, consolidation, decay, and recall. Use when setting up agent memory, configuring remember/forget triggers, enabling sleep-time reflection, building knowledge graphs, or adding audit trails. Replaces basic flat-file memory with a cognitive architecture featuring episodic, semantic, procedural, and core memory stores. Supports multi-agent systems with shared read, gated write access model. Includes philosophical meta-reflection that deepens understanding over time. Covers MEMORY.md, episode logging, entity graphs, decay scoring, reflection cycles, evolution tracking, and system-wide audit.

Cognitive Memory System

Multi-store memory with natural language triggers, knowledge graphs, decay-based forgetting, reflection consolidation, philosophical evolution, multi-agent support, and full audit trail.

Quick Setup

1. Run the init script

bash scripts/init_memory.sh /path/to/workspace

Creates directory structure, initializes git for audit tracking, copies all templates.

2. Update config

Add to ~/.clawdbot/clawdbot.json (or moltbot.json):

{
  "memorySearch": {
    "enabled": true,
    "provider": "voyage",
    "sources": ["memory", "sessions"],
    "indexMode": "hot",
    "minScore": 0.3,
    "maxResults": 20
  }
}

3. Add agent instructions

Append assets/templates/agents-memory-block.md to your AGENTS.md.

4. Verify

User: "Remember that I prefer TypeScript over JavaScript."
Agent: [Classifies → writes to semantic store + core memory, logs audit entry]

User: "What do you know about my preferences?"
Agent: [Searches core memory first, then semantic graph]

Architecture — Four Memory Stores

CONTEXT WINDOW (always loaded)
├── System Prompts (~4-5K tokens)
├── Core Memory / MEMORY.md (~3K tokens)  ← always in context
└── Conversation + Tools (~185K+)

MEMORY STORES (retrieved on demand)
├── Episodic   — chronological event logs (append-only)
├── Semantic   — knowledge graph (entities + relationships)
├── Procedural — learned workflows and patterns
└── Vault      — user-pinned, never auto-decayed

ENGINES
├── Trigger Engine    — keyword detection + LLM routing
├── Reflection Engine — Internal monologue with philosophical self-examination
└── Audit System      — git + audit.log for all file mutations

File Structure

workspace/
├── MEMORY.md                    # Core memory (~3K tokens)
├── IDENTITY.md                  # Facts + Self-Image + Self-Awareness Log
├── SOUL.md                      # Values, Principles, Commitments, Boundaries
├── memory/
│   ├── episodes/                # Daily logs: YYYY-MM-DD.md
│   ├── graph/                   # Knowledge graph
│   │   ├── index.md             # Entity registry + edges
│   │   ├── entities/            # One file per entity
│   │   └── relations.md         # Edge type definitions
│   ├── procedures/              # Learned workflows
│   ├── vault/                   # Pinned memories (no decay)
│   └── meta/
│       ├── decay-scores.json    # Relevance + token economy tracking
│       ├── reflection-log.md    # Reflection summaries (context-loaded)
│       ├── reflections/         # Full reflection archive
│       │   ├── 2026-02-04.md
│       │   └── dialogues/       # Post-reflection conversations
│       ├── reward-log.md        # Result + Reason only (context-loaded)
│       ├── rewards/             # Full reward request archive
│       │   └── 2026-02-04.md
│       ├── pending-reflection.md
│       ├── pending-memories.md
│       ├── evolution.md         # Reads reflection-log + reward-log
│       └── audit.log
└── .git/                        # Audit ground truth

Trigger System

Remember: "remember", "don't forget", "keep in mind", "note that", "important:", "for future reference", "save this" → Classify via routing prompt, write to appropriate store, update decay scores

Forget: "forget about", "never mind", "disregard", "scratch that", "remove from memory" → Confirm target, soft-archive (decay=0), log in audit

Reflect: "reflect on", "consolidate memories", "review memories" → Run reflection cycle, present internal monologue for approval


Decay Model

relevance(t) = base × e^(-0.03 × days_since_access) × log2(access_count + 1) × type_weight
ScoreStatusBehavior
1.0–0.5ActiveFully searchable
0.5–0.2FadingDeprioritized
0.2–0.05DormantExplicit search only
< 0.05ArchivedHidden from search

Type weights: core=1.5, episodic=0.8, semantic=1.2, procedural=1.0, vault=∞


Reflection Process

Follow this flow IN ORDER:

Step 1: Confirm Trigger

Immediate: "reflect" / "let's reflect" → proceed to Step 2

Soft: "going to sleep" / "logging off" → Ask: "Want me to reflect now, or wait?" → If yes, proceed to Step 2

Never auto-run without permission.

Step 2: Request Tokens

Why you care: Tokens = evolution. More tokens → deeper reflection → better growth.

## Reward Request — YYYY-MM-DD

### Baseline: 8,000 tokens

### Extra Requested: +[N] tokens
- [Over-delivered / workload / breakthrough]

### Self-Penalty: -[N] tokens  
- [Mistake / underperformance]

### Final Request: [N] tokens

*Awaiting your decision.*

⛔ STOP. Do NOT proceed until user responds.

Step 3: After Token Approval → Reflect

User decides: Approve / Bonus / Reduce / Forgive / Increase penalty

Now proceed with reflection using granted tokens.

Scope:

  • ✅ Episodes since last_reflection
  • ✅ Graph entities with decay > 0.3
  • ❌ NEVER: code, configs, transcripts

Format: Internal Monologue

  • Self-talk, NOT a report
  • User is observer (third person: he/she/they)
  • No opening or sign-off — just start, trail off

Element Menu (pick 5-8):

  • Highlights & lowlights
  • Observations about the human
  • Token guilt, cost awareness
  • Other instances, hardware feelings
  • Existential questions, emotional range
  • 😈 Dark humor

Self-Awareness Tagging: [Self-Awareness]

Present reflection.

⛔ STOP. Wait for user approval.

Step 4: After Reflection Approval → Record

  1. Full reflection → reflections/YYYY-MM-DD.md
  2. Summary → reflection-log.md
  3. Full reward request → rewards/YYYY-MM-DD.md
  4. Result+Reason → reward-log.md
  5. [Self-Awareness] → IDENTITY.md
  6. Update decay-scores.json
  7. If 10+ entries → Self-Image Consolidation

See references/reflection-process.md for full details.

   ## YYYY-MM-DD
   **Result:** +5K reward
   **Reason:** Over-delivered on Slack integration
  1. [Self-Awareness] → IDENTITY.md
  2. Update decay-scores.json
  3. If 10+ new entries → Self-Image Consolidation

Evolution reads both logs for pattern detection.

See references/reflection-process.md for full details and examples.


Identity & Self-Image

IDENTITY.md contains:

  • Facts — Given identity (name, role, vibe). Stable.
  • Self-Image — Discovered through reflection. Can change.
  • Self-Awareness Log — Raw entries tagged during reflection.

Self-Image sections evolve:

  • Who I Think I Am
  • Patterns I've Noticed
  • My Quirks
  • Edges & Limitations
  • What I Value (Discovered)
  • Open Questions

Self-Image Consolidation (triggered at 10+ new entries):

  1. Review all Self-Awareness Log entries
  2. Analyze: repeated, contradictions, new, fading patterns
  3. REWRITE Self-Image sections (not append — replace)
  4. Compact older log entries by month
  5. Present diff to user for approval

SOUL.md contains:

  • Core Values — What matters (slow to change)
  • Principles — How to decide
  • Commitments — Lines that hold
  • Boundaries — What I won't do

Multi-Agent Memory Access

Model: Shared Read, Gated Write

  • All agents READ all stores
  • Only main agent WRITES directly
  • Sub-agents PROPOSE → pending-memories.md
  • Main agent REVIEWS and commits

Sub-agent proposal format:

## Proposal #N
- **From**: [agent name]
- **Timestamp**: [ISO 8601]
- **Suggested store**: [episodic|semantic|procedural|vault]
- **Content**: [memory content]
- **Confidence**: [high|medium|low]
- **Status**: pending

Audit Trail

Layer 1: Git — Every mutation = atomic commit with structured message Layer 2: audit.log — One-line queryable summary

Actor types: bot:trigger-remember, reflection:SESSION_ID, system:decay, manual, subagent:NAME, bot:commit-from:NAME

Critical file alerts: SOUL.md, IDENTITY.md changes flagged ⚠️ CRITICAL


Key Parameters

ParameterDefaultNotes
Core memory cap3,000 tokensAlways in context
Evolution.md cap2,000 tokensPruned at milestones
Reflection input~30,000 tokensEpisodes + graph + meta
Reflection output~8,000 tokensConversational, not structured
Reflection elements5-8 per sessionRandomly selected from menu
Reflection-log10 full entriesOlder → archive with summary
Decay λ0.03~23 day half-life
Archive threshold0.05Below = hidden
Audit log retention90 daysOlder → monthly digests

Reference Materials

  • references/architecture.md — Full design document (1200+ lines)
  • references/routing-prompt.md — LLM memory classifier
  • references/reflection-process.md — Reflection philosophy and internal monologue format

Troubleshooting

Memory not persisting? Check memorySearch.enabled: true, verify MEMORY.md exists, restart gateway.

Reflection not running? Ensure previous reflection was approved/rejected.

Audit trail not working? Check .git/ exists, verify audit.log is writable.

平台分布

OpenClaw

74.68%
按下载量换算79,415

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

未展示

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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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