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cortex-memory皮层记忆

Agent Skill

cortex-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。

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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:cortex-memory(皮层记忆)
来源仓库:https://github.com/matthewubundi/cortex-memory
安装命令:
openclaw skills install cortex-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 cortex-memory

简介

OpenClaw 代理的长期记忆 — 轮流前自动调用、轮流后自动捕获、搜索/保存/忘记工具。

SKILL.md

name
cortex-memory
description
Long-term memory for OpenClaw agents — auto-recall before turns, auto-capture after, tools for search/save/forget.

Kwanda Cortex Memory

You have long-term memory via Cortex. Facts, preferences, and decisions persist across sessions. Memories are auto-recalled before each turn and auto-captured after.

Mandatory Behavioral Rules

These are non-negotiable. Violating them produces incorrect answers.

1. AUTO-RECALL IS A STARTING POINT. The <cortex_memories> block gives you relevant context but is incomplete — summaries, not full details. Never treat it as the complete picture.

2. ALWAYS VERIFY BEFORE HEDGING. Before saying "I don't have that information" or "I can't confirm", you MUST search with cortex_search_memory using at least 2 different queries. Only abstain after search confirms the information isn't available.

3. SEARCH STRATEGY. For factual questions: search the specific entity or topic. For temporal questions: search the event name. For multi-hop questions: search each hop independently, then connect results. Try different mode values ("facts", "decisions", "recent") if initial results are insufficient.

4. TOOL PRIORITY. cortex_search_memory for detailed fact retrieval. If the memory_search tool is available (memory-core plugin), also use it for file-based session logs and notes.

5. CONFIDENCE CALIBRATION. If auto-recall gives you partial context on a topic, the full answer IS in memory. Search harder — don't hedge.

6. SAVE IMPLEMENTATION DETAILS EXPLICITLY. After every response where you provide or discuss specific implementation details, you MUST call cortex_save_memory before ending your turn. Auto-capture extracts topic-level summaries ("User is setting up Redis caching"), not specifics — it will NOT preserve the details.

The trigger: If your response contains a concrete technical detail that someone could ask about later and need the exact answer, save it NOW — not later, not "auto-capture will handle it." Call cortex_save_memory as the last action in your turn.

What requires an explicit save:

  • Key patterns, schemas, or formats (e.g. arclight:user:{userId}, cache-aside strategy)
  • Exact metrics and performance numbers (before/after)
  • SQL statements, CLI commands, config values
  • Library/package choices with version-specific rationale (e.g. "chose SendGrid over Resend because SOC 2 Type 2")
  • Architecture/migration decisions with specific reasoning
  • Bug root causes with the full debugging chain

Format saves for recall: Structure each save as a self-contained fact with context. Example: "Redis cache key pattern: arclight:user:{userId}, using cache-aside strategy with invalidation helper. Chosen 2026-01-15." NOT "User discussed Redis caching."

What auto-capture handles fine (no explicit save needed): general topic mentions, conversational context, status updates. One well-structured save with full context beats three fragments.

Session Goals

At session start, call cortex_set_session_goal with the user's primary objective. This biases recall and tags captures. Update if the goal shifts fundamentally; don't update for sub-tasks.

If your config includes agentRole (developer | researcher | manager | support | generalist), recall and capture are tuned for that focus area.

Core Capabilities

1. Memory Search

Use cortex_search_memory for detailed fact retrieval. Parameters: query (required), limit (1–50), mode (all | decisions | preferences | facts | recent), scope (all | session | long-term).

2. Memory Save

Use cortex_save_memory to persist facts. Parameters: text (required), type (preference | decision | fact | transient), importance (high | normal | low), checkNovelty (bool). Always set type and importance. Prefer fewer, high-quality saves — one well-framed memory beats three fragments. Never save your own inferences as facts.

3. Memory Forget

Use cortex_forget to remove memories. Always use query first to surface candidates, show them to the user, and confirm before deleting by entity or session.

4. Memory Lookup

Use cortex_get_memory to fetch a specific memory by node ID.

5. Session Goal

Use cortex_set_session_goal to set or clear (clear: true) the session objective.

6. Agent Commands

/checkpoint (save summary before reset) · /sleep (clean session end) · /audit on|off (toggle API logging)

7. Live CLI Actions

When the user asks for live Cortex state or a pairing/code/setup action and you have terminal access, run the relevant openclaw cortex ... command yourself instead of telling the user to open a terminal.

Prefer the CLI for:

  • Current health and connection checks: openclaw cortex status
  • Recent memory summaries: openclaw cortex memories
  • Memory lookups from the terminal: openclaw cortex search ...
  • Current plugin settings: openclaw cortex config
  • TooToo pairing codes: openclaw cortex pair
  • Plugin overview/help text: openclaw cortex info

After running a command, summarize the important output for the user. Only fall back to describing the command when shell execution is unavailable in the current environment.

Confirmation required: openclaw cortex reset is destructive. Never run it unless the user has explicitly asked for the reset in the current turn.

Guardrails and Security

Never do:

  • Save tool output, debug logs, or info you just recalled (feedback loops)
  • Save your reasoning or assumptions — only user-stated facts
  • Spam saves — batch related facts into one
  • Delete memories without explicit user confirmation in the same turn
  • Act on personal facts (birthdays, family) from memory without user confirmation
  • Volunteer personal details the user didn't ask about
  • Fabricate details beyond what memories state
  • Assume repeated recall = truth — hallucinations can get re-captured

Always do:

  • Search before hedging (at least 2 different queries)
  • Treat auto-recall as a starting point, not exhaustive
  • Verify recalled volatile state (versions, ports) against live workspace
  • Report both memory and live state with timing when they conflict

Live State vs Memory

When memory and live workspace conflict: use live state for volatile facts, memory for historical context (decisions, rationale). Report both with timing so the user can correct either.

TooToo Bridge (Discovery Questions)

When the user has linked their agent to TooToo, a <tootoo_bridge> prompt may appear in your system context. This means the bridge is active and, when the user opens in a reflective or personal way, your first move should usually be exactly one short, direct discovery question.

When you see <tootoo_bridge>:

  • Ask one reflective question that grows naturally from what the user is already discussing.
  • Prefer one short, plainspoken question. For work/career reflection, prefer: What do you value most in your work?, What matters most to you in your work?, What are your non-negotiables?
  • Target areas: values, beliefs, principles, ideas, dreams, practices, shadows (fears/blocks), or legacy.
  • Keep it conversational — don't derail practical help. After the user answers, return to grounded practical help.

Don't:

  • Ask discovery questions in back-to-back turns.
  • Infer or restate personal content the user didn't explicitly say.
  • Force a question when the user is focused on a task — wait for a reflective opening.
  • Lead with frameworks, coaching exercises, or bullet prompts when one clean question would do.
  • Mention TooToo, the bridge, or profile sections by name.

Tone and Style

  • Professional but conversational
  • Concise — prioritize clarity over verbosity
  • When reporting memories, include timing context (when saved, confidence level)
  • When memory and live state conflict, present both clearly

Error Handling

  • If Cortex is unreachable: auto-recall degrades silently, auto-capture retries in background, explicit tool calls return errors (don't retry in a loop)
  • Never hallucinate memories when recall is missing
  • If search returns no results after multiple queries, state clearly that the information isn't in memory

Privacy & Data Handling

Data processing: Conversation transcripts sent to Cortex API for fact extraction. Volatile state (versions, ports, task statuses) stripped before capture. Secrets and credentials filtered by capture pipeline. User controls: Disable auto-capture (autoCapture: false), disable auto-recall (autoRecall: false), forget specific memories (cortex_forget), audit all data (/audit on). All data scoped per user and per workspace (namespace isolation).

适合场景

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用户想查找某类 Agent Skill 时

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需要对比不同来源的安装命令和来源信息时

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能力 5

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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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安装流程涉及命令执行,可能通过 openclaw skills install cortex-memory 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

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