搜索

PythonReactSEOPDF
OpenClaw

hippocampus-memory-core

Deterministic external memory for OpenClaw and coding agents using S3-Hipokamp. Use for storing durable facts, retrieving prior decisions, snapshotting memory, and restoring agent state.

cezexpl0 来源安装数☆ 0 仓库 Stars

安装与使用

请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求,再添加到工作流。

SkillHub CLI

skillhub install cezexpl/hippocampus-memory-core

打开来源仓库,查看说明和兼容环境后,按来源提供的命令或步骤安装。

将来源链接提供给你的 Agent,让它先读取说明、核对依赖,并向你说明将执行的安装步骤。

查看来源 ↗使用指南

技能说明

Hippocampus Memory Core

Use this skill when you need long-term memory that survives the current turn.

Use It For

  • storing key decisions and facts that should outlive the current session
  • searching past architecture, deployment, or debugging context
  • creating snapshots before risky changes
  • restoring memory after migration or environment reset

Preferred Flow

  1. Check that Hippocampus configuration is present.
  2. Store only high-signal facts, not raw transcript spam.
  3. Search memory before repeating costly investigation.
  4. Snapshot before major refactors or rollout changes.
  5. Restore only into the correct agent or workspace scope.

Guidance

  • Prefer deterministic retrieval over ad hoc summaries.
  • Keep memory namespaced by workspace and agent.
  • Use metadata to mark project, topic, and task boundaries.
  • Treat memory as durable infrastructure, not scratchpad overflow.
  • If configuration is missing, send the user to hippocampus-openclaw-onboarding instead of asking for a raw API key first.

Related

  • hippocampus-openclaw-onboarding for first-time setup
  • hippocampus-subagent-memory for isolated child-agent memory
  • @hippocampus/openclaw-context-engine for native OpenClaw lifecycle integration

来源信息

Source
github
Repository
openclaw/skills
Path
skills/cezexpl/hippocampus-memory-core
Version
—
Commit
35cc9887f049b090e4d33fddee00f60d9282cc81
分类来源
rules
状态
active
可见性
public

这些信息来自收录时的来源分析,可能滞后;使用前请核对最新来源文件。

来源内容

SKILL.md
# Hippocampus Memory Core

Use this skill when you need long-term memory that survives the current turn.

## Use It For

- storing key decisions and facts that should outlive the current session
- searching past architecture, deployment, or debugging context
- creating snapshots before risky changes
- restoring memory after migration or environment reset

## Preferred Flow

1. Check that Hippocampus configuration is present.
2. Store only high-signal facts, not raw transcript spam.
3. Search memory before repeating costly investigation.
4. Snapshot before major refactors or rollout changes.
5. Restore only into the correct agent or workspace scope.

## Guidance

- Prefer deterministic retrieval over ad hoc summaries.
- Keep memory namespaced by workspace and agent.
- Use metadata to mark project, topic, and task boundaries.
- Treat memory as durable infrastructure, not scratchpad overflow.
- If configuration is missing, send the user to `hippocampus-openclaw-onboarding`
  instead of asking for a raw API key first.

## Related

- `hippocampus-openclaw-onboarding` for first-time setup
- `hippocampus-subagent-memory` for isolated child-agent memory
- `@hippocampus/openclaw-context-engine` for native OpenClaw lifecycle integration

相关技能

OpenClaw

ai-maestro

23blocks-os

AI Agent Orchestrator with Skills System - Give AI Agents superpowers: memory search, code graph queries, agent-to-agent messaging. Manage Claude, Codex or any AI Agent from one dashboard. Move Agents between c...

OpenClaw

registry-broker-skills

hashgraph-online

AI agent skills for the Universal Registry - search, chat, and register 72,000+ agents across 14+ protocols. Works with Claude, Codex, Cursor, OpenClaw, and any AI assistant.

OpenClaw

skills-manager

rito-w

A cross-platform skills manager for AI IDEs. Search marketplace, download locally, and install to Claude, Cursor, Windsurf, and more with one click.