Agent Skills › EverClaw/EverClaw › memory-upgrade

memory-upgrade

GitHub

诊断并修复OpenClaw中因缺少嵌入配置导致的记忆搜索失效问题。通过本地推理启用混合搜索、会话索引等功能，无需API密钥，提升跨会话回忆能力。

memory-upgrade/SKILL.md EverClaw/EverClaw

Trigger Scenarios

memory_search返回空结果代理跨会话回忆效果差用户希望升级记忆系统全新安装OpenClaw后

Install

npx skills add EverClaw/EverClaw --skill memory-upgrade -g -y

More Options

Non-standard path

npx skills add https://github.com/EverClaw/EverClaw/tree/main/memory-upgrade -g -y

Use without installing

npx skills use EverClaw/EverClaw@memory-upgrade

指定 Agent (Claude Code)

npx skills add EverClaw/EverClaw --skill memory-upgrade -a claude-code -g -y

安装 repo 全部 skill

npx skills add EverClaw/EverClaw --all -g -y

预览 repo 内 skill

npx skills add EverClaw/EverClaw --list

SKILL.md

Frontmatter

{
    "name": "memory-upgrade",
    "description": "Diagnose and fix broken memory search in OpenClaw. Enables local embeddings, hybrid search (BM25+vector), session transcript indexing, MMR diversity, and temporal decay — all running locally with zero API keys. Use when: memory_search returns empty results, agent has poor cross-session recall, user wants to upgrade their memory system, or after a fresh OpenClaw install."
}

Memory Upgrade

Most OpenClaw installs have broken memory search — the memory_search tool returns empty results because no embedding provider is configured. OpenClaw auto-detects OpenAI → Google → Voyage keys; if none exist, embeddings stay disabled silently.

This skill fixes it with fully local inference. No API keys. No data leaves the machine.

Quick Start

# 1. Diagnose
bash scripts/diagnose.sh

# 2. Fix (patches openclaw.json, restart [REDACTED] after)
bash scripts/configure.sh

# 3. Restart [REDACTED]
openclaw [REDACTED] restart

# 4. Verify (waits for indexing, runs test query)
bash scripts/verify.sh

Optional Enhancements

# Organize memory files into clean directory structure
bash scripts/organize.sh

# Add YAML frontmatter tags to untagged files
bash scripts/tag.sh

What Gets Enabled

Feature	Details
Local embeddings	embeddinggemma-300m (~328MB GGUF, auto-downloads)
Hybrid search	BM25 keyword + vector semantic (70/30 weight)
Session transcripts	Past conversations become searchable
MMR diversity	Reduces duplicate/overlapping results (λ=0.7)
Temporal decay	Recent memories rank higher (30-day half-life)
Embedding cache	50k entries, avoids re-embedding unchanged text
File watcher	Auto-reindexes when memory files change