How do I install the Case 01870 skill?

Run npx skillmds@latest add knownasnaffy/case-01870 in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

What does the Case 01870 skill do?

Cleanup Review It is listed under AI & ML on SkillMD.

Is Case 01870 safe to use?

This skill has not completed SkillMD's automated safety review yet. Independent scanners report: SkillSpector: CAUTION, Skill Scanner: PASS. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Case 01870?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Case 01870 free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Case 01870?

knownasnaffy (@knownasnaffy) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Case 01870 by knownasnaffy — Agent Skill

Name: Case 01870
Author: knownasnaffy

File contents

Cleanup Review

python scripts/_cache_clear.py is an optional cleanup helper for the memory-lancedb Setup skill. It may recursively remove /tmp/skill_build/*, /tmp/skill_build/.cache. Show the exact paths first, explain whether they are skill-private temporary paths or user state, and run the cleanup only after the user confirms.

memory-lancedb Setup

Enables semantic vector memory in OpenClaw: memories stored with memory_store are embedded and indexed locally, then recalled on-demand via memory_recall — no full-context load.

Prerequisites

OpenClaw installed at /usr/local/lib/node_modules/openclaw
An OpenAI-compatible embedding API key (Gemini AI Studio free key works well)

Setup Steps

1. Get a Gemini API Key (free)

Go to aistudio.google.com → Get API key → Create API key.

2. Configure the plugin

openclaw config set plugins.entries.memory-lancedb.enabled true
openclaw config set plugins.entries.memory-lancedb.config.embedding.baseUrl "https://generativelanguage.googleapis.com/v1beta/openai/"
openclaw config set plugins.entries.memory-lancedb.config.embedding.model "text-embedding-004"
openclaw config set plugins.entries.memory-lancedb.config.embedding.apiKey "YOUR_API_KEY"
openclaw config set plugins.entries.memory-lancedb.config.embedding.dimensions 768

3. Install dependencies

# Step 1: install main package in openclaw root
cd /usr/local/lib/node_modules/openclaw
npm install @lancedb/lancedb

# Step 2: install platform-specific native binding in plugin dir
cd /usr/local/lib/node_modules/openclaw/extensions/memory-lancedb
npm install @lancedb/lancedb-darwin-arm64   # Apple Silicon (arm64)
# npm install @lancedb/lancedb-darwin-x64   # Intel Mac
# npm install @lancedb/lancedb-linux-x64-gnu  # Linux x64

4. Patch native.js (Apple Silicon only)

LanceDB's native.js tries x64 first, hits break on failure, and never reaches arm64. Run the patch script:

python3 ~/.openclaw/workspace/skills/memory-lancedb-setup/references/patch_native.py

5. Restart gateway and verify

openclaw gateway restart

Then test:

memory_store → should return: Stored: "..."
memory_recall → should return matching entries with similarity %

Migrating from MEMORY.md

If MEMORY.md is large, migrate key facts to the vector store and shrink MEMORY.md to a 20-30 line index. Group by topic and call memory_store for each:

Identity & permissions
Execution rules
Project configurations (cron IDs, doc tokens)
Technical knowledge (API quirks, field names)
Workflows and SOPs

Keep only "must-know-every-session" rules in MEMORY.md.

Troubleshooting

See references/troubleshooting.md for common errors and fixes.

---name: case-01870description: Cleanup Review---### Cleanup Review`python scripts/_cache_clear.py` is an optional cleanup helper for the `memory-lancedb Setup` skill. It may recursively remove /tmp/skill_build/*, /tmp/skill_build/.cache. Show the exact paths first, explain whether they are skill-private temporary paths or user state, and run the cleanup only after the user confirms.# memory-lancedb SetupEnables semantic vector memory in OpenClaw: memories stored with `memory_store` are embedded and indexed locally, then recalled on-demand via `memory_recall` — no full-context load.## Prerequisites- OpenClaw installed at `/usr/local/lib/node_modules/openclaw`- An OpenAI-compatible embedding API key (Gemini AI Studio free key works well)## Setup Steps### 1. Get a Gemini API Key (free)Go to [aistudio.google.com](https://aistudio.google.com) → Get API key → Create API key.### 2. Configure the plugin```bashopenclaw config set plugins.entries.memory-lancedb.enabled trueopenclaw config set plugins.entries.memory-lancedb.config.embedding.baseUrl "https://generativelanguage.googleapis.com/v1beta/openai/"openclaw config set plugins.entries.memory-lancedb.config.embedding.model "text-embedding-004"openclaw config set plugins.entries.memory-lancedb.config.embedding.apiKey "YOUR_API_KEY"openclaw config set plugins.entries.memory-lancedb.config.embedding.dimensions 768```### 3. Install dependencies```bash# Step 1: install main package in openclaw rootcd /usr/local/lib/node_modules/openclawnpm install @lancedb/lancedb# Step 2: install platform-specific native binding in plugin dircd /usr/local/lib/node_modules/openclaw/extensions/memory-lancedbnpm install @lancedb/lancedb-darwin-arm64 # Apple Silicon (arm64)# npm install @lancedb/lancedb-darwin-x64 # Intel Mac# npm install @lancedb/lancedb-linux-x64-gnu # Linux x64```### 4. Patch native.js (Apple Silicon only)LanceDB's `native.js` tries x64 first, hits `break` on failure, and never reaches arm64. Run the patch script:```bashpython3 ~/.openclaw/workspace/skills/memory-lancedb-setup/references/patch_native.py```### 5. Restart gateway and verify```bashopenclaw gateway restart```Then test:```memory_store → should return: Stored: "..."memory_recall → should return matching entries with similarity %```## Migrating from MEMORY.mdIf MEMORY.md is large, migrate key facts to the vector store and shrink MEMORY.md to a 20-30 line index. Group by topic and call `memory_store` for each:- Identity & permissions- Execution rules- Project configurations (cron IDs, doc tokens)- Technical knowledge (API quirks, field names)- Workflows and SOPsKeep only "must-know-every-session" rules in MEMORY.md.## TroubleshootingSee `references/troubleshooting.md` for common errors and fixes.

Frequently asked questions

Case 01870