Case 01870

Skill knownasnaffy/prompthound/dataset/case_01870

A fast, offline static risk analysis CLI for AI agent skill files. Detects malicious instructions, steganographic payloads, and dangerous capability chains.

Install

npx -y skills add knownasnaffy/prompthound --skill case_01870

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What its author says it does

Copied from the file, not written here

Configure OpenClaw's memory-lancedb plugin for semantic vector memory using a local LanceDB database. Use when: (1) setting up vector memory for the first time, (2) memory-lancedb fails with module not found errors, (3) migrating from flat-file MEMORY.md to vector-based recall, (4) configuring an embedding provider (Gemini, OpenAI-compatible). NOT for: general memory_store/memory_recall usage (just use the tools directly).

SKILL.md

3.2 KB, 686 tokens by cl100k_base, as published. Nobody here has run it

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.