Memory Semantic Search

Semantic search over workspace markdown files using embedding API + SQLite vector store. Use when: (1) searching workspace notes/memory by meaning rather tha...

This skill appears to implement a legitimate local Markdown semantic-search tool, but take these precautions before installing/using it: - Expect to provide an embeddings API key (EMBEDDING_API_KEY); the registry metadata omitted this — verify environment requirements before trusting the package. - By default it will send the full text of your Markdown chunks to the configured embedding endpoint (default: https://api.openai.com/v1). Only use a provider you trust, or configure a self-hosted/enterprise-compatible embedding endpoint if your notes are sensitive. - Avoid indexing secrets or credentials. Use a workspace path that excludes sensitive files, or add exclusions before running index.py. - Consider setting --db to a controlled path (not a global skill directory) and protect that SQLite file appropriately. - Review/verify EMBEDDING_API_BASE if you need embeddings to stay in your environment (e.g., Ollama, internal proxy). If you need privacy guarantees, confirm the embedding provider’s retention policy. - The main technical inconsistency is the missing declared env vars in the registry; if this skill will run in an automated agent environment, confirm the platform will surface the required API key prompt before the skill runs. If you want me to, I can: (1) point out the exact lines that transmit data to the network, (2) suggest a small patch to redact or exclude sensitive files before indexing, or (3) show how to change the default DB path and embedding base in the code.

Type: OpenClaw Skill Name: memory-semantic-search Version: 1.0.0 The skill bundle provides a legitimate semantic search utility for markdown files using Python's standard library. It indexes workspace content into a local SQLite database and uses a user-configured OpenAI-compatible API for generating embeddings (scripts/index.py and scripts/search.py). The behavior is transparent, well-documented, and strictly follows the stated purpose without any signs of malicious intent, obfuscation, or prompt injection.