Markdown Docs Full-Text Search

Full-text search across structured Markdown documentation archives using SQLite FTS5. Use when you need to search large collections of Markdown articles that...

This bundle appears to be a straightforward local Markdown indexing/search tool. Before installing/running: 1) Confirm you will point the tool at an intended docs directory — it will read all *.md files recursively and create a SQLite DB (e.g., competitor_docs.db) in or near that directory. 2) Note there are no network calls or credential requests in the code, so it does not exfiltrate secrets. 3) The included code references 'competitor' and product names in comments and _meta.json (slug/version mismatch with the registry metadata) — this is likely repurposed code and not a functional risk, but you may want to verify the source if provenance matters. 4) Ensure your Python runtime has SQLite built with FTS5; otherwise searches may fail. 5) If you run it against very large directories, expect disk and CPU usage while building the index. If you need further assurance, run the scripts in a controlled environment (a temporary directory or container) to observe behavior and output before pointing them at sensitive folders.

Type: OpenClaw Skill Name: md-docs-search Version: 1.0.2 The skill bundle is designed for local documentation search and indexing, which is a benign purpose. However, it contains several vulnerabilities that make it suspicious. The most critical is a Regular Expression Denial of Service (ReDoS) risk in `scripts/search_docs.py`, where user-supplied search queries are directly compiled and executed as regex patterns, potentially leading to resource exhaustion. Additionally, the FTS5 search in `scripts/fts_search.py` could be vulnerable to Denial of Service via maliciously crafted complex queries, and the `db_path` argument across scripts allows for arbitrary file creation or overwrite, posing a file integrity risk.