Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Semantic search over local text files using embeddings. Use when grep/ripgrep fails to find relevant results because the exact wording is unknown, or when se...
Semantic search over local text files using embeddings. Use when grep/ripgrep fails to find relevant results because the exact wording is unknown, or when se...
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Semantic grep for the terminal. Searches files by meaning using local embeddings (BAAI/bge-small-en-v1.5 + FAISS). No API keys needed.
grep or ripgrep returned no results or irrelevant results You don't know the exact wording of what you're looking for You want to search by concept/meaning rather than exact text Do NOT use semfind when grep works โ grep is instant and has zero overhead.
pip install semfind First run downloads a ~65MB model (~10-30s). Subsequent runs use the cached model.
# Basic search semfind "deployment issue" logs.md # Search multiple files, top 3 results semfind "permission error" memory/*.md -k 3 # With context lines semfind "database migration" notes.md -n 2 # Force re-index after file changes semfind "query" file.md --reindex # Minimum similarity threshold semfind "auth bug" *.md -m 0.5
FlagDescriptionDefault-k, --top-kNumber of results5-n, --contextContext lines before/after0-m, --max-distanceMinimum similarity scorenone--reindexForce re-embedfalse--no-cacheSkip embedding cachefalse
Grep-like with similarity scores: file.md:9: [2026-01-15] Fixed docker build with missing env vars (0.796) file.md:3: [2026-01-17] Agent couldn't write to /var/log (0.689) Higher scores (closer to 1.0) mean stronger semantic match.
~250MB RAM while running, freed immediately on exit ~65MB model cached in /tmp/fastembed_cache/ ~2s first query (model load), ~14ms cached queries Embedding cache in ~/.cache/semfind/, auto-invalidates on file changes
# Step 1: Try grep first grep "deployment" memory/*.md # Step 2: If grep fails, use semfind semfind "something went wrong with the deployment" memory/*.md -k 5
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Largest current source with strong distribution and engagement signals.