semantic-grep
Installation
SKILL.md
Semantic Grep
jina-grep-style semantic search, done in-process via Python rather than as an external CLI. Embeds query + corpus chunks with gemini-embedding-001, ranks by cosine similarity, returns grep-format output.
When Semantic Search Helps
The core trade-off (lifted from jina-grep-cli's own docs and validated in testing):
| Task | Tool |
|---|---|
| Known exact string, filename, or regex | grep / rg / searching-codebases |
| "What files discuss concept X" when X may not appear verbatim | semantic-grep |
| Hybrid: prefilter with grep, rerank by concept | grep → rerank_candidates() |
Regression test result (workshop session corpus, 135 docs):
- "handling regulatory constraints" → top hit "Engineering AI Systems Under Sovereignty Constraints" (0.67). ✓
- "sessions about GEPA" → top hit "Gemma, DeepMind's Family of Open Models" (0.69). ✗ — false positive on phonetic neighbor. GEPA is mentioned verbatim in one session description; grep would find it correctly.
Rule: when the user query reads like a named entity or keyword, try grep first. Only reach for semantic-grep when paraphrase/concept matching is actually needed.