| name | sqlite-vectordb |
| description | SQLite vector DB for work log storage and semantic search. Use for indexing work logs, generating embeddings, semantic search, and DB maintenance. |
SQLITE VECTOR DB SKILL
Store work logs in a searchable vector database and provide semantic search infrastructure.
When to use:
- Add entry: After
/log-work execution (integrated with work-logger)
- Search: When previous work context is needed (called by work-context-finder)
- Delete: To clean up incorrect/outdated logs
Note: DB initializes automatically. No need to run init_db.py manually.
Add Entry
Index a markdown log into the vector DB.
uv run .claude/skills/sqlite-vectordb/scripts/add_entry.py \
--file "private-docs/work-logs/YYYY-MM-DD-slug.md" \
--summary "One-line summary" \
--tags "tag1,tag2"
--file, -f (required): Work log file path
--summary, -s (required): One-line summary for search indexing
--tags, -t (required): Comma-separated tags
Search
Semantic similarity search in work logs.
uv run .claude/skills/sqlite-vectordb/scripts/search.py \
--query "search terms" \
--limit 5
--query, -q (required): Search query
--limit, -l: Max results (default: 5)
--tag, -t: Filter by tag
--type, -T: Filter by log type
--json, -j: JSON output
Delete Entry
Remove a work log entry from the database.
uv run .claude/skills/sqlite-vectordb/scripts/delete_entry.py \
--file "private-docs/work-logs/YYYY-MM-DD-slug.md"
Initialize DB (Optional)
Manual schema creation. Usually not needed - other scripts auto-initialize.
uv run .claude/skills/sqlite-vectordb/scripts/init_db.py
<technical_specs>
- DB location:
private-docs/work-logs/.vector-db/work-logs.db
- Engine: SQLite +
sqlite-vec extension
- Embedding model:
all-MiniLM-L6-v2 (384-dim)
- Chunk types: summary, details, challenges, other
- Execution:
uv run with PEP 723 inline deps
- Schema reference: references/schema.md
</technical_specs>
Language requirement: All data stored in the vector DB MUST be written in English.
- Summary: English only (for consistent embedding quality)
- Tags: English only (e.g.,
auth, refactor, not 인증, 리팩토링)
- Query: English preferred for optimal search accuracy
- Rationale: Embedding model (
all-MiniLM-L6-v2) performs best with English text
Exit codes:
- 0: Success
- 1: File not found
- 2: DB error
Common fixes:
- DB corrupted: Delete
private-docs/work-logs/.vector-db/work-logs.db and re-run (auto-recreates)
- Missing deps: Run
uv sync
Valid usage:
uv run .claude/skills/sqlite-vectordb/scripts/add_entry.py \
--file "private-docs/work-logs/2026-01-16-auth-feature.md" \
--summary "Implement OAuth2 authentication" \
--tags "auth,oauth,security"
uv run .claude/skills/sqlite-vectordb/scripts/search.py \
--query "authentication login" --limit 3 --tag auth
Invalid usage:
uv run .../add_entry.py --file "missing.md" ...
uv run .../add_entry.py --file "..." --summary "" --tags "..."