Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/syi0808/screenize/sqlite-vectordbnpx skills add syi0808/screenize --skill sqlite-vectordbgit clone --depth 1 https://github.com/syi0808/screenizeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/syi0808/screenize/sqlite-vectordb)<a href="https://agentmods.dev/skills/syi0808/screenize/sqlite-vectordb"><img src="https://agentmods.dev/badge/skills/syi0808/screenize/sqlite-vectordb.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00033 | $0.00969 |
| Opus 5 | $0.00016 | $0.00485 |
| Sonnet 5 | $0.00007 | $0.00194 |
| Haiku 4.5 | $0.00003 | $0.00097 |
Grade A, and why
sqlite-vectordb scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SQLITE VECTOR DB SKILL
Store work logs in a searchable vector database and provide semantic search infrastructure.
When to use:
- Add entry: After
/log-workexecution (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-vecextension - Embedding model:
all-MiniLM-L6-v2(384-dim) - Chunk types: summary, details, challenges, other
- Execution:
uv runwith 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.
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 135 lines · 33 tokens per session scan A c43f632de582
sqlite-vectordb is a skill published in the GitHub repository syi0808/screenize (602 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 33 tokens to every session and 969 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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