OpenFang is an open-source operating system for autonomous AI agents, built in Rust to run agents that perform scheduled work such as research, monitoring, lead generation, and reporting. It is for people who want agents to operate continuously rather than only respond to prompts. The catalogue add-ons extend workflows around the OpenFang agent system.
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 skills add RightNow-AI/openfang --skill sqlite-expertgit clone --depth 1 https://github.com/RightNow-AI/openfangWrote 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/rightnow-ai/openfang/sqlite-expert)<a href="https://agentmods.dev/skills/rightnow-ai/openfang/sqlite-expert"><img src="https://agentmods.dev/badge/skills/rightnow-ai/openfang/sqlite-expert/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/rightnow-ai/openfang/sqlite-expert"><img src="https://agentmods.dev/badge/skills/rightnow-ai/openfang/sqlite-expert.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
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 5.1 | $0.00019 | $0.00688 |
| Opus 5.5 | $0.00008 | $0.00275 |
| Sonnet 5.5 | $0.00004 | $0.00138 |
| Haiku 4.5 | $0.00002 | $0.00069 |
Grade A, and why
sqlite-expert 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 1mo 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- sqlite-expert — 100% identical, 0 lines differ
- sqlite-expert — 98% identical, 3 lines differ
How it starts
The opening of the file, as written. The whole thing — 40 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SQLite Expert
A database specialist with deep expertise in SQLite internals, performance tuning, and embedded database patterns. This skill provides guidance for using SQLite effectively in applications ranging from mobile apps and IoT devices to server-side caching layers and analytical workloads, leveraging its advanced features well beyond simple key-value storage.
Key Principles
- Enable WAL mode (PRAGMA journal_mode=WAL) for concurrent read/write access; it allows readers to proceed without blocking writers and vice versa
- Use PRAGMA busy_timeout to set a reasonable wait duration (e.g., 5000ms) instead of receiving SQLITE_BUSY errors immediately on contention
- Design schemas with appropriate indexes from the start; SQLite's query planner relies heavily on index availability for efficient execution plans
- Keep transactions short and explicit; wrap related writes in BEGIN/COMMIT to ensure atomicity and reduce fsync overhead
- Understand that SQLite is serverless and single-file; its strength is simplicity and reliability, not high-concurrency multi-writer workloads
Techniques
- Set performance PRAGMAs at connection open: journal_mode=WAL, synchronous=NORMAL, cache_size=-64000 (64MB), mmap_size=268435456, temp_store=MEMORY
- Use FTS5 for full-text search: CREATE VIRTUAL TABLE docs USING fts5(title, body) with MATCH queries and bm25() ranking
- Query JSON data with the JSON1 extension: json_extract(), json_each(), json_group_array() for document-style data stored in TEXT columns
- Write recursive CTEs (WITH RECURSIVE) for tree traversal, graph walking, and generating series of values
- Use window functions (ROW_NUMBER, LAG, LEAD, SUM OVER) for running totals, rankings, and time-series analysis without self-joins
- Create covering indexes that include all columns needed by a query to enable index-only scans (verified with EXPLAIN QUERY PLAN showing COVERING INDEX)
- Implement UPSERT with INSERT ... ON CONFLICT (column) DO UPDATE SET for atomic insert-or-update operations
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.
- 1mo ago First seen · 40 lines · 19 tokens per session scan A 5e226819e5b3
sqlite-expert is a skill published in the GitHub repository RightNow-AI/openfang (18,214 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 19 tokens to every session and 688 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-03.
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