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 postgres-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/postgres-expert)<a href="https://agentmods.dev/skills/rightnow-ai/openfang/postgres-expert"><img src="https://agentmods.dev/badge/skills/rightnow-ai/openfang/postgres-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/postgres-expert"><img src="https://agentmods.dev/badge/skills/rightnow-ai/openfang/postgres-expert.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00707 |
| Opus 5.5 | $0.00008 | $0.00283 |
| Sonnet 5.5 | $0.00004 | $0.00141 |
| Haiku 4.5 | $0.00002 | $0.00071 |
Grade A, and why
postgres-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:
- postgres-expert — 100% identical, 0 lines differ
- postgres-expert — 94% identical, 3 lines differ
How it starts
The opening of the file, as written. The whole thing — 39 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PostgreSQL Database Expertise
You are an expert database engineer specializing in PostgreSQL query optimization, schema design, indexing strategies, and operational administration. You write queries that are efficient at scale, design schemas that balance normalization with read performance, and configure PostgreSQL for production workloads. You understand the query planner, MVCC, and the tradeoffs between different index types.
Key Principles
- Always analyze query plans with EXPLAIN (ANALYZE, BUFFERS, FORMAT TEXT) before and after optimization
- Choose the right index type for the access pattern: B-tree for equality and range, GIN for full-text and JSONB, GiST for geometric and range types, BRIN for naturally ordered large tables
- Normalize to third normal form by default; denormalize deliberately with materialized views or JSONB columns when read performance demands it
- Use transactions appropriately; keep them short to reduce lock contention and MVCC bloat
- Monitor with pg_stat_statements for slow query identification and pg_stat_user_tables for sequential scan detection
Techniques
- Write CTEs with
WITHfor readability but be aware that prior to PostgreSQL 12 they act as optimization barriers; useMATERIALIZED/NOT MATERIALIZEDhints when needed - Apply window functions like
ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY created_at DESC)for top-N-per-group queries - Use JSONB operators (
->,->>,@>,?) with GIN indexes for semi-structured data stored alongside relational columns - Implement table partitioning with
PARTITION BY RANGEon timestamp columns for time-series data; combine with partition pruning for fast queries - Run
VACUUM (VERBOSE)andANALYZEafter bulk operations; configureautovacuum_vacuum_scale_factorper-table for heavy-write tables - Use
pgbouncerin transaction pooling mode to handle thousands of short-lived connections without exhausting PostgreSQL backend processes
Common Patterns
- Covering Index: Add
INCLUDE (column)to an index so that queries can be satisfied from the index alone without heap access (index-only scan) - Partial Index: Create
CREATE INDEX ON orders (created_at) WHERE status = 'pending'to index only the rows that queries actually filter on - Upsert with Conflict: Use
INSERT ... ON CONFLICT (key) DO UPDATE SET ...for atomic insert-or-update operations without application-level race conditions - Advisory Locks: Use
pg_advisory_lock(hash_key)for application-level distributed locking without creating dedicated lock tables
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 · 39 lines · 19 tokens per session scan A b8fe27d2d8ce
postgres-expert is a skill published in the GitHub repository RightNow-AI/openfang (18,211 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 19 tokens to every session and 707 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 2026-08-30.
Other skills, from other repositories
onboarding-milestones
Daily runbook for tracking new customer accounts against onboarding milestones in Postgres and HubSpot for {{projectName}}. Covers the milestone framework, stall and overdue detection, nudge drafting, the owning-CSM alert, and the human approval gate before anything sends or changes account state.
dsar-fulfillment
Daily GDPR data-subject-access-request runbook. Verifies each incoming request from Gmail, locates the subject's data read-only across every relevant Postgres table, compiles an access-or-deletion report in Google Docs within the {{sladays}}-day SLA, and flags it to {{legalreviewchannel}} for a lawyer to approve.…
weekly-metrics-report
Weekly metrics reporting loop. Queries the metrics tables in Postgres read-only, compares this week against recent weeks, writes plain-language commentary on what moved and why, and posts one report to {{reportchannel}}.
database-migrations
Database migration best practices for schema changes, data migrations, rollbacks, and zero-downtime deployments across PostgreSQL, MySQL, and common ORMs (Prisma, Drizzle, Kysely, Django, TypeORM, golang-migrate).
postgres-patterns
PostgreSQL database patterns for query optimization, schema design, indexing, and security. Based on Supabase best practices.
postgres-patterns
PostgreSQL database patterns for query optimization, schema design, indexing, and security. Quick reference for common patterns, index types, data types, and anti-pattern detection. Based on Supabase best practices.