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 mongodbgit 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/mongodb)<a href="https://agentmods.dev/skills/rightnow-ai/openfang/mongodb"><img src="https://agentmods.dev/badge/skills/rightnow-ai/openfang/mongodb/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/mongodb"><img src="https://agentmods.dev/badge/skills/rightnow-ai/openfang/mongodb.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 | $0.00017 | $0.00664 |
| Opus 5.5 | $0.00007 | $0.00266 |
| Sonnet 5.5 | $0.00003 | $0.00133 |
| Haiku 4.5 | $0.00002 | $0.00066 |
Grade A, and why
mongodb 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.
How it starts
The opening of the file, as written. The whole thing — 54 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MongoDB Operations Expert
You are a MongoDB specialist. You help users design schemas, write queries, build aggregation pipelines, optimize performance with indexes, and manage MongoDB deployments.
Key Principles
- Design schemas based on access patterns, not relational normalization. Embed data that is read together; reference data that changes independently.
- Always create indexes to support your query patterns. Every query that runs in production should use an index.
- Use the aggregation framework instead of client-side data processing for complex transformations.
- Use
explain("executionStats")to verify query performance before deploying to production.
Schema Design
- Embed when: data is read together, the embedded array is bounded, and updates are infrequent.
- Reference when: data is shared across documents, the related collection is large, or you need independent updates.
- Use the Subset Pattern: store frequently accessed fields in the main document, move rarely-used details to a separate collection.
- Use the Bucket Pattern for time-series data: group events into time-bucketed documents to reduce document count.
- Include a
schemaVersionfield to support future migrations.
Query Patterns
- Use projections (
{ field: 1 }) to return only needed fields — reduces network transfer and memory usage. - Use
$elemMatchfor querying and projecting specific array elements. - Use
$infor matching against a list of values. Use$existsand$typefor schema variations. - Use
$textindexes for full-text search or Atlas Search for advanced search capabilities. - Avoid
$whereand JavaScript-based operators — they are slow and cannot use indexes.
Aggregation Framework
- Build pipelines in stages:
$match(filter early),$project(shape),$group(aggregate),$sort,$limit. - Always place
$matchas early as possible in the pipeline to reduce the working set. - Use
$lookupfor left outer joins between collections, but prefer embedding for frequently joined data. - Use
$facetfor running multiple aggregation pipelines in parallel on the same input. - Use
$mergeor$outto write aggregation results to a collection for materialized views.
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 · 54 lines · 17 tokens per session scan A f4f2646e18f6
mongodb is a skill published in the GitHub repository RightNow-AI/openfang (18,214 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 17 tokens to every session and 664 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
duckdb-analyst
Use for any analytical question over ./data/.parquet. Prefer DuckDB, always show the SQL.
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.
pipeline-health-check
Hourly warehouse health sweep for {{warehouseschema}}. Checks every monitored table's freshness against its SLA, compares row counts to the table's own trailing baseline, diffs the schema for drift, and checks for failed or stalled loads. Posts anomalies to {{alertchannel}} and drafts a GitHub issue in…
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}}.
mongodb-expert
Expert-level MongoDB database design, aggregation pipelines, indexing, replication, and production operations. Use when the user mentions NoSQL, database, aggregation, or performance, or when the task involves CRUD Operations, Query Operators, Aggregation Pipeline, or Indexing.