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 librefang/librefang-registry --skill mongodbgit clone --depth 1 https://github.com/librefang/librefang-registryWrote 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/librefang/librefang-registry/mongodb)<a href="https://agentmods.dev/skills/librefang/librefang-registry/mongodb"><img src="https://agentmods.dev/badge/skills/librefang/librefang-registry/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/librefang/librefang-registry/mongodb"><img src="https://agentmods.dev/badge/skills/librefang/librefang-registry/mongodb.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00017 | $0.00689 |
| Opus 5.5 | $0.00007 | $0.00276 |
| Sonnet 5.5 | $0.00003 | $0.00138 |
| Haiku 4.5 | $0.00002 | $0.00069 |
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.
This is a copy
97% identical to mongodb — 3 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 57 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 · 57 lines · 17 tokens per session scan A bd544928a78b
mongodb is a skill published in the GitHub repository librefang/librefang-registry (11 stars, last pushed 1mo ago), licensed MIT. It adds 17 tokens to every session and 689 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 97% identical to mongodb, differing in 3 lines, and is treated as a copy.
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