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 ericrisco/rsc-harness --skill mongodbgit clone --depth 1 https://github.com/ericrisco/rsc-harnessWrote 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/ericrisco/rsc-harness/mongodb)<a href="https://agentmods.dev/skills/ericrisco/rsc-harness/mongodb"><img src="https://agentmods.dev/badge/skills/ericrisco/rsc-harness/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/ericrisco/rsc-harness/mongodb"><img src="https://agentmods.dev/badge/skills/ericrisco/rsc-harness/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 | $0.00118 | $0.04886 |
| Opus 5.5 | $0.00047 | $0.01954 |
| Sonnet 5.5 | $0.00024 | $0.00977 |
| Haiku 4.5 | $0.00012 | $0.00489 |
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 — 343 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MongoDB — modeling, indexing, aggregation, transactions, ops
Engine-level MongoDB 8.2 guidance: model documents for the queries you actually run, pick the
index the planner will use, write aggregation pipelines that stay index-eligible, run
multi-document transactions with correct retry, and operate/secure a deployment. Driver-agnostic —
every example is mongosh shell syntax that maps 1:1 to the official drivers (Node, Python, Go,
Java, Rust). This skill owns the server's query and the index it picks, not any ODM's API.
When to use / When NOT to use
When to use:
- Document modeling: embed vs reference, the 16 MB cap, one-to-many/many-to-many, the subset/extended-reference/bucket/computed/outlier patterns, taming unbounded array growth.
- Index decisions: single-field, compound (the ESR ordering rule), multikey, partial, TTL, text,
wildcard,
2dsphere; and when an index is NOT worth it. - Any query that is slow or scans too much; reading
explain("executionStats"). - Aggregation pipelines: stage order so
$match/$sorthit an index,$lookupcost,$unwindexplosion,$group/$sortmemory limits andallowDiskUse,$merge/$out, faceting. - Multi-document transactions: sessions,
withTransactionretry semantics, read/write concern. - Operating/securing: replica set, read preference, write concern, Atlas tier choice, Atlas Search & Vector Search, Queryable Encryption, role-based access, connection-pool knobs.
When NOT to use:
- Relational schema / SQL /
EXPLAIN ANALYZE→postgresdb. Different engine, planner, and concurrency model. - ODM/driver API ergonomics (Mongoose pre-save hooks, the Node driver's
bulkWritereturn shape,updateMany's result object) → that tool's own docs. This skill owns the server query and the index the server picks, not the JS object the driver hands back. - App-layer caching as a product (Redis-in-front-of-reads).
- Cloud-console click-paths — we give the shell command / connection string, not the Atlas UI tour.
- Picking a vector store across engines (Pinecone vs Weaviate). Atlas Vector Search inside Mongo is in scope; cross-engine selection is not.
What ships with it
6 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.
- 1mo ago First seen · 343 lines · 118 tokens per session scan A bf13001664bd
mongodb is a skill published in the GitHub repository ericrisco/rsc-harness (231 stars, last pushed today), licensed MIT. It adds 118 tokens to every session and 4,886 once invoked, about $0.0005 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.
Other skills, from other repositories
mongodb
MongoDB - NoSQL document database with flexible schema design, aggregation pipelines, indexing strategies, and Spring Data integration.
huawei-cloud-dds-dcs-instance-management
Huawei Cloud DDS (Document Database Service) and DCS (Distributed Cache Service) unified management skill. Provides 16 operations across five capability domains: (1) Query — list/get DDS/DCS instances, DCS node info, DCS custom templates; (2) Analysis — analyze DDS deployment architecture and DCS security posture; (3)…
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.
nosql-database-design
Designs a NoSQL data model by leading with access pattern analysis. Covers DynamoDB single-table design (PK/SK/GSI) and MongoDB embedding vs referencing, consistency models, and capacity planning. Invoked when the user asks to design a DynamoDB schema, MongoDB data model, or NoSQL data model.
traverse-multi-hop
Expresses a multi-hop lineage question as a single variable-length path match against native graph storage, bounded by an explicit hop depth and an explicit relationship-type allowlist, instead of a recursive relational join that grows one level per hop.
transact-graph-write
Wraps every write to a shared graph edge in a transaction tied to the exact row version it was computed against, and retries a rejected write against the row's current state instead of dropping it or blindly reapplying stale values.