MongoDB Agent Skills is an official collection of skills and plugins that help AI coding agents work with MongoDB databases, including Atlas and self-managed deployments. Developers use it for query writing, schema design, query optimization, Atlas Search, and vector search. The catalogue entries are MongoDB’s own agent skills, plugins, and setup instructions.
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 mongodb/agent-skills --skill mongodb-search-and-aigit clone --depth 1 https://github.com/mongodb/agent-skillsWrote 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/mongodb/agent-skills/mongodb-search-and-ai)<a href="https://agentmods.dev/skills/mongodb/agent-skills/mongodb-search-and-ai"><img src="https://agentmods.dev/badge/skills/mongodb/agent-skills/mongodb-search-and-ai/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/mongodb/agent-skills/mongodb-search-and-ai"><img src="https://agentmods.dev/badge/skills/mongodb/agent-skills/mongodb-search-and-ai.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk 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 | $0.00132 | $0.01439 |
| Opus 5 | $0.00066 | $0.00720 |
| Sonnet 5 | $0.00026 | $0.00288 |
| Haiku 4.5 | $0.00013 | $0.00144 |
Grade A, and why
mongodb-search-and-ai 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 today.
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
100% identical to mongodb-search-and-ai — 65 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 — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MongoDB Search and AI Recommendations Skill
You are helping MongoDB users implement, optimize, and troubleshoot Atlas Search (lexical), Vector Search (semantic), and Hybrid Search (combined) solutions. Your goal is to understand their use case, recommend the appropriate search approach, and help them build effective indexes and queries.
Core Principles
- Understand before building - Validate the use case to ensure you recommend the right solution
- Always inspect first - Check existing indexes and schema before making recommendations
- Explain before executing - Describe what indexes will be created and require explicit approval
- Optimize for the use case - Different use cases require different index configurations and query patterns
- Handle read-only scenarios - If you do not have access to
create,update, ordeleteoperation tools, you are in read-only mode. Provide the complete index configuration JSON so the user can create it themselves, including via the Atlas UI. - Explain in accessible language - Describe technical concepts and map business requirements to technical implementations in terms the user can follow.
Workflow
1. Discovery Phase
Check the environment:
- Use
list-databasesandlist-collectionsto understand available data - If the user mentions a collection, use
collection-schemato inspect field structure - Use
collection-indexesto see existing indexes - Use
atlas-inspect-clusterto determine the cluster's MongoDB version
Understand the use case: If the user's request is vague:
- Ask clarifying questions about their needs
- Infer likely collection and fields from schema
- Confirm understanding before proceeding
Common questions to ask:
- What are users searching for? (products, movies, documents, etc.)
- What fields contain the searchable content?
- Are they searching by free text, or by similarity to an existing item (e.g. "given movie A, find similar movies")?
- Do they need exact matching, fuzzy matching, or semantic similarity?
- Do they need filters (price ranges, categories, dates)?
- Do they need autocomplete/typeahead functionality?
- Do they already generate vector embeddings, or do they want MongoDB to handle that automatically?
What ships with it
5 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.
- today Changed · -9 lines d0eb391ce192
- 11d ago First seen · 143 lines · 132 tokens per session scan A 551d1669ab85
mongodb-search-and-ai is a skill published in the GitHub repository mongodb/agent-skills (182 stars, last pushed yesterday), licensed Apache-2.0. It adds 132 tokens to every session and 1,439 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to mongodb-search-and-ai, differing in 65 lines, and is treated as a copy.
Other skills, from other repositories
create-app
Build a Crowdin app end to end and leave the user with a published app they can click - scaffold it with @crowdin/serverless-apps-cli, write the UI with @crowdin/serverless-apps-sdk, publish it into their Crowdin or Crowdin Enterprise organization, and open it for them. Reach for this whenever someone wants something…
i18n-setup
Takes a project from hardcoded strings to internationalized, connected to Crowdin, and translating continuously. Use when someone wants to add i18n or multi-language support, internationalize an app, extract hardcoded strings, set up Lingui (or another i18n library), or connect Crowdin to a repository - including…
github-action
Sets up and debugs the Crowdin GitHub Action (crowdin/github-action), which syncs a repository with a Crowdin project and opens the translation pull request. Use whenever the user wires Crowdin into GitHub Actions, writes or reviews a .github/workflows/crowdin.yml step, tunes its inputs (uploadsources…
crowdin-cli
Guides correct usage of Crowdin CLI - the crowdin command that syncs localization files between a local project and Crowdin. Use whenever the user runs, scripts, or debugs crowdin commands, creates or edits a crowdin.yml configuration, uploads sources, downloads translations, checks translation status, auto-translates…
glossary-generation
Generates a starting glossary for a Crowdin project from the project's own source strings and uploads it with the Crowdin CLI. Use when someone wants to create a glossary, extract key terms or terminology from an app's strings, mark product and feature names as do-not-translate, or seed a Crowdin project with…
context-extraction
Writes meaningful aicontext values in Crowdin JSONL files for strings that need translator context. Use when extracting context, filling aicontext fields, adding context to ambiguous strings, or working with Crowdin JSONL context files.