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 PracticalSwan/agent-skills --skill mongodb-search-and-aigit clone --depth 1 https://github.com/PracticalSwan/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/practicalswan/agent-skills/mongodb-search-and-ai)<a href="https://agentmods.dev/skills/practicalswan/agent-skills/mongodb-search-and-ai"><img src="https://agentmods.dev/badge/skills/practicalswan/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/practicalswan/agent-skills/mongodb-search-and-ai"><img src="https://agentmods.dev/badge/skills/practicalswan/agent-skills/mongodb-search-and-ai.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 | $0.00132 | $0.01962 |
| Opus 5 | $0.00066 | $0.00981 |
| Sonnet 5 | $0.00026 | $0.00392 |
| Haiku 4.5 | $0.00013 | $0.00196 |
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 3d 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 — 191 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.
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?
- Do they need exact matching, fuzzy matching, or semantic similarity?
- Do they need filters (price ranges, categories, dates)?
- Do they need autocomplete/typeahead functionality?
2. Determine Search Type
Atlas Search (Lexical/Full-Text): Use when users need:
- Keyword matching with relevance scoring
- Fuzzy matching for typo tolerance
- Autocomplete/typeahead
- Faceted search with filters
- Language-specific text analysis
- Token-based search
- Lexical search with views
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.
- 3d ago Changed e513e590feb0
- 5d ago Changed a0a977b7b222
- 7d ago First seen · 191 lines · 132 tokens per session scan A 462027b94ace
mongodb-search-and-ai is a skill published in the GitHub repository PracticalSwan/agent-skills (14 stars, last pushed 3d ago), licensed MIT. It adds 132 tokens to every session and 1,962 once invoked, about $0.0007 per session on Opus 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.
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