claude-codex-settings is a collection of configurations and reusable extensions for Claude Code, OpenAI Codex, Cursor, and related coding tools. Developers use its skills, commands, hooks, agents, plugins, and MCP servers to shape coding-agent workflows and connect alternative model APIs. The catalogue entries are components of this collection that can be installed into supported coding tools.
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 fcakyon/claude-codex-settings --skill mongodb-search-and-aigit clone --depth 1 https://github.com/fcakyon/claude-codex-settingsWrote 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/fcakyon/claude-codex-settings/mongodb-search-and-ai)<a href="https://agentmods.dev/skills/fcakyon/claude-codex-settings/mongodb-search-and-ai"><img src="https://agentmods.dev/badge/skills/fcakyon/claude-codex-settings/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/fcakyon/claude-codex-settings/mongodb-search-and-ai"><img src="https://agentmods.dev/badge/skills/fcakyon/claude-codex-settings/mongodb-search-and-ai.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 | $0.00132 | $0.01418 |
| Opus 5 | $0.00066 | $0.00709 |
| Sonnet 5 | $0.00026 | $0.00284 |
| Haiku 4.5 | $0.00013 | $0.00142 |
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 7d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- mongodb-search-and-ai — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 143 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
4 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.
- 7d ago First seen · 143 lines · 132 tokens per session scan A 551d1669ab85
mongodb-search-and-ai is a skill published in the GitHub repository fcakyon/claude-codex-settings (1,139 stars, last pushed 2d ago), licensed Apache-2.0. It adds 132 tokens to every session and 1,418 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.
Other skills, from other repositories
sqlite-vec-skilld
ALWAYS use when writing code importing "sqlite-vec". Consult for debugging, best practices, or modifying sqlite-vec, sqlite vec.
qdrant-clients-sdk
Qdrant provides client SDKs for various programming languages, allowing easy integration with Qdrant deployments.
neo4j-document-import-skill
Ingests unstructured and semi-structured documents into Neo4j as a knowledge graph. Use when chunking PDFs, HTML, plain text, or Markdown; extracting entities and relationships from text with an LLM (SimpleKGPipeline, neo4j-graphrag); loading JSON via apoc.load.json; building Document→Chunk→Entity graph structures; or…
neo4j-vector-index-skill
Create and manage Neo4j vector indexes, run vector similarity search (ANN/kNN), store embeddings on nodes or relationships, use SEARCH clause (Neo4j 2026.01+, preferred) or db.index.vector.queryNodes() procedure (deprecated 2026.04, still works on 2025.x), configure HNSW and quantization options, pick similarity…
neo4j-graphrag-skill
Build GraphRAG retrieval pipelines on Neo4j using the neo4j-graphrag Python package (v1.16.0+). Covers retriever selection (VectorRetriever, HybridRetriever, VectorCypherRetriever, HybridCypherRetriever, Text2CypherRetriever, ToolsRetriever), external vector DB retrievers (Weaviate, Pinecone, Qdrant), retrievalquery…
neo4j-genai-plugin-skill
Use Neo4j GenAI Plugin ai.text. functions and procedures for in-Cypher embedding generation, text completion, structured output, chat, tokenization, and batch ingestion. Covers ai.text.embed(), ai.text.embedBatch(), ai.text.completion(), ai.text.structuredCompletion(), ai.text.aggregateCompletion(), ai.text.chat()…