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 alivirgo/Major-AI-Skills --skill chromadbgit clone --depth 1 https://github.com/alivirgo/Major-AI-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/alivirgo/major-ai-skills/chromadb)<a href="https://agentmods.dev/skills/alivirgo/major-ai-skills/chromadb"><img src="https://agentmods.dev/badge/skills/alivirgo/major-ai-skills/chromadb/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/alivirgo/major-ai-skills/chromadb"><img src="https://agentmods.dev/badge/skills/alivirgo/major-ai-skills/chromadb.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 | $0.00039 | $0.02193 |
| Opus 5.5 | $0.00016 | $0.00877 |
| Sonnet 5.5 | $0.00008 | $0.00439 |
| Haiku 4.5 | $0.00004 | $0.00219 |
Grade A, and why
chromadb 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 4d 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 — 198 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ChromaDB Vector Store AI Skill Guide (Claude)
Overview & Engine Architecture
Chroma is an embedding-first vector store: documents (or precomputed vectors) land in collections, each locked to a fixed embedding dimension and distance space after first write. Clients run embedded (PersistentClient / ephemeral Client) or client–server (HttpClient / AsyncHttpClient). Agents treat the embedding function + distance metric as a retrieval contract, persist paths intentionally, and never mix models across upsert and query without a new collection rebuild.
┌─────────────────────────────────────────────────────────────┐
│ Chroma retrieval stack │
│ │
│ Clients │
│ ├── Ephemeral Client() — in-memory notebooks/tests │
│ ├── PersistentClient(path) — local embedded + on-disk │
│ └── HttpClient / AsyncHttpClient — remote Chroma server │
│ │
│ Collection contract │
│ ├── EmbeddingFunction (default: all-MiniLM-L6-v2 / 384-d) │
│ ├── Space: hnsw.space = l2 | cosine | ip (immutable) │
│ └── Dimension locked on first embedding write │
│ │
│ Read path │
│ ├── query(query_texts|query_embeddings) + where │
│ ├── where_document ($contains / $regex) │
│ └── get(ids|filters) — no similarity ranking │
└─────────────────────────────────────────────────────────────┘
When to use / when not to
Use when
- Local/dev RAG with durable collections beside
@langchain/@llamaindex. - Metadata-filtered nearest-neighbor retrieval (ACL tags, source path, tenant).
- Embedded apps that ship a single-node persist directory.
What ships with it
2 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.
- 4d ago Changed · +92 lines · +15 tokens per session f32e411f62d2
- 1mo ago Changed · -2 tokens per session 83056fd64e1b
- 1mo ago First seen · 106 lines · 26 tokens per session scan A 33ce039f5ecf
chromadb is a skill published in the GitHub repository alivirgo/Major-AI-Skills (2 stars, last pushed yesterday), licensed MIT. It adds 39 tokens to every session and 2,193 once invoked, about $0.0002 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 2026-09-05.
Other skills, from other repositories
vector-databases
Vector database engineering covering Pinecone, Weaviate, Chroma, Qdrant, pgvector, and FAISS — including embedding pipeline design, HNSW index parameter tuning, hybrid dense+sparse (BM25) search, metadata filtering, namespace and tenant sharding, and RAG retrieval patterns for production knowledge systems.
rag-qdrant
High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.
chroma
Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source…
vector-collection-management
Create, populate, list, and delete vector-store collections through the vector-mcp MCP server's vectorcollectionmanagement tool. Use when the agent must stand up a new RAG collection, ingest documents (from a directory, file paths/URLs, or raw text) into an existing collection, enumerate collections, or drop one …
chroma
Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source…
qdrant-vector-search
High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.