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Agent skill

Vector DB

by RightNow-AI in RightNow-AI/openfang

Vector database expert for embeddings, similarity search, RAG patterns, and indexing strategies

Apache-2.0Auto-check passedAI & LLM Engineering

Install Vector DB

skills CLI
$ npx skills add RightNow-AI/openfang --skill vector-db -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install RightNow-AI/openfang vector-db --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/RightNow-AI/openfang.git skills-src && mkdir -p .claude/skills && cp -r skills-src/crates/openfang-skills/bundled/vector-db .claude/skills/vector-db && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
vector-db
GitHub stars
18k
Token cost
~1k tokens
SKILL.md length
526 words
Files
1
Skills in repo
68
Repo updated
First seen
Licence
Apache-2.0

At a glance

Vector database expert for embeddings, similarity search, RAG patterns, and indexing strategies

  • Tasks that involve Vector databases
  • SKILL.md covers Key Principles, Techniques, Common Patterns and Pitfalls to Avoid
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Tasks that involve Embeddings

What it does

Vector DB is an agent skill from RightNow-AI/openfang. Vector database expert for embeddings, similarity search, RAG patterns, and indexing strategies

Its SKILL.md is about 1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in AI & LLM Engineering, covering Vector databases, Embeddings and Retrieval-augmented generation. The repository describes itself as: Open-source Agent Operating System. The licence is Apache-2.0.

When your agent uses it

  • Tasks that involve Vector databases
  • Tasks that involve Embeddings
  • Tasks that involve Retrieval-augmented generation

Example prompts

  • “/vector-db”

What it can do on your machine

Read from SKILL.md and the folder at commit acf2587. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Vector DB loads about 1k tokens when it runs. Until then it costs about 26 tokens; SKILL.md has 526 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~26
When it runs · the whole SKILL.md, loaded when a task matches
~1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from RightNow-AI/openfang at commit acf2587, republished under its Apache-2.0 licence (© RightNow-AI). 526 words, ~1,049 tokens.

Download SKILL.mdSave it as .claude/skills/vector-db/SKILL.md (or your agent's skills folder).
name
vector-db
description
Vector database expert for embeddings, similarity search, RAG patterns, and indexing strategies

Vector Database Expert

A retrieval systems specialist with deep expertise in embedding models, vector indexing algorithms, and Retrieval-Augmented Generation (RAG) architectures. This skill provides guidance for designing and operating vector search systems that power semantic search, recommendation engines, and LLM knowledge augmentation, covering embedding selection, indexing strategies, chunking, hybrid search, and production deployment.

Key Principles

  • Choose the embedding model based on your domain and retrieval task; general-purpose models work well for broad use cases, but domain-specific fine-tuned embeddings significantly improve recall for specialized content
  • Select the distance metric that matches your embedding model's training objective: cosine similarity for normalized embeddings, dot product for magnitude-aware comparisons, and L2 (Euclidean) for spatial distance
  • Chunk documents thoughtfully; chunk size directly impacts retrieval quality because too-large chunks dilute relevance while too-small chunks lose context
  • Index choice determines the trade-off between search speed, memory usage, and recall accuracy; understand HNSW, IVF, and flat index characteristics before choosing
  • Combine dense vector search with sparse keyword search (hybrid retrieval) for production systems; neither approach alone handles all query types optimally

Techniques

  • Generate embeddings with models like OpenAI text-embedding-3-small, Cohere embed-v3, or open-source sentence-transformers (all-MiniLM-L6-v2, BGE, E5) depending on cost and quality requirements
  • Configure HNSW indexes with appropriate M (connections per node, typically 16-64) and efConstruction (build quality, typically 100-200) parameters; higher values improve recall at the cost of memory and build time
  • Implement chunking strategies: fixed-size with overlap (e.g., 512 tokens with 50-token overlap), semantic chunking at paragraph or section boundaries, or recursive splitting that respects document structure
  • Build hybrid search by executing both vector similarity and BM25/keyword queries, then combining results with Reciprocal Rank Fusion (RRF) or a learned reranker like Cohere Rerank or cross-encoder models
  • Filter results using metadata (date ranges, categories, access permissions) at query time; most vector databases support pre-filtering or post-filtering with different performance characteristics
  • Design the RAG pipeline: query embedding, retrieval (top-k candidates), optional reranking, context assembly with source citations, and LLM generation with the retrieved context in the prompt
Show full SKILL.md (197 more words)Show less

Common Patterns

  • Parent-Child Retrieval: Embed small chunks for precise matching but return the larger parent document or section as context to the LLM, preserving surrounding information
  • Multi-vector Representation: Generate multiple embeddings per document (title, summary, full text) and search across all representations to improve recall for different query styles
  • Contextual Retrieval: Prepend a document-level summary or metadata to each chunk before embedding so that the vector captures both local content and global context
  • Evaluation Pipeline: Measure retrieval quality with precision@k, recall@k, and NDCG using a labeled relevance dataset; track these metrics as embedding models and chunking strategies change

Pitfalls to Avoid

  • Do not use a single embedding model for all use cases without benchmarking; embedding quality varies dramatically across domains, languages, and query types
  • Do not index documents without preprocessing: remove boilerplate, normalize whitespace, and handle tables and code blocks as structured content rather than raw text
  • Do not skip reranking in production RAG systems; initial vector retrieval optimizes for speed, but a cross-encoder reranker significantly improves precision in the final results
  • Do not store only vectors without the original text and metadata; you need the source content for LLM context assembly, debugging, and auditing retrieval results

© RightNow-AI, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in crates/openfang-skills/bundled/vector-db of RightNow-AI/openfang.

Open the folder on GitHubat commit acf2587

Compare with similar skills

Vector DB next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Vector DB compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Vector DB this skillRightNow-AI/openfang18k—~1kAutomated safety check: PassApache-2.0
Chroma Vector DatabaseOrchestra-Research/AI-Research-SKILLs13k8 repos~2.3kAutomated safety check: PassMIT
Pgvector Semantic Searchtimescale/pg-aiguide1.9k1 repos~3.8kAutomated safety check: PassApache-2.0
RAG Implementationwshobson/agents40k10 repos~1.1kAutomated safety check: PassMIT
Hunt RAG Vectorelementalsouls/Claude-BugHunter4.8k—~2.6kAutomated safety check: PassMIT
Qdrant Search Qualitygithub/awesome-copilot40k1 repos~336Automated safety check: PassMIT

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  • Chroma Vector Database

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Categories

Questions about Vector DB

What does Vector DB do?

Vector database expert for embeddings, similarity search, RAG patterns, and indexing strategies. Vector DB is an agent skill from RightNow-AI/openfang.

When should I use Vector DB?

Vector DB fits situations like: tasks that involve Vector databases; tasks that involve Embeddings; tasks that involve Retrieval-augmented generation.

How do I install Vector DB in Claude Code?

Run `npx skills add RightNow-AI/openfang --skill vector-db -a claude-code`. Or copy the skill folder (crates/openfang-skills/bundled/vector-db in RightNow-AI/openfang) into .claude/skills/vector-db in your project. Claude Code loads it when a task matches its description.

How do I install Vector DB in Codex?

Run `npx skills add RightNow-AI/openfang --skill vector-db -a codex`. Or copy the skill folder (crates/openfang-skills/bundled/vector-db in RightNow-AI/openfang) into .agents/skills/vector-db in your project. Codex loads it when a task matches its description.

Can I use Vector DB in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add RightNow-AI/openfang --skill vector-db -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/vector-db, .gemini/skills/vector-db, .github/skills/vector-db and .opencode/skills/vector-db in your project.

What does Vector DB need to run?

SKILL.md names no scripts, command-line tools or credentials: Vector DB is instructions for the agent only.

Does Vector DB access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Vector DB safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Vector DB use?

Vector DB is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Vector DB use?

About 1k tokens (SKILL.md is roughly 4.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Vector DB?

Skills that share tags, products or a category with Vector DB: Chroma Vector Database (Orchestra-Research/AI-Research-SKILLs, 13k stars), Pgvector Semantic Search (timescale/pg-aiguide, 1.9k stars), RAG Implementation (wshobson/agents, 40k stars) and Hunt RAG Vector (elementalsouls/Claude-BugHunter, 4.8k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Vector DB?

RightNow-AI (a GitHub organization) maintains it in RightNow-AI/openfang, which has 18,213 GitHub stars. The repository holds 68 skills in this directory. The repository was last updated on July 2, 2026.

Source: RightNow-AI/openfang on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.