Chroma Vector Database
Orchestra-Research/AI-Research-SKILLs
Shows how to store documents and embeddings in Chroma, query them by similarity with metadata filters, and persist them to disk for RAG and semantic search projects.
A skill your agent uses when the user asks to design a RAG pipeline, choose a chunking strategy or embedding model, pick a vector database, or evaluate retrieval quality (precision@k, recall@k, NDCG).
Install RAG Architect
$ npx skills add alirezarezvani/claude-skills --skill rag-architect -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install alirezarezvani/claude-skills rag-architect --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/alirezarezvani/claude-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/engineering/skills/rag-architect .claude/skills/rag-architect && rm -rf skills-srcUse ~/.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/
Install the "rag-architect" agent skill from https://github.com/alirezarezvani/claude-skills/tree/main/engineering/skills/rag-architect into .claude/skills/rag-architect/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rag-architect", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/alirezarezvani/claude-skills/tree/main/engineering/skills/rag-architectType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add alirezarezvani/claude-skills --skill rag-architect -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install alirezarezvani/claude-skills rag-architect --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/alirezarezvani/claude-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/engineering/skills/rag-architect .agents/skills/rag-architect && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "rag-architect" agent skill from https://github.com/alirezarezvani/claude-skills/tree/main/engineering/skills/rag-architect into .agents/skills/rag-architect/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rag-architect", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add alirezarezvani/claude-skills --skill rag-architect -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install alirezarezvani/claude-skills rag-architect --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/alirezarezvani/claude-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/engineering/skills/rag-architect .cursor/skills/rag-architect && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "rag-architect" agent skill from https://github.com/alirezarezvani/claude-skills/tree/main/engineering/skills/rag-architect into .cursor/skills/rag-architect/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rag-architect", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/alirezarezvani/claude-skills.git --path engineering/skills/rag-architect--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add alirezarezvani/claude-skills --skill rag-architect -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install alirezarezvani/claude-skills rag-architect --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/alirezarezvani/claude-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/engineering/skills/rag-architect .gemini/skills/rag-architect && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "rag-architect" agent skill from https://github.com/alirezarezvani/claude-skills/tree/main/engineering/skills/rag-architect into .gemini/skills/rag-architect/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rag-architect", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install alirezarezvani/claude-skills rag-architectInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add alirezarezvani/claude-skills --skill rag-architect -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/alirezarezvani/claude-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/engineering/skills/rag-architect .github/skills/rag-architect && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "rag-architect" agent skill from https://github.com/alirezarezvani/claude-skills/tree/main/engineering/skills/rag-architect into .github/skills/rag-architect/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rag-architect", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add alirezarezvani/claude-skills --skill rag-architect -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install alirezarezvani/claude-skills rag-architect --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/alirezarezvani/claude-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/engineering/skills/rag-architect .opencode/skills/rag-architect && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "rag-architect" agent skill from https://github.com/alirezarezvani/claude-skills/tree/main/engineering/skills/rag-architect into .opencode/skills/rag-architect/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rag-architect", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
rag-architect- GitHub stars
- 28k
- Token cost
- ~1.1k tokens
- SKILL.md length
- 424 words
- Files
- 7 (incl. references)
- Skills in repo
- 342
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when the user asks to design a RAG pipeline, choose a chunking strategy or embedding model, pick a vector database, or evaluate retrieval quality (precision@k, recall@k, NDCG).
- Works in 4 steps: Analyze the corpus and pick chunking → Design the pipeline from requirements → Evaluate retrieval quality → …
- The user asks to design a RAG pipeline
- SKILL.md covers Hard rules, Embedding model tiers…, Workflow and References
- Runs Python scripts from its folder; calls python3
What it does
RAG Architect is an agent skill from alirezarezvani/claude-skills. Use when the user asks to design a RAG pipeline, choose a chunking strategy or embedding model, pick a vector database, or evaluate retrieval quality (precision@k, recall@k, NDCG). Examples: 'design a RAG system for our docs', 'what chunk size should I use for this corpus', 'evaluate my retriever against ground truth'. NOT for general LLM cost tuning (use llm-cost-optimizer) or agent loops over retrieval (use agenthub).
Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including reference files (for example `chunking_optimizer.py`, `rag_pipeline_designer.py` and `references/chunking_strategies_comparison.md`).
It sits in AI & LLM Engineering, covering Retrieval-augmented generation, Embeddings and Vector databases. The repository describes itself as: 380 Claude Code skills & agent skills & plugins (30+ Agents, 70+ custom commands, 380+ skills, customizable references, scripts)for Claude Code, Codex, Gemini CLI, Cursor, and 8… The licence is MIT.
When your agent uses it
- The user asks to design a RAG pipeline
- Choose a chunking strategy
- Embedding model
- Pick a vector database
Example prompts
- “design a RAG system for our docs”
- “what chunk size should I use for this corpus”
- “evaluate my retriever against ground truth”
- “/rag-architect”
Requirements
- Python 3
Workflow steps
4 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 19392f7. 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
Ships script files (Python), which the agent can run.
Shell commands in SKILL.md call:
python3
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
RAG Architect loads about 1.1k tokens when it runs, and up to ~11k if it reads all its reference files. Until then it costs about 109 tokens; SKILL.md has 424 words of instructions outside code blocks.
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 alirezarezvani/claude-skills at commit 19392f7, republished under its MIT licence (© alirezarezvani). 424 words, ~1,121 tokens.
Download SKILL.mdSave it as
.claude/skills/rag-architect/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.- name
- rag-architect
- description
- Use when the user asks to design a RAG pipeline, choose a chunking strategy or embedding model, pick a vector database, or evaluate retrieval quality (precision@k, recall@k, NDCG). Examples: 'design a RAG system for our docs', 'what chunk size should I use for this corpus', 'evaluate my retriever against ground truth'. NOT for general LLM cost tuning (use llm-cost-optimizer) or agent loops over retrieval (use agenthub).
RAG Architect
Design, tune, and evaluate production RAG pipelines with three deterministic tools. Run the tools against the actual corpus and requirements — do not pick chunk sizes or databases by intuition.
Hard rules
- Never present model names or vendor prices as current facts. Embedding models and vector-DB pricing rot in months. Recommend a tier (see table below), name a current-generation candidate, and tell the user to verify against the provider's live pricing page.
- Every design ends with an evaluation run. A RAG design without
retrieval_evaluator.pynumbers is a hypothesis, not a deliverable. - Chunking is corpus-driven. Run
chunking_optimizer.pyon the real documents before choosing a strategy.
Embedding model tiers (pattern, not price list)
| Tier | Current-generation examples (verify before use) | When |
|---|---|---|
| Fast / self-hosted | all-MiniLM-L6-v2, bge-small | Cost-sensitive, small scale, real-time |
| Balanced open | all-mpnet-base-v2, bge-large, e5-large | Quality without API dependency |
| Quality API | text-embedding-3-large, voyage-3-large | Accuracy-priority general retrieval |
| Code | voyage-code-3, CodeBERT-family | Code search corpora |
Pricing discipline: build the cost model with a placeholder table — columns model | $/1M tokens (verify) | dims | as-of date — and have the user fill in live numbers. Same for vector DBs (Pinecone/Weaviate/Qdrant/Chroma/pgvector): the selection criteria (managed vs self-hosted, scale, filtering, existing Postgres) are durable; the dollar figures are not.
Workflow
All paths relative to this skill folder. Outputs chain: corpus analysis → design → evaluation.
1. Analyze the corpus and pick chunking
python3 chunking_optimizer.py /path/to/docs --extensions .md .txt -o chunking.jsonEmits chunking.json with corpus_info, per-strategy strategy_results, a recommendation, and sample_chunks. Use recommendation.strategy and its config; show the user 2-3 sample_chunks so they can sanity-check boundaries.
Show full SKILL.md (178 more words)Show less
2. Design the pipeline from requirements
Write a requirements JSON with these keys (all required): document_types[], document_count, avg_document_size (chars), queries_per_day, query_patterns[], latency_requirement, budget_monthly, accuracy_priority (0-1), cost_priority (0-1), maintenance_complexity.
python3 rag_pipeline_designer.py requirements.json -o design.jsonEmits design.json with chunking, embedding, vector_db, retrieval, reranking, evaluation, total_cost, architecture_diagram (mermaid), and config_templates. Present the diagram; label every cost_monthly figure as an estimate to verify (rule 1).
3. Evaluate retrieval quality
Prepare queries.json (list of {id, text} or {"queries": [...]}) and ground_truth.json ({query_id: [relevant_doc_ids]}), then:
python3 retrieval_evaluator.py queries.json /path/to/docs ground_truth.json --k-values 3 5 10 -o eval.jsonReports precision@k, recall@k, MRR, NDCG@k, plus poor_precision_examples / poor_recall_examples for failure analysis.
4. Verification loop
The design is done only when:
eval.jsonmeets targets — typical floors: precision@5 ≥ 0.8, recall@10 ≥ 0.85 (set per use case with the user).- If below target: inspect the poor-example lists, then change one variable (chunking strategy → re-run step 1; embedding tier; add reranking; hybrid retrieval) and re-run step 3. Repeat.
- Every recommended model/price in the deliverable carries a "verify current pricing/model availability" note with an as-of date.
References
references/chunking_strategies_comparison.md— strategy trade-offs the optimizer implementsreferences/embedding_model_benchmark.md— benchmark methodology (dated snapshot; staleness warning at top)references/rag_evaluation_framework.md— metric definitions (faithfulness, relevance, precision/recall/NDCG)
© alirezarezvani, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 6 other files (references) in engineering/skills/rag-architect of alirezarezvani/claude-skills.
- SKILL.md
- chunking_optimizer.py
- rag_pipeline_designer.py
- references/chunking_strategies_comparison.md
- references/embedding_model_benchmark.md
- references/rag_evaluation_framework.md
- retrieval_evaluator.py
Open the folder on GitHubat commit 19392f7
Compare with similar skills
RAG Architect 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| RAG Architect this skillalirezarezvani/claude-skills | 28k | — | ~1.1k | Automated safety check: Pass | MIT | |
| Chroma Vector DatabaseOrchestra-Research/AI-Research-SKILLs | 13k | 7 repos | ~2.3k | Automated safety check: Pass | MIT | |
| Ms Agent Framework RAGshuyu-labs/WebCode | 278 | — | ~1.1k | Automated safety check: Pass | Custom licence | |
| Pgvector Semantic Searchtimescale/pg-aiguide | 1.9k | — | ~3.8k | Automated safety check: Pass | Apache-2.0 | |
| RAG Implementationwshobson/agents | 40k | 9 repos | ~1.1k | Automated safety check: Pass | MIT | |
| RAG Engineerdavila7/claude-code-templates | 32k | 5 repos | ~729 | Automated safety check: Pass | MIT |
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Related
Categories
Questions about RAG Architect
What does RAG Architect do?
A skill your agent uses when the user asks to design a RAG pipeline, choose a chunking strategy or embedding model, pick a vector database, or evaluate retrieval quality (precision@k, recall@k, NDCG). RAG Architect is an agent skill from alirezarezvani/claude-skills. Use when the user asks to design a RAG pipeline, choose a chunking strategy or embedding model, pick a vector database, or evaluate retrieval quality (precision@k, recall@k, NDCG).
When should I use RAG Architect?
RAG Architect fits situations like: the user asks to design a RAG pipeline; choose a chunking strategy; embedding model; pick a vector database.
How do I install RAG Architect in Claude Code?
Run `npx skills add alirezarezvani/claude-skills --skill rag-architect -a claude-code`. Or copy the skill folder (engineering/skills/rag-architect in alirezarezvani/claude-skills) into .claude/skills/rag-architect in your project. Claude Code loads it when a task matches its description.
How do I install RAG Architect in Codex?
Run `npx skills add alirezarezvani/claude-skills --skill rag-architect -a codex`. Or copy the skill folder (engineering/skills/rag-architect in alirezarezvani/claude-skills) into .agents/skills/rag-architect in your project. Codex loads it when a task matches its description.
Can I use RAG Architect 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 alirezarezvani/claude-skills --skill rag-architect -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/rag-architect, .gemini/skills/rag-architect, .github/skills/rag-architect and .opencode/skills/rag-architect in your project.
What does RAG Architect need to run?
Going by SKILL.md and its folder, RAG Architect needs Python for the scripts in its folder and the command-line tools its instructions call (python3). Our summary lists: Python 3.
Does RAG Architect 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 RAG Architect 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 RAG Architect use?
RAG Architect is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does RAG Architect use?
About 1.1k tokens (SKILL.md is roughly 4.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 9.5k tokens, read only when the agent opens those files.
What are the alternatives to RAG Architect?
Skills that share tags, products or a category with RAG Architect: Chroma Vector Database (Orchestra-Research/AI-Research-SKILLs, 13k stars), Ms Agent Framework RAG (shuyu-labs/WebCode, 278 stars), Pgvector Semantic Search (timescale/pg-aiguide, 1.9k stars) and RAG Implementation (wshobson/agents, 40k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains RAG Architect?
alirezarezvani (a GitHub user) maintains it in alirezarezvani/claude-skills, which has 27,891 GitHub stars. The repository holds 342 skills in this directory. The repository was last updated on August 30, 2026.
Source: alirezarezvani/claude-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.