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.
Design RAG pipelines: chunking, retrieval evaluation, and architecture.
Install RAG Architect
$ npx skills add borghei/Claude-Skills --skill rag-architect -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install borghei/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/borghei/Claude-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/engineering/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/borghei/Claude-Skills/tree/main/engineering/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/borghei/Claude-Skills/tree/main/engineering/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 borghei/Claude-Skills --skill rag-architect -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install borghei/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/borghei/Claude-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/engineering/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/borghei/Claude-Skills/tree/main/engineering/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 borghei/Claude-Skills --skill rag-architect -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install borghei/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/borghei/Claude-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/engineering/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/borghei/Claude-Skills/tree/main/engineering/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/borghei/Claude-Skills.git --path engineering/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 borghei/Claude-Skills --skill rag-architect -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install borghei/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/borghei/Claude-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/engineering/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/borghei/Claude-Skills/tree/main/engineering/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 borghei/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 borghei/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/borghei/Claude-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/engineering/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/borghei/Claude-Skills/tree/main/engineering/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 borghei/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 borghei/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/borghei/Claude-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/engineering/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/borghei/Claude-Skills/tree/main/engineering/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
- 874
- Token cost
- ~1.8k tokens
- SKILL.md length
- 704 words
- Files
- 9 (incl. references)
- Skills in repo
- 364
- Repo updated
- First seen
- Licence
- MIT
At a glance
Design RAG pipelines: chunking, retrieval evaluation, and architecture.
- Building a RAG system
- SKILL.md covers Core Capabilities, When to Use, Clarify First and Tools, plus 3 more sections
- Runs Python scripts from its folder; calls python
- Selecting a chunking strategy
What it does
RAG Architect is an agent skill from borghei/Claude-Skills. Design RAG pipelines: chunking, retrieval evaluation, and architecture. Use when building a RAG system, selecting a chunking strategy, choosing a vector database, optimizing retrieval quality, or evaluating with RAGAS metrics.
Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 9 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 and Vector databases. The repository describes itself as: 385 AI skills, 77 expert agents, and 900 stdlib Python tools for every team: engineering, PM, marketing, C-level, compliance, business ops, research, and a LinkedIn toolkit… The licence is MIT.
When your agent uses it
- Building a RAG system
- Selecting a chunking strategy
- Choosing a vector database
- Optimizing retrieval quality
Example prompts
- “/rag-architect”
Requirements
- Python 3
What it can do on your machine
Read from SKILL.md and the folder at commit c9a1487. 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:
python
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 1.8k tokens when it runs, and up to ~15k if it reads all its reference files. Until then it costs about 60 tokens; SKILL.md has 704 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 borghei/Claude-Skills at commit c9a1487, republished under its MIT licence (© borghei). 704 words, ~1,785 tokens.
Download SKILL.mdSave it as
.claude/skills/rag-architect/SKILL.md (or your agent's skills folder). This skill also uses 8 other files; get the full folder from GitHub.- name
- rag-architect
- description
- Design RAG pipelines: chunking, retrieval evaluation, and architecture. Use when building a RAG system, selecting a chunking strategy, choosing a vector database, optimizing retrieval quality, or evaluating with RAGAS metrics.
- license
- MIT + Commons Clause
- metadata.version
- 1.1.0
- metadata.author
- borghei
- metadata.category
- engineering
- metadata.domain
- ai-ml
- metadata.tier
- POWERFUL
- metadata.updated
- 2026-06-17
RAG Architect
The agent designs, implements, and optimizes production-grade RAG pipelines, from document chunking through evaluation.
Core Capabilities
- Chunking strategy selection — match corpus characteristics to fixed-size, sentence, paragraph, semantic, recursive, or document-aware chunking with sized parameters.
- Embedding & vector-DB choice — pick an embedding model (local vs API) and vector store (Pinecone, Weaviate, Qdrant, Chroma, pgvector) by scale, latency, and cost.
- Retrieval design — dense, sparse (BM25), or hybrid retrieval with Reciprocal Rank Fusion plus cross-encoder reranking when precision must exceed 0.85.
- Query transformations — HyDE, multi-query, and step-back techniques for style mismatch and ambiguous queries.
- Guardrails — PII detection, hallucination/NLI checks, source attribution, confidence scoring, and injection prevention.
- Evaluation — RAGAS faithfulness/relevance plus IR metrics (Precision@K, Recall@K, MRR, NDCG) with failure analysis.
- Production patterns — caching, streaming, fallbacks, incremental re-indexing, and cost control.
When to Use
- Building a RAG system end to end.
- Selecting a chunking strategy or choosing a vector database.
- Optimizing retrieval quality or adding reranking.
- Evaluating a pipeline with RAGAS or IR metrics.
Clarify First
Before designing the pipeline, confirm these inputs. If any is unknown or vague, ASK — do not assume:
- Corpus characteristics — size, document structure, and domain (drives the chunking-strategy selection and parameters)
- Scale / latency / cost constraints — query volume and budget (selects the embedding model and vector DB)
- Retrieval precision target — the accuracy bar (precision >0.85 forces hybrid retrieval + cross-encoder reranking)
- Query types — ambiguous, multi-hop, or style-mismatched (decides which query transforms: HyDE / multi-query / step-back)
Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.
Tools
Python tools live at the skill root (no scripts/ dir). Full flags/output formats: references/tool-cli-reference.md.
| Tool | Purpose | Command |
|---|---|---|
chunking_optimizer.py | Analyze a corpus and recommend the optimal chunking strategy with parameters | python chunking_optimizer.py ./docs --output results.json |
retrieval_evaluator.py | Evaluate retrieval with Precision@K, Recall@K, MRR, NDCG + failure analysis | python retrieval_evaluator.py queries.json ./corpus ground_truth.json |
rag_pipeline_designer.py | Generate a full pipeline design, cost projection, and Mermaid diagram from requirements | python rag_pipeline_designer.py requirements.json --output pipeline_design.json |
References
Load the reference that matches the task — keep this file lean and pull detail on demand:
- references/rag-design-guide.md — the 8-step workflow, every selection matrix (chunking, embedding, vector DB, retrieval, query transforms), context-window optimization, RAGAS targets, guardrails, a worked YAML pipeline example, production patterns, common pitfalls, troubleshooting table, and success criteria. Read when designing or debugging a pipeline.
- references/tool-cli-reference.md — full flag/parameter tables, examples, and output formats for
chunking_optimizer.py,retrieval_evaluator.py, andrag_pipeline_designer.py. Read before running the scripts. - references/chunking_strategies_comparison.md — deep comparison of the five chunking strategies with size distributions, quality metrics, and domain recommendations. Read when choosing a chunking strategy.
- references/embedding_model_benchmark.md — benchmark of OpenAI, open-source, specialized, and domain-specific embedding models. Read when selecting an embedding model.
- references/rag_evaluation_framework.md — full evaluation framework: retrieval/generation/end-to-end dimensions, offline/online/human methodologies, metric implementations. Read when building an evaluation harness.
Show full SKILL.md (245 more words)Show less
Scope & Limitations
This skill covers:
- End-to-end RAG pipeline architecture design: chunking, embedding, vector storage, retrieval, reranking, and evaluation.
- Quantitative chunking analysis across four strategy families (fixed-size, sentence, paragraph, semantic).
- Retrieval quality evaluation using standard IR metrics (Precision@K, Recall@K, MRR, NDCG) with a built-in TF-IDF baseline.
- Automated pipeline design with component selection, cost projection, and Mermaid architecture diagrams.
This skill does NOT cover:
- LLM prompt engineering or generation-side optimization -- see
engineering/prompt-engineer-toolkit. - Database schema design for metadata stores alongside vector databases -- see
engineering/database-designer. - Production observability, alerting, and SLO dashboards for deployed pipelines -- see
engineering/observability-designer. - Agent orchestration or multi-step reasoning workflows that sit on top of RAG retrieval -- see
engineering/agent-workflow-designer.
Integration Points
| Skill | Integration | Data Flow |
|---|---|---|
engineering/prompt-engineer-toolkit | Optimize system prompts and few-shot examples fed alongside retrieved chunks | Pipeline design output --> prompt templates that reference chunk format and metadata |
engineering/database-designer | Design relational metadata stores (tags, access control, source tracking) paired with the vector database | Vector DB recommendation --> metadata schema for hybrid storage |
engineering/observability-designer | Set up latency, throughput, and accuracy monitoring for the deployed RAG pipeline | Evaluation metrics and SLO targets --> dashboards and alerting rules |
engineering/agent-workflow-designer | Embed the RAG retrieval step inside multi-agent reasoning workflows | Retrieval config --> agent tool definition with top-K and threshold parameters |
engineering/ci-cd-pipeline-builder | Automate embedding re-indexing, evaluation regression tests, and deployment on document changes | Evaluation thresholds --> CI gate that blocks deploys when metrics regress |
engineering/api-design-reviewer | Review the query and ingestion API surface exposed by the RAG service | Pipeline config --> OpenAPI spec review for search and ingest endpoints |
© borghei, 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 8 other files (references) in engineering/rag-architect of borghei/Claude-Skills.
- SKILL.md
- chunking_optimizer.py
- rag_pipeline_designer.py
- references/chunking_strategies_comparison.md
- references/embedding_model_benchmark.md
- references/rag-design-guide.md
- references/rag_evaluation_framework.md
- references/tool-cli-reference.md
- retrieval_evaluator.py
Open the folder on GitHubat commit c9a1487
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 skillborghei/Claude-Skills | 874 | — | ~1.8k | Automated safety check: Pass | MIT | |
| Chroma Vector DatabaseOrchestra-Research/AI-Research-SKILLs | 13k | 8 repos | ~2.3k | Automated safety check: Pass | MIT | |
| Pgvector Semantic Searchtimescale/pg-aiguide | 1.9k | 1 repos | ~3.8k | Automated safety check: Pass | Apache-2.0 | |
| Postgres Hybrid Text Searchtimescale/pg-aiguide | 1.9k | — | ~3.1k | Automated safety check: Pass | Apache-2.0 | |
| RAG Implementationwshobson/agents | 40k | 9 repos | ~1.1k | Automated safety check: Pass | MIT | |
| Hunt RAG Vectorelementalsouls/Claude-BugHunter | 4.8k | — | ~2.6k | Automated safety check: Pass | MIT |
Similar skills
Pgvector Semantic Search
timescale/pg-aiguide
A skill your agent uses for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search.
Postgres Hybrid Text Search
timescale/pg-aiguide
A skill your agent uses to implement hybrid search combining BM25 keyword search with semantic vector search using Reciprocal Rank Fusion (RRF).
RAG Implementation
wshobson/agents
Build retrieval-augmented generation systems: pick a vector database and embedding model, choose retrieval and reranking strategies, and start from a LangGraph pipeline.
Hunt RAG Vector
elementalsouls/Claude-BugHunter
Hunt vector-store / embedding-layer weaknesses in RAG pipelines (OWASP LLM08 Vector and Embedding Weaknesses) — persistent corpus poisoning that survives across sessions and users (distinct from…
Vector DB
RightNow-AI/openfang
Vector database expert for embeddings, similarity search, RAG patterns, and indexing strategies
More from borghei/Claude-Skills
All 364 skills in this repoAgents In The Team
borghei/Claude-Skills
Run delivery when AI coding and ops agents take tickets. An agent skill from borghei/Claude-Skills.
AI Content Disclosure
borghei/Claude-Skills
Check AI-generated marketing content and reviews for required disclosures under the EU AI Act, FTC rules and platform AI-label policies.
AI Prototyping
borghei/Claude-Skills
Idea to AI-generated prototype to customer validation to engineering handoff.
Analytics Engineer
borghei/Claude-Skills
Analytics engineering across data modeling, dbt, transformation, and semantic layers.
Ansoff Matrix
borghei/Claude-Skills
Ansoff Matrix — 4-quadrant framework for growth options: market penetration, market/product development, and diversification.
Brainstorm Okrs
borghei/Claude-Skills
OKR brainstorming and validation using the Radical Focus framework — outcome objectives, measurable key results, counter-metrics.
Related
Categories
Questions about RAG Architect
What does RAG Architect do?
Design RAG pipelines: chunking, retrieval evaluation, and architecture. RAG Architect is an agent skill from borghei/Claude-Skills. Design RAG pipelines: chunking, retrieval evaluation, and architecture.
When should I use RAG Architect?
RAG Architect fits situations like: building a RAG system; selecting a chunking strategy; choosing a vector database; optimizing retrieval quality.
How do I install RAG Architect in Claude Code?
Run `npx skills add borghei/Claude-Skills --skill rag-architect -a claude-code`. Or copy the skill folder (engineering/rag-architect in borghei/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 borghei/Claude-Skills --skill rag-architect -a codex`. Or copy the skill folder (engineering/rag-architect in borghei/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 borghei/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 (python). 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 (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does RAG Architect use?
About 1.8k tokens (SKILL.md is roughly 7.1k 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 13k 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), Pgvector Semantic Search (timescale/pg-aiguide, 1.9k stars), Postgres Hybrid Text 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?
borghei (a GitHub user) maintains it in borghei/Claude-Skills, which has 874 GitHub stars. The repository holds 364 skills in this directory. The repository was last updated on October 7, 2026.
Source: borghei/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.