Retail Product Search Agent
google/adk-recipes
Builds a retail product search agent on Google Cloud, from catalog ingestion into BigQuery and Vector Search to ADK scaffolding, evaluation and Cloud Run deployment.
A skill your agent uses for Feast RAG, vector search, vector-indexed fields, vector online stores, document embedding/chunking, retrieveonlinedocuments APIs, DocEmbedder, FeastVectorStore, and…
Install RAG And Vector Search
$ npx skills add VectorSpaceLab/AREX-Skill --skill rag-and-vector-search -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill rag-and-vector-search --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/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/repositories/repo-skills/feast/sub-skills/rag-and-vector-search .claude/skills/rag-and-vector-search && 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-and-vector-search" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/feast/sub-skills/rag-and-vector-search into .claude/skills/rag-and-vector-search/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rag-and-vector-search", 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/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/feast/sub-skills/rag-and-vector-searchType 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 VectorSpaceLab/AREX-Skill --skill rag-and-vector-search -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill rag-and-vector-search --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/repositories/repo-skills/feast/sub-skills/rag-and-vector-search .agents/skills/rag-and-vector-search && 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-and-vector-search" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/feast/sub-skills/rag-and-vector-search into .agents/skills/rag-and-vector-search/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rag-and-vector-search", 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 VectorSpaceLab/AREX-Skill --skill rag-and-vector-search -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill rag-and-vector-search --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/repositories/repo-skills/feast/sub-skills/rag-and-vector-search .cursor/skills/rag-and-vector-search && 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-and-vector-search" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/feast/sub-skills/rag-and-vector-search into .cursor/skills/rag-and-vector-search/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rag-and-vector-search", 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/VectorSpaceLab/AREX-Skill.git --path skills/repositories/repo-skills/feast/sub-skills/rag-and-vector-search--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 VectorSpaceLab/AREX-Skill --skill rag-and-vector-search -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill rag-and-vector-search --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/repositories/repo-skills/feast/sub-skills/rag-and-vector-search .gemini/skills/rag-and-vector-search && 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-and-vector-search" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/feast/sub-skills/rag-and-vector-search into .gemini/skills/rag-and-vector-search/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rag-and-vector-search", 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 VectorSpaceLab/AREX-Skill rag-and-vector-searchInstalls 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 VectorSpaceLab/AREX-Skill --skill rag-and-vector-search -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/repositories/repo-skills/feast/sub-skills/rag-and-vector-search .github/skills/rag-and-vector-search && 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-and-vector-search" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/feast/sub-skills/rag-and-vector-search into .github/skills/rag-and-vector-search/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rag-and-vector-search", 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 VectorSpaceLab/AREX-Skill --skill rag-and-vector-search -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill rag-and-vector-search --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/repositories/repo-skills/feast/sub-skills/rag-and-vector-search .opencode/skills/rag-and-vector-search && 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-and-vector-search" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/feast/sub-skills/rag-and-vector-search into .opencode/skills/rag-and-vector-search/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rag-and-vector-search", 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-and-vector-search- GitHub stars
- 331
- Token cost
- ~622 tokens
- SKILL.md length
- 207 words
- Files
- 5 (incl. scripts, references)
- Skills in repo
- 157
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
A skill your agent uses for Feast RAG, vector search, vector-indexed fields, vector online stores, document embedding/chunking, retrieveonlinedocuments APIs, DocEmbedder, FeastVectorStore, and…
- Works in 4 steps: Read references/vector-reference.md to… → Read references/rag-workflows.md for… → Use references/troubleshooting.md for… → …
- Vector-indexed fields
- SKILL.md covers Route Here For, Route Elsewhere, Start Here and Fast Safety Checks
- Runs Python scripts from its folder; calls python
What it does
RAG And Vector Search is an agent skill from VectorSpaceLab/AREX-Skill. Use for Feast RAG, vector search, vector-indexed fields, vector online stores, document embedding/chunking, retrieveonlinedocuments APIs, DocEmbedder, FeastVectorStore, and FeastRAGRetriever workflows.
Its SKILL.md is about 620 tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including scripts and reference files (for example `references/rag-workflows.md`, `references/troubleshooting.md` and `references/vector-reference.md`).
It sits in AI & LLM Engineering, covering Vector databases, Retrieval-augmented generation and E-commerce operations. The repository describes itself as: A Skill Library for Automated Machine Learning. The licence is Apache-2.0.
When your agent uses it
- Vector-indexed fields
- Vector online stores
- Document embedding/chunking
- Retrieveonlinedocuments APIs
Example prompts
- “/rag-and-vector-search”
Requirements
- Python 3
Workflow steps
4 steps, taken from the first numbered list in SKILL.md.
- Read references/vector-reference.md to choose schema, store config, and retrieval API.
- Read references/rag-workflows.md for document ingestion, DocEmbedder, FeastVectorStore, and FeastRAGRetriever patterns.
- Use references/troubleshooting.md for install, optional extra, vector dimension, service, and API failures.
- Run scripts/vector_config_lint.py --help before asking users to connect to a vector database.
What it can do on your machine
Read from SKILL.md and the folder at commit ac3fe1a. 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 1 file in scripts/ (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 And Vector Search loads about 622 tokens when it runs, and up to ~6.3k if it reads all its reference files. Until then it costs about 56 tokens; SKILL.md has 207 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); the scripts in this folder are not scanned.
SKILL.md
The full file from VectorSpaceLab/AREX-Skill at commit ac3fe1a, republished under its Apache-2.0 licence (© VectorSpaceLab). 207 words, ~622 tokens.
Download SKILL.mdSave it as
.claude/skills/rag-and-vector-search/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- rag-and-vector-search
- description
- Use for Feast RAG, vector search, vector-indexed fields, vector online stores, document embedding/chunking, retrieve_online_documents APIs, DocEmbedder, FeastVectorStore, and FeastRAGRetriever workflows.
- disable-model-invocation
- true
- metadata.disco-role
- operating
- license
- Apache 2.0
Feast RAG And Vector Search
Use this sub-skill when the user asks to build, configure, debug, or validate Feast-powered vector retrieval or RAG workflows.
Route Here For
- Vector schema fields using
Field(..., vector_index=True, vector_length=..., vector_search_metric=...). - Vector online store choices and
feature_store.yamlsettings for Milvus, SQLite vector mode, Postgres/pgvector, Elasticsearch, Qdrant, MongoDB, or Faiss. - SDK retrieval with
FeatureStore.retrieve_online_documents_v2(...)or legacyretrieve_online_documents(...). - RAG helper APIs:
DocEmbedder,TextChunker,MultiModalEmbedder,FeastVectorStore,FeastIndex, andFeastRAGRetriever. - Document ingestion: chunk documents, create embeddings, write vectors to the online store, retrieve top-k context, and format context for an LLM.
Route Elsewhere
- Generic Feast object modeling that is not vector-specific:
../feature-definitions/SKILL.md. - Non-vector online/historical retrieval, materialization, or push ingestion:
../retrieval-and-materialization/SKILL.md. - Feature server, MCP, auth, TLS, and remote endpoint setup:
../servers-and-remote/SKILL.md. - Optional dependency selection across non-vector stores or custom store implementation:
../integrations-and-extensibility/SKILL.md.
Start Here
- Read
references/vector-reference.mdto choose schema, store config, and retrieval API. - Read
references/rag-workflows.mdfor document ingestion, DocEmbedder, FeastVectorStore, and FeastRAGRetriever patterns. - Use
references/troubleshooting.mdfor install, optional extra, vector dimension, service, and API failures. - Run
scripts/vector_config_lint.py --helpbefore asking users to connect to a vector database.
Fast Safety Checks
python scripts/vector_config_lint.py path/to/feature_repo.py
python scripts/vector_config_lint.py feature_store.yaml --config-only
python scripts/vector_config_lint.py vector_snippet.jsonExpected success output includes OK: lines and a final Summary:. Any ERROR: should be fixed before feast apply, materialization, or remote service debugging.
© VectorSpaceLab, 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
SKILL.md and 4 other files (scripts, references) in skills/repositories/repo-skills/feast/sub-skills/rag-and-vector-search of VectorSpaceLab/AREX-Skill.
- SKILL.md
- references/rag-workflows.md
- references/troubleshooting.md
- references/vector-reference.md
- scripts/vector_config_lint.py
Open the folder on GitHubat commit ac3fe1a
Compare with similar skills
RAG And Vector Search 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 And Vector Search this skillVectorSpaceLab/AREX-Skill | 331 | — | ~622 | Automated safety check: Pass | Apache-2.0 | |
| Retail Product Search Agentgoogle/adk-recipes | 10k | — | ~3k | Automated safety check: Pass | Apache-2.0 | |
| 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 |
Similar skills
- Official
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.
Ms Agent Framework RAG
shuyu-labs/WebCode
Comprehensive guide for building Agentic RAG systems using Microsoft Agent Framework in C.
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.
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.
RAG Engineer
davila7/claude-code-templates
Expert in building Retrieval-Augmented Generation systems. An agent skill from davila7/claude-code-templates.
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Related
Categories
Questions about RAG And Vector Search
What does RAG And Vector Search do?
A skill your agent uses for Feast RAG, vector search, vector-indexed fields, vector online stores, document embedding/chunking, retrieveonlinedocuments APIs, DocEmbedder, FeastVectorStore, and…. RAG And Vector Search is an agent skill from VectorSpaceLab/AREX-Skill. Use for Feast RAG, vector search, vector-indexed fields, vector online stores, document embedding/chunking, retrieveonlinedocuments APIs, DocEmbedder, FeastVectorStore, and FeastRAGRetriever workflows.
When should I use RAG And Vector Search?
RAG And Vector Search fits situations like: vector-indexed fields; vector online stores; document embedding/chunking; retrieveonlinedocuments APIs.
How do I install RAG And Vector Search in Claude Code?
Run `npx skills add VectorSpaceLab/AREX-Skill --skill rag-and-vector-search -a claude-code`. Or copy the skill folder (skills/repositories/repo-skills/feast/sub-skills/rag-and-vector-search in VectorSpaceLab/AREX-Skill) into .claude/skills/rag-and-vector-search in your project. Claude Code loads it when a task matches its description.
How do I install RAG And Vector Search in Codex?
Run `npx skills add VectorSpaceLab/AREX-Skill --skill rag-and-vector-search -a codex`. Or copy the skill folder (skills/repositories/repo-skills/feast/sub-skills/rag-and-vector-search in VectorSpaceLab/AREX-Skill) into .agents/skills/rag-and-vector-search in your project. Codex loads it when a task matches its description.
Can I use RAG And Vector Search 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 VectorSpaceLab/AREX-Skill --skill rag-and-vector-search -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-and-vector-search, .gemini/skills/rag-and-vector-search, .github/skills/rag-and-vector-search and .opencode/skills/rag-and-vector-search in your project.
What does RAG And Vector Search need to run?
Going by SKILL.md and its folder, RAG And Vector Search needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.
Does RAG And Vector Search 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 And Vector Search 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does RAG And Vector Search use?
RAG And Vector Search is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does RAG And Vector Search use?
About 622 tokens (SKILL.md is roughly 2.5k 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 5.7k tokens, read only when the agent opens those files.
What are the alternatives to RAG And Vector Search?
Skills that share tags, products or a category with RAG And Vector Search: Retail Product Search Agent (google/adk-recipes, 10k stars), Chroma Vector Database (Orchestra-Research/AI-Research-SKILLs, 13k stars), Ms Agent Framework RAG (shuyu-labs/WebCode, 278 stars) and Pgvector Semantic Search (timescale/pg-aiguide, 1.9k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains RAG And Vector Search?
VectorSpaceLab (a GitHub organization) maintains it in VectorSpaceLab/AREX-Skill, which has 331 GitHub stars. The repository holds 157 skills in this directory. The repository was last updated on September 3, 2026.
Source: VectorSpaceLab/AREX-Skill on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.