Agent skill

Dspy Embedding Retrieval

A skill your agent uses for DSPy retrieval with dspy.Embedder, dspy.Embeddings, FAISS indexes, semantic search, and local or hosted embedding models.

MITAuto-check passedAI & LLM Engineering

Install Dspy Embedding Retrieval

skills CLI

$ npx skills add OmidZamani/dspy-skills --skill dspy-embedding-retrieval -a claude-code

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

GitHub CLI

$ gh skill install OmidZamani/dspy-skills dspy-embedding-retrieval --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/OmidZamani/dspy-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/dspy-embedding-retrieval .claude/skills/dspy-embedding-retrieval && 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/

Paste into Claude Code

Install the "dspy-embedding-retrieval" agent skill from https://github.com/OmidZamani/dspy-skills/tree/master/skills/dspy-embedding-retrieval into .claude/skills/dspy-embedding-retrieval/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dspy-embedding-retrieval", 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

$skill-installer install https://github.com/OmidZamani/dspy-skills/tree/master/skills/dspy-embedding-retrieval

Type 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.

skills CLI

$ npx skills add OmidZamani/dspy-skills --skill dspy-embedding-retrieval -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install OmidZamani/dspy-skills dspy-embedding-retrieval --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/OmidZamani/dspy-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/dspy-embedding-retrieval .agents/skills/dspy-embedding-retrieval && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "dspy-embedding-retrieval" agent skill from https://github.com/OmidZamani/dspy-skills/tree/master/skills/dspy-embedding-retrieval into .agents/skills/dspy-embedding-retrieval/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dspy-embedding-retrieval", 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.

skills CLI

$ npx skills add OmidZamani/dspy-skills --skill dspy-embedding-retrieval -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install OmidZamani/dspy-skills dspy-embedding-retrieval --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/OmidZamani/dspy-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/dspy-embedding-retrieval .cursor/skills/dspy-embedding-retrieval && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "dspy-embedding-retrieval" agent skill from https://github.com/OmidZamani/dspy-skills/tree/master/skills/dspy-embedding-retrieval into .cursor/skills/dspy-embedding-retrieval/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dspy-embedding-retrieval", 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

$ gemini skills install https://github.com/OmidZamani/dspy-skills.git --path skills/dspy-embedding-retrieval

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add OmidZamani/dspy-skills --skill dspy-embedding-retrieval -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install OmidZamani/dspy-skills dspy-embedding-retrieval --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/OmidZamani/dspy-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/dspy-embedding-retrieval .gemini/skills/dspy-embedding-retrieval && rm -rf skills-src

Use ~/.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/

Paste into Gemini CLI

Install the "dspy-embedding-retrieval" agent skill from https://github.com/OmidZamani/dspy-skills/tree/master/skills/dspy-embedding-retrieval into .gemini/skills/dspy-embedding-retrieval/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dspy-embedding-retrieval", 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.

GitHub CLI

$ gh skill install OmidZamani/dspy-skills dspy-embedding-retrieval

Installs 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).

skills CLI

$ npx skills add OmidZamani/dspy-skills --skill dspy-embedding-retrieval -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/OmidZamani/dspy-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/dspy-embedding-retrieval .github/skills/dspy-embedding-retrieval && rm -rf skills-src

Use ~/.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/

Paste into GitHub Copilot

Install the "dspy-embedding-retrieval" agent skill from https://github.com/OmidZamani/dspy-skills/tree/master/skills/dspy-embedding-retrieval into .github/skills/dspy-embedding-retrieval/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dspy-embedding-retrieval", 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.

skills CLI

$ npx skills add OmidZamani/dspy-skills --skill dspy-embedding-retrieval -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install OmidZamani/dspy-skills dspy-embedding-retrieval --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/OmidZamani/dspy-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/dspy-embedding-retrieval .opencode/skills/dspy-embedding-retrieval && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "dspy-embedding-retrieval" agent skill from https://github.com/OmidZamani/dspy-skills/tree/master/skills/dspy-embedding-retrieval into .opencode/skills/dspy-embedding-retrieval/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dspy-embedding-retrieval", 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: dspy-embedding-retrieval
GitHub stars: 123
Token cost: ~720 tokens
SKILL.md length: 139 words
Files: 2
Skills in repo: 17
Repo updated: Jun 23, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

A skill your agent uses for DSPy retrieval with dspy.Embedder, dspy.Embeddings, FAISS indexes, semantic search, and local or hosted embedding models.

Works in 5 steps: Evaluate retrieval quality separately… → Keep corpus chunking deterministic and… → Persist expensive indexes. → …
DSPy retrieval with dspy.Embedder
SKILL.md covers Goal, Basic Hosted Embedder, Use in RAG and Custom Local Embeddings, plus 4 more sections
Runs Python scripts from its folder; calls pip

What it does

Dspy Embedding Retrieval is an agent skill from OmidZamani/dspy-skills. Use for DSPy retrieval with dspy.Embedder, dspy.Embeddings, FAISS indexes, semantic search, and local or hosted embedding models.

Its SKILL.md is about 720 tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `example.py`).

It sits in AI & LLM Engineering, covering Embeddings and Vector databases. The repository describes itself as: Collection of Claude Skills for DSPy framework - program language models, optimize prompts, and build RAG pipelines systematically. The licence is MIT.

When your agent uses it

DSPy retrieval with dspy.Embedder
Dspy.Embeddings
Semantic search
Hosted embedding models

Example prompts

“/dspy-embedding-retrieval”

Requirements

Python 3
Pre-approved tools (allowed-tools): Read, Write, Glob, Grep

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

Evaluate retrieval quality separately from answer quality.
Keep corpus chunking deterministic and versioned.
Persist expensive indexes.
Use EmbeddingsWithScores when debugging relevance.
Measure memory and latency before enabling FAISS for large corpora.

What it can do on your machine

Read from SKILL.md and the folder at commit f5db3b7. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves these tools, so the agent can use them without asking each time:
- Read
- Write
- Glob
- Grep
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:
- pip
From the folder's file list and the shell code blocks in SKILL.md.
Network
Links to these hosts (documentation or services it may open):
- dspy.ai
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

Dspy Embedding Retrieval loads about 720 tokens when it runs. Until then it costs about 39 tokens; SKILL.md has 139 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~39
When it runs · the whole SKILL.md, loaded when a task matches: ~720

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 OmidZamani/dspy-skills at commit f5db3b7, republished under its MIT licence (© OmidZamani). 139 words, ~720 tokens.

Download SKILL.mdSave it as .claude/skills/dspy-embedding-retrieval/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.

name: dspy-embedding-retrieval
description: Use for DSPy retrieval with dspy.Embedder, dspy.Embeddings, FAISS indexes, semantic search, and local or hosted embedding models.
allowed-tools: Read, Write, Glob, Grep
version: 1.0.0
dspy-compatibility: 3.2.1
tags: retrieval
requires-extras: faiss-cpu

DSPy Embedding Retrieval

Goal

Build semantic retrieval over an application-owned text corpus with dspy.Embedder and dspy.Embeddings.

Basic Hosted Embedder

python

import dspy

corpus = [
    "DSPy programs are composed from modules.",
    "MIPROv2 optimizes instructions and demonstrations.",
    "RLM explores large contexts with a sandboxed REPL.",
]

embedder = dspy.Embedder("openai/text-embedding-3-small")
search = dspy.Embeddings(corpus=corpus, embedder=embedder, k=2)

result = search("Which optimizer tunes prompts?")
print(result.passages)
print(result.indices)

Use in RAG

python

class LocalRAG(dspy.Module):
    def __init__(self, retriever):
        super().__init__()
        self.retriever = retriever
        self.answer = dspy.ChainOfThought("context: list[str], question -> answer")

    def forward(self, question: str):
        context = self.retriever(question).passages
        return self.answer(context=context, question=question)

Custom Local Embeddings

Wrap any callable that accepts list[str] and returns a 2D numeric array:

python

from sentence_transformers import SentenceTransformer
import dspy

model = SentenceTransformer("sentence-transformers/static-retrieval-mrl-en-v1")
embedder = dspy.Embedder(model.encode)
search = dspy.Embeddings(corpus=corpus, embedder=embedder, k=5)

Scores, FAISS, and Persistence

Use dspy.EmbeddingsWithScores when downstream logic needs similarity thresholds or reranking.

For corpora at or above the brute_force_threshold default of 20_000, DSPy builds a FAISS index. Install FAISS first:

bash

pip install faiss-cpu

Persist the index when embedding the corpus is expensive:

python

search.save("./retrieval-index")
loaded = dspy.Embeddings.from_saved("./retrieval-index", embedder=embedder)

Build a complete pipeline: dspy-rag-pipeline
Design typed context fields: dspy-signature-designer
Harden caches: dspy-production-deployment

Best Practices

Evaluate retrieval quality separately from answer quality.
Keep corpus chunking deterministic and versioned.
Persist expensive indexes.
Use EmbeddingsWithScores when debugging relevance.
Measure memory and latency before enabling FAISS for large corpora.

Official Documentation

Embedder API: https://dspy.ai/api/models/Embedder/
Embeddings API: https://dspy.ai/api/tools/Embeddings/

Files

SKILL.md and 1 other file in skills/dspy-embedding-retrieval of OmidZamani/dspy-skills.

SKILL.md
example.py

Open the folder on GitHubat commit f5db3b7

Compare with similar skills

Dspy Embedding Retrieval 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.

Dspy Embedding Retrieval compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Dspy Embedding Retrieval this skillOmidZamani/dspy-skills	123	—	~720	Automated safety check: Pass	MIT	3 mo ago
Chroma Vector DatabaseOrchestra-Research/AI-Research-SKILLs	13k	7 repos	~2.3k	Automated safety check: Pass	MIT	3 mo ago
Codebase Managementgiancarloerra/SocratiCode	3.3k	1 repos	~1.8k	Automated safety check: Pass	AGPL-3.0	2 days ago
Ms Agent Framework RAGshuyu-labs/WebCode	278	—	~1.1k	Automated safety check: Pass	Custom licence	3 mo ago
Pgvector Semantic Searchtimescale/pg-aiguide	1.9k	—	~3.8k	Automated safety check: Pass	Apache-2.0	2 days ago
RAG Implementationwshobson/agents	40k	9 repos	~1.1k	Automated safety check: Pass	MIT	5 days ago

Similar skills

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.
13k GitHub starsUsed in 7 repos~2.3k tokens
AI & LLM EngineeringAuto-check passed
Codebase Management
giancarloerra/SocratiCode
Set up, index, and manage SocratiCode codebase indexing. An agent skill from giancarloerra/SocratiCode.
3.3k GitHub starsUsed in 1 repo~1.8k tokens
AI & LLM EngineeringAuto-check passed
Ms Agent Framework RAG
shuyu-labs/WebCode
Comprehensive guide for building Agentic RAG systems using Microsoft Agent Framework in C.
278 GitHub stars~1.1k tokensUpdated 3 mo ago
AI & LLM EngineeringAuto-check passed
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.
1.9k GitHub stars~3.8k tokensUpdated 2 days ago
AI & LLM EngineeringAuto-check passed
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.
40k GitHub starsUsed in 9 repos~1.1k tokens
AI & LLM EngineeringAuto-check passed
RAG Engineer
davila7/claude-code-templates
Expert in building Retrieval-Augmented Generation systems. An agent skill from davila7/claude-code-templates.
33k GitHub starsUsed in 5 repos~729 tokens
AI & LLM EngineeringAuto-check passed

Questions about Dspy Embedding Retrieval

What does Dspy Embedding Retrieval do?

A skill your agent uses for DSPy retrieval with dspy.Embedder, dspy.Embeddings, FAISS indexes, semantic search, and local or hosted embedding models. Dspy Embedding Retrieval is an agent skill from OmidZamani/dspy-skills.Embeddings, FAISS indexes, semantic search, and local or hosted embedding models.

When should I use Dspy Embedding Retrieval?

Dspy Embedding Retrieval fits situations like: DSPy retrieval with dspy.Embedder; dspy.Embeddings; semantic search; hosted embedding models.

How do I install Dspy Embedding Retrieval in Claude Code?

Run `npx skills add OmidZamani/dspy-skills --skill dspy-embedding-retrieval -a claude-code`. Or copy the skill folder (skills/dspy-embedding-retrieval in OmidZamani/dspy-skills) into .claude/skills/dspy-embedding-retrieval in your project. Claude Code loads it when a task matches its description.

How do I install Dspy Embedding Retrieval in Codex?

Run `npx skills add OmidZamani/dspy-skills --skill dspy-embedding-retrieval -a codex`. Or copy the skill folder (skills/dspy-embedding-retrieval in OmidZamani/dspy-skills) into .agents/skills/dspy-embedding-retrieval in your project. Codex loads it when a task matches its description.

Can I use Dspy Embedding Retrieval 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 OmidZamani/dspy-skills --skill dspy-embedding-retrieval -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/dspy-embedding-retrieval, .gemini/skills/dspy-embedding-retrieval, .github/skills/dspy-embedding-retrieval and .opencode/skills/dspy-embedding-retrieval in your project.

What does Dspy Embedding Retrieval need to run?

Going by SKILL.md and its folder, Dspy Embedding Retrieval needs Python for the scripts in its folder and the command-line tools its instructions call (pip). Our summary lists: Python 3. Its frontmatter pre-approves these tools: Read, Write, Glob, Grep.

Does Dspy Embedding Retrieval access the network?

SKILL.md names 1 domain. As links in the text: dspy.ai. This is read from the text; nothing was executed.

Is Dspy Embedding Retrieval 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 Dspy Embedding Retrieval use?

Dspy Embedding Retrieval 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 Dspy Embedding Retrieval use?

About 720 tokens (SKILL.md is roughly 2.9k 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 Dspy Embedding Retrieval?

Skills that share tags, products or a category with Dspy Embedding Retrieval: Chroma Vector Database (Orchestra-Research/AI-Research-SKILLs, 13k stars), Codebase Management (giancarloerra/SocratiCode, 3.3k 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 Dspy Embedding Retrieval?

OmidZamani (a GitHub user) maintains it in OmidZamani/dspy-skills, which has 123 GitHub stars. The repository holds 17 skills in this directory. The repository was last updated on June 23, 2026.

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

Install Dspy Embedding Retrieval

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

DSPy Embedding Retrieval

Goal

Basic Hosted Embedder

Use in RAG

Custom Local Embeddings

Scores, FAISS, and Persistence

Related Skills

Best Practices

Official Documentation

Files

Compare with similar skills

Similar skills

More from OmidZamani/dspy-skills

Related

Questions about Dspy Embedding Retrieval