mteb-retrieve
Guidance for text embedding retrieval tasks using sentence transformers or similar embedding models. This skill should be used when the task involves loading documents, encoding text with embedding models, computing similarity scores (cosine similarity), and retrieving/ranking documents based on semantic similarity to a query. Applies to MTEB benchmark tasks, document retrieval, semantic search, and text similarity ranking.
npx skills add majiayu000/claude-skill-registry --skill mteb-retrieve --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# MTEB Retrieve ## Overview This skill provides guidance for text embedding retrieval tasks that involve encoding documents and queries using embedding models, computing similarity scores, and retrieving or ranking documents based on semantic similarity. ## Workflow ### Step 1: Inspect and Parse Data Before writing any code, carefully inspect the raw data format: 1. **Read the data file** and examine actual line contents 2. **Identify formatting artifacts** such as: - Line number prefixes (e.g., `1→`, `2→`, `1.`, `1:`) - Whitespace or tab characters - Quote characters or escape sequences - Header rows or metadata 3. **Design parsing logic** that strips all non-content artifacts **Common data format issues:** - Files with line numbers prepended (e.g., `1→Document text here`) - CSV/TSV files with headers - JSON files with nested structures - Files with trailing whitespace or newlines **Verification:** Print 2-3 parsed documents to confirm they contain only the actual text content. ### Step 2: Load the Embedding Model 1. **Identify the model** specified in the task (e.g., `sentence-transformers/all-MiniLM-L6-v2`) 2. **Load the model** using the appropriate library (typically `sentence
- Overview
- Workflow
- Step 1: Inspect and Parse Data
- Step 2: Load the Embedding Model
- Step 3: Encode Documents and Query
- Step 4: Compute Similarities
- Step 5: Rank and Retrieve
- Step 6: Validate Results
- Common Pitfalls
- 1. Data Format Parsing Errors
- 2. Skipping Validation
- 3. Off-by-One Errors in Ranking
- 4. Ignoring Semantic Reasonableness
- Verification Checklist
What does the mteb-retrieve skill do?
Guidance for text embedding retrieval tasks using sentence transformers or similar embedding models. This skill should be used when the task involves loading documents, encoding text with embedding models, computing similarity scores (cosine similarity), and retrieving/ranking documents based on semantic similarity to a query. Applies to MTEB benchmark tasks, document retrieval, semantic search, and text similarity ranking.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill mteb-retrieve --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.