mteb-leaderboard
Guidance for querying ML model leaderboards and benchmarks (MTEB, HuggingFace, embedding benchmarks). This skill applies when tasks involve finding top-performing models on specific benchmarks, comparing model performance across leaderboards, or answering questions about current benchmark standings. Covers strategies for accessing live leaderboard data, handling temporal requirements, and avoiding common pitfalls with outdated sources.
npx skills add majiayu000/claude-skill-registry --skill mteb-leaderboard --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 Leaderboard Query Skill This skill provides guidance for accurately querying machine learning model leaderboards and benchmarks, particularly the Massive Text Embedding Benchmark (MTEB) and related embedding leaderboards. ## When to Use This Skill - Finding top-performing models on specific benchmarks (MTEB, Scandinavian Embedding Benchmark, etc.) - Answering questions about current leaderboard standings - Comparing model performance across different benchmarks - Tasks with specific temporal requirements (e.g., "as of August 2025") ## Core Approach ### Step 1: Identify Authoritative Data Sources Before searching for results, establish which sources contain authoritative, current data: 1. **Primary Sources (prefer these)**: - Official leaderboard websites (e.g., `mteb-leaderboard` on HuggingFace Spaces) - GitHub repositories with raw benchmark data - API endpoints or JSON data files from leaderboard maintainers 2. **Secondary Sources (use with caution)**: - Academic papers (often outdated by publication time) - Blog posts and articles (may reference outdated results) - News articles about benchmark results ### Step 2: Verify Temporal Alignment When a task specifies a time con
- When to Use This Skill
- Core Approach
- Step 1: Identify Authoritative Data Sources
- Step 2: Verify Temporal Alignment
- Step 3: Access Live Leaderboard Data
- Step 4: Validate Model Eligibility
- Verification Strategies
- Cross-Reference Multiple Sources
- Sanity Check Results
- Test Alternative Access Methods
- Common Pitfalls to Avoid
- 1. Relying on Outdated Academic Papers
- 2. Giving Up When Web Scraping Fails
- 3. Making Assumptions About Model Format
What does the mteb-leaderboard skill do?
Guidance for querying ML model leaderboards and benchmarks (MTEB, HuggingFace, embedding benchmarks). This skill applies when tasks involve finding top-performing models on specific benchmarks, comparing model performance across leaderboards, or answering questions about current benchmark standings. Covers strategies for accessing live leaderboard data, handling temporal requirements, and avoiding common pitfalls with outdated sources.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill mteb-leaderboard --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.