academic-benchmark-researcher
When the user requests information about academic benchmarks, datasets, or research papers, particularly in machine learning, deep learning, or logical reasoning domains. This skill enables systematic research of academic benchmarks by searching web sources, downloading and analyzing arXiv papers, extracting key metadata (number of tasks, training availability, difficulty levels), and compiling comparative summaries. It triggers on requests involving dataset comparisons, benchmark analysis, or academic paper research for table creation.
npx skills add majiayu000/claude-skill-registry --skill academic-benchmark-researcher --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.
# Instructions ## Primary Objective Systematically research academic benchmarks, datasets, or research papers to extract and compile comparative information (e.g., into a summary table). The core workflow involves: 1) Identifying relevant sources, 2) Extracting key metadata, 3) Synthesizing findings into a structured output (like a LaTeX table). ## Core Workflow 1. **Clarify & Parse Request:** Identify the specific benchmarks/datasets/papers mentioned by the user. Note any required output format (e.g., LaTeX table with specific columns) and constraints (e.g., "no commented lines"). 2. **Initial Information Gathering:** For each identified entity (dataset/paper): * Use `local-web_search` to find general information, official pages (GitHub, project sites), and relevant arXiv IDs. * For arXiv papers, use `arxiv_local-download_paper` or `fetch-fetch_markdown` to obtain the paper content. * Search for specific attributes requested by the user (e.g., "number of tasks," "training set," "difficulty levels"). 3. **Deep Dive & Verification:** Read paper abstracts, introductions, and methodology sections (using `arxiv_local-read_paper` or parsed markdown) to confirm key details. Cross-referen
- Primary Objective
- Core Workflow
- Key Metadata to Extract
- Tool Usage Guidelines
- Output Standards
- Common Pitfalls & Resolutions
What does the academic-benchmark-researcher skill do?
When the user requests information about academic benchmarks, datasets, or research papers, particularly in machine learning, deep learning, or logical reasoning domains. This skill enables systematic research of academic benchmarks by searching web sources, downloading and analyzing arXiv papers, extracting key metadata (number of tasks, training availability, difficulty levels), and compiling comparative summaries. It triggers on requests involving dataset comparisons, benchmark analysis, or academic paper research for table creation.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill academic-benchmark-researcher --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.