Top ClawHub Skills Every AI Researcher Needs

Why ClawHub Skills Matter for AI Research: clawhub skills researchers

Papers pour in from arXiv, PubMed, and journals. Searching and summarizing them by hand takes hours each week. ClawHub skills link these sources to OpenClaw agents via MCP. You can query in plain language, like "Find recent papers on transformer efficiency."

Researchers read about 250 papers a year. Tools for discovery and analysis give more time for experiments. These skills manage storage and RAG, making papers into searchable knowledge bases.

Helpful references: Fast.io Workspaces, Fast.io Collaboration, and Fast.io AI.

Practical execution note for top clawhub skills for ai researchers: define a baseline process, assign ownership, and document fallback behavior when dependencies fail. Run a pilot with a small team, collect concrete metrics, and compare throughput, error rate, and review time before broad rollout. After rollout, keep a living checklist so future contributors can repeat the workflow without re-learning critical constraints.

Top ClawHub Skills Comparison

Practical execution note for top clawhub skills for ai researchers: define a baseline process, assign ownership, and document fallback behavior when dependencies fail. Run a pilot with a small team, collect concrete metrics, and compare throughput, error rate, and review time before broad rollout. After rollout, keep a living checklist so future contributors can repeat the workflow without re-learning critical constraints.

How We Evaluated These Skills

We checked skills for research workflow fit: paper discovery via arXiv/PubMed/Scholar APIs, analysis like summarization and extraction, storage for RAG, MCP compatibility, setup ease, and community activity such as stars and updates. Only active skills with MCP support qualified. We focused on free tiers for solo researchers.

Practical execution note for top clawhub skills for ai researchers: define a baseline process, assign ownership, and document fallback behavior when dependencies fail. Run a pilot with a small team, collect concrete metrics, and compare throughput, error rate, and review time before broad rollout. After rollout, keep a living checklist so future contributors can repeat the workflow without re-learning critical constraints.

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1. arxiv-search by OpenAI

Searches and downloads arXiv papers by title, author, or topic.

Strengths:

• Real-time arXiv API access
• PDF download and metadata
• Keyword alerts

Limitations:

• arXiv only, no journals
• No built-in summarization

Good for starting literature searches. Free. Install: clawhub install openai/arxiv-search

Add one practical example, one implementation constraint, and one measurable outcome so the section is concrete and useful for execution.

Teams should validate this approach in a small test path first, then standardize it across environments once metrics and outcomes are stable.

Document decisions, ownership, and rollback steps so implementation remains repeatable as the workflow scales.

2. pubmed-tools by NIH

Queries PubMed for biomedical papers with filters.

Strengths:

• Advanced PubMed filters
• MeSH terms support
• Export to RIS/BibTeX

Limitations:

• Biomedical focus only
• Slower for large queries

Good for medical AI work. Free. Install: clawhub install nih/pubmed-tools

Add one practical example, one implementation constraint, and one measurable outcome so the section is concrete and useful for execution.

Teams should validate this approach in a small test path first, then standardize it across environments once metrics and outcomes are stable.

Document decisions, ownership, and rollback steps so implementation remains repeatable as the workflow scales.

Teams should validate this approach in a small test path first, then standardize it across environments once metrics and outcomes are stable.

3. semantic-scholar by AllenAI

Access to Semantic Scholar's citation network.

Strengths:

• Citation graphs and TL;DRs
• Paper recommendations
• Influence scores

Limitations:

• Smaller coverage than Google Scholar
• Rate limits on free tier

Good for spotting key papers. Free. Install: clawhub install allenai/semantic-scholar

Add one practical example, one implementation constraint, and one measurable outcome so the section is concrete and useful for execution.

Teams should validate this approach in a small test path first, then standardize it across environments once metrics and outcomes are stable.

Document decisions, ownership, and rollback steps so implementation remains repeatable as the workflow scales.

Teams should validate this approach in a small test path first, then standardize it across environments once metrics and outcomes are stable.

4. fast-io by dbalve

File storage with built-in RAG for papers and datasets.

Strengths:

• 50GB free storage, 1GB files
• Auto-indexing and semantic search
• Share workspaces with collaborators

Limitations:

• 5000 credits/month free limit
• Setup requires agent account

Good for storing and querying research data. Free agent tier. Install: clawhub install dbalve/fast-io

Add one practical example, one implementation constraint, and one measurable outcome so the section is concrete and useful for execution.

Teams should validate this approach in a small test path first, then standardize it across environments once metrics and outcomes are stable.

Document decisions, ownership, and rollback steps so implementation remains repeatable as the workflow scales.

5. paper-qa by paper-qa

RAG-based Q&A over paper collections.

Strengths:

• Multi-paper question answering
• Citation-backed responses
• Local processing option

Limitations:

• Requires vector DB setup
• Compute-heavy for large sets

Good for in-depth paper analysis. Free. Install: clawhub install paper-qa/paper-qa

Add one practical example, one implementation constraint, and one measurable outcome so the section is concrete and useful for execution.

Teams should validate this approach in a small test path first, then standardize it across environments once metrics and outcomes are stable.

Document decisions, ownership, and rollback steps so implementation remains repeatable as the workflow scales.

Teams should validate this approach in a small test path first, then standardize it across environments once metrics and outcomes are stable.

6. citation-finder by citation-finder

Extracts and formats citations from papers.

Strengths:

• Supports PDF/DOCX input
• Multiple formats (APA, MLA)
• Batch processing

Limitations:

• No search/discovery
• Accuracy varies by paper quality

Good for bibliography work. Free. Install: clawhub install citation-finder/cite

Add one practical example, one implementation constraint, and one measurable outcome so the section is concrete and useful for execution.

Teams should validate this approach in a small test path first, then standardize it across environments once metrics and outcomes are stable.

Document decisions, ownership, and rollback steps so implementation remains repeatable as the workflow scales.

Teams should validate this approach in a small test path first, then standardize it across environments once metrics and outcomes are stable.

7. pinecone-vdb by Pinecone

Vector database for embedding papers.

Strengths:

• Scalable hybrid search
• Serverless pods
• Metadata filtering

Limitations:

• Paid beyond free tier
• Embeddings extra cost

Good for custom RAG setups. Usage-based. Install: clawhub install pinecone/pinecone-vdb

Add one practical example, one implementation constraint, and one measurable outcome so the section is concrete and useful for execution.

Teams should validate this approach in a small test path first, then standardize it across environments once metrics and outcomes are stable.

Document decisions, ownership, and rollback steps so implementation remains repeatable as the workflow scales.

Teams should validate this approach in a small test path first, then standardize it across environments once metrics and outcomes are stable.

8. lit-review-auto by lit-review

Automates literature review synthesis.

Strengths:

• Theme extraction
• Gap analysis
• Report generation

Limitations:

• Early stage, fewer updates
• Relies on upstream skills

Good for drafting review papers. Free. Install: clawhub install lit-review/lit-review

Add one practical example, one implementation constraint, and one measurable outcome so the section is concrete and useful for execution.

Teams should validate this approach in a small test path first, then standardize it across environments once metrics and outcomes are stable.

Document decisions, ownership, and rollback steps so implementation remains repeatable as the workflow scales.

Teams should validate this approach in a small test path first, then standardize it across environments once metrics and outcomes are stable.

Which ClawHub Skill Should You Choose?

Begin with arxiv-search and pubmed-tools for discovery. Add fast-io for storage and RAG. Layer pinecone-vdb or paper-qa for special cases. Test in a dev OpenClaw setup. Stack them for complete flows: search, store, analyze, cite.

Add one practical example, one implementation constraint, and one measurable outcome so the section is concrete and useful for execution.

Teams should validate this approach in a small test path first, then standardize it across environments once metrics and outcomes are stable.

Document decisions, ownership, and rollback steps so implementation remains repeatable as the workflow scales.

Teams should validate this approach in a small test path first, then standardize it across environments once metrics and outcomes are stable.