Agent skills
Skills you can use with AI coding agents, indexed from public GitHub repositories.
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xurl
A CLI tool for making authenticated requests to the X (Twitter) API. Use this skill when you need to post tweets, reply, quote, search, read posts, manage followers, send DMs, upload media, or interact with any X API v2 endpoint.
beita6969/ScienceClaw 571
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openai-whisper
Local speech-to-text with the Whisper CLI (no API key).
beita6969/ScienceClaw 571
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brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
beita6969/ScienceClaw 571
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math-computation
Mathematical computation including symbolic math, numerical methods, linear algebra, calculus, differential equations, optimization, and mathematical modeling. Uses Python with SymPy, NumPy, SciPy. Use when user asks to solve equations, compute integrals/derivatives, do matrix operations, solve ODEs/PDEs, optimize functions, or build mathematical models. Triggers on "solve equation", "integral", "derivative", "matrix", "eigenvalue", "differential equation", "optimization", "linear algebra", "symbolic math", "proof".
beita6969/ScienceClaw 571
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openalex-search
Open academic metadata via OpenAlex API. Use when: user needs author profiles, institution data, concept mapping, or open citation data. NOT for: full-text search or downloading papers.
beita6969/ScienceClaw 571
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exploratory-data-analysis
Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.
beita6969/ScienceClaw 571
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statsmodels
Statistical models library for Python. Use when you need specific model classes (OLS, GLM, mixed models, ARIMA) with detailed diagnostics, residuals, and inference. Best for econometrics, time series, rigorous inference with coefficient tables. For guided statistical test selection with APA reporting use statistical-analysis.
beita6969/ScienceClaw 571
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quantum-computing
Designs and analyzes quantum computing solutions including quantum circuit construction, algorithm implementation, error correction, and quantum advantage assessment; trigger when users discuss qubits, quantum gates, quantum algorithms, or quantum hardware.
beita6969/ScienceClaw 571
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himalaya
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).
beita6969/ScienceClaw 571
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biorxiv-search
Search bioRxiv biology preprints with natural language queries. Semantic search powered by Valyu.
beita6969/ScienceClaw 571
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science-communication
beita6969/ScienceClaw 571
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biostatistics
Performs biostatistical analyses specialized for clinical and biomedical research including survival analysis, Kaplan-Meier estimation, Cox proportional hazards regression, longitudinal data modeling, and diagnostic test evaluation; trigger when users discuss clinical outcomes, survival curves, or biomedical study statistics.
beita6969/ScienceClaw 571
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scientific-visualization
Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib/seaborn/plotly with publication styles. For quick exploration use seaborn or plotly directly.
beita6969/ScienceClaw 571
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scienceclaw-discovery
Identify research gaps, synthesize cross-disciplinary insights, and generate novel hypotheses. Use when: user asks about unexplored areas, cross-field connections, or new research directions. NOT for: routine literature review or data analysis.
beita6969/ScienceClaw 571
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networkx
Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.
beita6969/ScienceClaw 571
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wikidata-knowledge
Query Wikidata for structured knowledge using SPARQL and entity search. Use when: (1) finding structured facts about entities (people, places, organizations), (2) querying relationships between entities, (3) cross-referencing external identifiers (Wikipedia, VIAF, GND, ORCID), (4) building knowledge graphs from linked data. NOT for: full-text article content (use Wikipedia API), scientific literature (use semantic-scholar), geospatial data (use OpenStreetMap).
beita6969/ScienceClaw 571
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bear-notes
Create, search, and manage Bear notes via grizzly CLI.
beita6969/ScienceClaw 571
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computational-pathology-agent
COPYRIGHT NOTICE
beita6969/ScienceClaw 571
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pubmed-database
Direct REST API access to PubMed. Advanced Boolean/MeSH queries, E-utilities API, batch processing, citation management. For Python workflows, prefer biopython (Bio.Entrez). Use this for direct HTTP/REST work or custom API implementations.
beita6969/ScienceClaw 571
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lit-synthesizer
Search PubMed and bioRxiv, summarise papers with LLM, build citation graphs, and generate literature review sections.
beita6969/ScienceClaw 571
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citation-management
Comprehensive citation management for academic research. Search Google Scholar and PubMed for papers, extract accurate metadata, validate citations, and generate properly formatted BibTeX entries. This skill should be used when you need to find papers, verify citation information, convert DOIs to BibTeX, or ensure reference accuracy in scientific writing.
beita6969/ScienceClaw 571
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test-driven-development
Use when implementing any feature or bugfix, before writing implementation code
beita6969/ScienceClaw 571
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data-extractor
Extract numerical data from scientific figure images using Claude vision + OpenCV calibration. Supports 26+ plot types including bar charts, scatter plots, forest plots, Kaplan-Meier curves, box plots, and more.
beita6969/ScienceClaw 571
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statistical-testing
Advanced statistical testing including hypothesis testing, Bayesian analysis, survival analysis, time series, multivariate methods, and meta-analysis. Use when user needs specific statistical tests beyond basic EDA, power analysis, Bayesian inference, survival curves, time series forecasting, or meta-analysis. Triggers on "hypothesis test", "Bayesian", "survival analysis", "time series", "meta-analysis", "bootstrap", "permutation test", "mixed model", "structural equation".
beita6969/ScienceClaw 571