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Claude Skills by spacering-net
github.com/spacering-net21 skills2 installs52 views
- BrainstormingYou MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.Votes: 0GitHub stars: 3,839
- Executing PlansUse when you have a written implementation plan to execute in a separate session with review checkpointsVotes: 0GitHub stars: 3,839
- Requesting Code ReviewUse when completing tasks, implementing major features, or before merging to verify work meets requirementsVotes: 0GitHub stars: 3,839
- Subagent Driven DevelopmentUse when executing implementation plans with independent tasks in the current sessionVotes: 0GitHub stars: 3,839
- Test Driven DevelopmentUse when implementing any feature or bugfix, before writing implementation codeVotes: 0GitHub stars: 3,839
- Using SuperpowersUse when starting any conversation - establishes how to find and use skills, requiring skill invocation before ANY response including clarifying questionsVotes: 0GitHub stars: 3,839
- Writing PlansUse when you have a spec or requirements for a multi-step task, before touching codeVotes: 0GitHub stars: 3,839
- Writing SkillsUse when creating new skills, editing existing skills, or verifying skills work before deploymentVotes: 0GitHub stars: 3,839
- Citation ManagementComprehensive 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.Votes: 0GitHub stars: 3,839
- Experimental DesignDesign experiments and studies BEFORE data is collected — choosing a design, randomizing, blocking, and laying out treatment combinations so the results will actually be interpretable. Use whenever someone is planning a study, asks how to assign subjects/samples to groups, mentions randomization, blocking, stratification, controls, factorial or fractional-factorial designs, design of experiments (DOE), screening many factors, response-surface optimization, crossover or repeated-measures or sp...Votes: 0GitHub stars: 3,839
- Exploratory Data AnalysisPerform 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 scien...Votes: 0GitHub stars: 3,839
- Hypothesis GenerationStructured hypothesis formulation from observations. Use when you have experimental observations or data and need to formulate testable hypotheses with predictions, propose mechanisms, and design experiments to test them. Follows scientific method framework. For open-ended ideation use scientific-brainstorming; for automated LLM-driven hypothesis testing on datasets use hypogenic.Votes: 0GitHub stars: 3,839
- Paper LookupSearch 10 academic literature APIs for papers, preprints, citations, and open-access full text, and return results with reproducible provenance. Covers PubMed, PMC (full text), bioRxiv, medRxiv, arXiv, OpenAlex, Crossref, Semantic Scholar, CORE, Unpaywall. Use when searching for papers, citations, DOI/PMID/arXiv lookups, abstracts, full text, open-access PDFs, preprints, citation graphs, author publications, or any scholarly literature query. Triggers on mentions of any supported database or ...Votes: 0GitHub stars: 3,839
- Peer ReviewStructured manuscript/grant review with checklist-based evaluation. Use when writing formal peer reviews with specific criteria methodology assessment, statistical validity, reporting standards compliance (CONSORT/STROBE), and constructive feedback. Best for actual review writing, manuscript revision. For evaluating claims/evidence quality use scientific-critical-thinking; for quantitative scoring frameworks use scholar-evaluation.Votes: 0GitHub stars: 3,839
- Scholar EvaluationSystematically evaluate scholarly work using the ScholarEval framework, providing structured assessment across research quality dimensions including problem formulation, methodology, analysis, and writing with quantitative scoring and actionable feedback.Votes: 0GitHub stars: 3,839
- Scientific BrainstormingCreative research ideation and exploration. Use for open-ended brainstorming sessions, exploring interdisciplinary connections, challenging assumptions, or identifying research gaps. Best for early-stage research planning when you do not have specific observations yet. For formulating testable hypotheses from data use hypothesis-generation.Votes: 0GitHub stars: 3,839
- Scientific Critical ThinkingEvaluate scientific claims and evidence quality. Use for assessing experimental design validity, identifying biases and confounders, applying evidence grading frameworks (GRADE, Cochrane Risk of Bias), or teaching critical analysis. Best for understanding evidence quality, identifying flaws. For formal peer review writing use peer-review.Votes: 0GitHub stars: 3,839
- Scientific SchematicsCreate publication-quality scientific diagrams using Nano Banana 2 AI with smart iterative refinement. Uses Gemini 3.1 Pro Preview for quality review. Only regenerates if quality is below threshold for your document type. Specialized in neural network architectures, system diagrams, flowcharts, biological pathways, and complex scientific visualizations.Votes: 0GitHub stars: 3,839
- Scientific VisualizationMeta-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.Votes: 0GitHub stars: 3,839
- Statistical AnalysisGuided statistical analysis for research data - test selection, assumption checking, effect sizes, power analysis, Bayesian alternatives, and APA-formatted reporting. Use whenever a user wants to compare groups, test a hypothesis, analyze experimental or survey data, check statistical assumptions, compute required sample sizes, or write up results - even if they never name a specific test. Covers t-tests, ANOVA, chi-square, correlation, regression, non-parametric and Bayesian methods. For low...Votes: 0GitHub stars: 3,839
- Statistical PowerSample-size and statistical power calculations for planning studies. Use whenever someone asks "how many subjects/samples/replicates do I need", wants an a priori power analysis, a minimum detectable effect (MDE), a power curve, or needs to justify a sample size for a grant, IRB protocol, or pre-registration. Covers closed-form power for t-tests, ANOVA, proportions, correlations, chi-square, and regression, plus simulation-based (Monte Carlo) power for designs with no formula — logistic/Poiss...Votes: 0GitHub stars: 3,839