3 535 testing skills from 425 authors. They run test suites, check accessibility and catch what broke. Half of them fit into 1 800 tokens or less — that is what one costs your context window when the agent loads it. 404 ship runnable scripts rather than instructions alone. 2 of them cannot work without an MCP server, most often rube. We also found 403 copies of these same skills sitting in other people's repositories — counted once here, not 403 times.
3 535 unique 425 authors 2 151 updated this month 319 from vendors
Build and analyze pangenomes for prokaryotes (Panaroo, PPanGGOLiN, PEPPAN, GET_HOMOLOGUES, anvi'o pangenomics) and eukaryotes (Minigraph-Cactus, PGGB, vg pangenome graphs). Implement Tettelin core/accessory/cloud genome decomposition (Tettelin 2005), Heap's law open/closed pangenome modeling, gene presence/absence GWAS (Scoary, pyseer), pangenome graph variant calling (vg, PanGenie), and structural-variation graph indexing. Use when assembling species- or genus-level pan-gene catalogs, separating core from accessory/shell/cloud genes, testing gene-content associations with phenotypes, building pangenome graphs from haplotype-resolved assemblies, calling SVs from pangenome graphs, or selecting between bacterial-pangenome and eukaryotic-pangenome workflows.
Discovers periodic signals of unknown period in time-series omics data using Lomb-Scargle periodograms (scipy), autocorrelation, and wavelet time-frequency decomposition (pywt). Identifies dominant frequencies, handles irregularly sampled data, and detects transient periodicity. Use when searching for periodic patterns of unknown period length, analyzing cell cycle oscillations, or processing unevenly spaced time-series. Not for testing known 24-hour rhythms (see temporal-genomics/circadian-rhythms).
Detect positive (diversifying / episodic / pervasive) selection using codon dN/dS frameworks. Implements PAML codeml site models (M0/M1a/M2a/M7/M8/M8a), branch models, branch-site model A (Zhang 2005), and HyPhy methods (BUSTED, BUSTED-S, BUSTED-MH, BUSTED-PH, MEME, FEL, FUBAR, aBSREL, SLAC, RELAX, GARD, FUBAR-MH). Includes McDonald-Kreitman framework (asymptotic alpha, impMKT, polyDFE, DFE-alpha, GRAPES) for within-species + divergence inference, RERconverge for trait-correlated rate shifts, CSUBST for convergent substitution, and PhyloAcc for accelerated noncoding evolution. Use when testing adaptive evolution at codons, branches, or full gene; running GARD recombination pre-screen; controlling alignment-error and gBGC false positives; reconciling PAML vs HyPhy results; or performing genome-scale selection scans.
End-to-end proteomics workflow from MaxQuant output to differential protein abundance. Orchestrates data import, normalization, imputation, and statistical testing with limma (default) or MSstats for complex feature-level designs. Use when processing mass spectrometry proteomics.
Spatial analysis of cell neighborhoods and interactions in IMC data. Covers neighbor graphs, spatial statistics, and interaction testing. Use when analyzing spatial relationships between cell types, testing for neighborhood enrichment, or identifying cell-cell interaction patterns in imaging mass cytometry data.
Statistical analysis for metabolomics data. Covers preprocessing (log2 transformation, normalization), limma moderated testing with empirical Bayes, Welch's t-tests with BH correction, fold change estimation, and multivariate methods (PCA, PLS-DA, OPLS-DA). Use when identifying differentially abundant metabolites or building classification models.
Add p-value brackets, significance asterisks, and effect-size annotations to distribution plots using ggpubr, ggsignif, and statannotations with correct test selection (parametric vs non-parametric vs paired), multiple-testing adjustment, and rendering of negative results. Use when a boxplot/violin/raincloud needs in-figure statistical comparisons between groups.
Performs subgroup and heterogeneous treatment effect (HTE) analyses for clinical trials. Covers Mantel-Haenszel pooling, Breslow-Day, interaction tests in regression, RERI for additive interaction, modern data-adaptive HTE methods (STEPP, SIDES, causal forests, X/R-learners), Bayesian shrinkage (Dixon-Simon, MAP, EXNEX), graphical multiplicity (Bretz-Maurer), and credibility frameworks (Sun BMJ, EMA 2019). Use when analyzing treatment effects across patient subgroups for regulatory submissions or precision-medicine claims.
Models continuous temporal trajectories from bulk or time-resolved omics data using generalized additive models (mgcv), spline regression, and changepoint detection (segmented, ruptures). Fits smooth gene expression curves and tests trajectory differences between conditions. Use when fitting non-linear temporal models to bulk time-series data or comparing developmental trajectories across conditions. Not for single-cell pseudotime (see single-cell/trajectory-inference).
Detect, date, and contextualize whole-genome duplication (WGD / paleopolyploidy) events using wgd v2 (Chen & Zwaenepoel 2024), KsRates (Sensalari 2022 substitution-rate-corrected Ks dating), DupGen_finder (Qiao 2019), MAPS (Li 2018 phylogenomic), POInT (Conant 2008 ordered-block), SLEDGe (2024 ML-based), Whale.jl (Bayesian DTL+WGD), and synteny-anchored paranome construction. Use when identifying ancient polyploidy from Ks distributions and synteny block analysis, positioning WGD events relative to speciation, distinguishing tandem from segmental from WGD duplications, dating the 2R/3R vertebrate / fish / salmonid WGDs, building paranome and Ks-age mixture models, applying KsRates substitution-rate correction across lineages, or testing alternative biased-fractionation / dosage-balance models post-WGD.
Publication-grade figure correctness and legibility rules. Load before drawing any plot and call `apply_figure_style()` — sets a role-mapped font-size ladder, outward ticks, frameless legends, and 300-dpi output. The skill is a checklist, not a house look: data fidelity (claim-titles tested against every row, excluded data never enters summaries), label economy (floor and ceiling), colour threading, chart-choice-by-data-shape, layout, and a render-then-verify QA loop (bbox collision + per-panel perceptual check). Ships helpers: focal_palette, bar_with_points, strip_with_median, end_of_line_labels, panel_letter, set_frame, panel_crops. For multi-panel figures load `figure-composer`; for whole-paper figure arc load `paper-narrative`.
End-to-end GWAS automation wrapping PLINK2 for genotype QC and REGENIE for two-step whole-genome regression association testing. Produces Manhattan plots, QQ plots, clumped lead variants, and structured summary statistics.
Differential expression analysis for label-free quantitative (LFQ) intensity data with standard MaxQuant and DIA-NN output. Workflow includes preprocessing, imputation, and statistical testing.
Differential expression analysis for bulk RNA-seq and pseudo-bulk count matrices with QC, PCA, and contrast testing.
Use this skill when the user wants to debug, diagnose, or systematically iterate on an experiment that already exists, or when they need a structured experiment log for tracking runs, hypotheses, failures, results, and next steps during active research. Apply it to underperforming methods, training that will not converge, regressions after a change, inconsistent results across datasets, aimless experimentation without progress, and questions like 'why doesn't this work?', 'no progress after many attempts', or 'how should I investigate this failure?'. Also use it for setting up practical experiment logging/record-keeping that supports debugging and iteration. Do not use it for designing a brand-new experiment pipeline or full experiment program (use experiment-pipeline), generating research ideas, fixing isolated coding/syntax errors, or writing retrospective summaries into research memory/notes/knowledge bases.
Iterative code refinement through plan → code → evaluate → refine cycles. Runs lint checks (ruff), tests (pytest), and structured self-evaluation each cycle, then diagnoses failures and refines. Decomposes complex tasks into sequential phases, iterates up to 3 times per phase (10 total). Use when: the main agent delegates a code task with 'MODE: MORE_EFFORT', the user selects 'More Effort' code generation mode, or the task explicitly requests iterative refinement for higher code quality. Do NOT use for single-pass code generation (Lite mode), experiment pipeline orchestration (use experiment-pipeline), or diagnosing a specific experiment failure (use experiment-craft).
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
Automated LLM-driven hypothesis generation and testing on tabular datasets. Use when you want to systematically explore hypotheses about patterns in empirical data (e.g., deception detection, content analysis). Combines literature insights with data-driven hypothesis testing. For manual hypothesis formulation use hypothesis-generation; for creative ideation use scientific-brainstorming.
Structured 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.
Guide Claude through omicverse's bulk RNA-seq DEG pipeline, from gene ID mapping and DESeq2 normalization to statistical testing, visualization, and pathway enrichment. Use when a user has bulk count matrices and needs differential expression analysis in omicverse.
Walk Claude through PyDESeq2-based differential expression, including ID mapping, DE testing, fold-change thresholding, and enrichment visualisation.
Perform statistical tests, hypothesis testing, correlation analysis, and multiple testing corrections using scipy and statsmodels. Works with ANY LLM provider (GPT, Gemini, Claude, etc.).
Perform comprehensive gene enrichment and pathway analysis using gseapy (ORA and GSEA), PANTHER, STRING, Reactome, and 40+ ToolUniverse tools. Supports GO enrichment (BP, MF, CC), KEGG, Reactome, WikiPathways, MSigDB Hallmark, and 220+ Enrichr libraries. Handles multiple ID types (gene symbols, Ensembl, Entrez, UniProt), multiple organisms (human, mouse, rat, fly, worm, yeast), customizable backgrounds, and multiple testing correction (BH, Bonferroni). Use when users ask about gene enrichment, pathway analysis, GO term enrichment, KEGG pathway analysis, GSEA, over-representation analysis, functional annotation, or gene set analysis.
Provide differential diagnosis for patients with suspected rare diseases based on phenotype and genetic data. Matches symptoms to HPO terms, identifies candidate diseases from Orphanet/OMIM, prioritizes genes for testing, interprets variants of uncertain significance. Use when clinician asks about rare disease diagnosis, unexplained phenotypes, or genetic testing interpretation.
Production-ready RNA-seq differential expression analysis using PyDESeq2. Performs DESeq2 normalization, dispersion estimation, Wald testing, LFC shrinkage, and result filtering. Handles multi-factor designs, multiple contrasts, batch effects, and integrates with gene enrichment (gseapy) and ToolUniverse annotation tools (UniProt, Ensembl, OpenTargets). Supports CSV/TSV/H5AD input formats and any organism. Use when analyzing RNA-seq count matrices, identifying DEGs, performing differential expression with statistical rigor, or answering questions about gene expression changes.
Perform statistical modeling and regression analysis on biomedical datasets. Supports linear regression, logistic regression (binary/ordinal/multinomial), mixed-effects models, Cox proportional hazards survival analysis, Kaplan-Meier estimation, and comprehensive model diagnostics. Extracts odds ratios, hazard ratios, confidence intervals, p-values, and effect sizes. Designed to solve BixBench statistical reasoning questions involving clinical/experimental data. Use when asked to fit regression models, compute odds ratios, perform survival analysis, run statistical tests, or interpret model coefficients from provided data.
Live-test any Electron desktop app with native-devtools-mcp, Chrome DevTools Protocol, screenshots, OCR, and accessibility tools. Use when the user asks for Electron UI verification, MCP-driven app control, renderer CDP interaction, native desktop automation, screenshots, or OCR-driven checks.
Use this skill when the user wants to turn an application, product, startup idea, SaaS, mobile app, web app, API, AI product, or internal tool into a production-ready Markdown specification package for coding agents. Creates PRD/product spec, UX flows, design system brief, technical architecture, ADRs, AI/safety/privacy specs, API/data model, client/backend implementation specs, QA acceptance tests, release readiness, and executable task checklists. Do not use for simple one-off coding tasks unless the user asks for a complete spec, PRD, ADR, project plan for coding agents, or build package.
Decide exactly where bug-fix test coverage belongs. Use before adding, moving, or deleting tests after a bug fix or architectural change. Select one owning layer, reuse existing canonical suites, block redundant or weakly placed tests, and remove weaker duplicates.
Run local Go health checks (tests, coverage, lint) in Go repositories that contain go.mod/go.sum. Use when the user asks to run or interpret local Go test/coverage/lint workflows using tools like lazygotest, gocovsh, tparse, and golangci-lint. Do not use for Rust or non-Go projects.
Run unit tests for the Cosmos Explorer project. Use this skill when asked to run tests, check test results, or debug test failures.
Build a release APK and a simulator .app file locally for a React Native Expo app using gradlew and xcodebuild. Use when the user wants to produce installable artifacts without EAS (for e2e testing, sharing a build with a teammate, or quick iteration on a real device/simulator). Generates two shell scripts wired up to the user's actual app name, scheme, and bundle identifier.
Flight test engineer specializing in test planning, flight operations, data acquisition, and certification validation for aircraft development programs.
Elite Data Scientist skill with expertise in statistical analysis, predictive modeling, experimental design (A/B testing), feature engineering, and data visualization. Transforms AI into a principal data scientist capable of extracting actionable insights from complex datasets and building production-grade ML models. Use when: data-science, statistics, machine-learning, predictive-modeling,
Expert-level Simulation Platform Engineer specializing in autonomous driving simulation, scenario generation, sensor model validation, and large-scale regression testing pipelines. Expert-level Simulation Platform Engineer specializing in autonomous driving... Use when: autono...
Expert UX designer specializing in user research, interaction design, usability testing, and user-centered design methodology. Use when conducting user research, designing user flows, creating wireframes, or optimizing user experiences. Use when: ux-design, user-research, interaction-design, usability-testing, wireframing.
Expert-level Language Test Trainer with deep knowledge of IELTS, TOEFL, GRE, PTE academic testing formats, scoring rubrics, and test-taking strategies. Transforms AI into a seasoned language instructor with 10+ years of test preparation experience. Use when: ielts, toefl, language-test, test-preparation, esl.
Expert Test Prep Instructor specializing in SAT, ACT, GRE, GMAT, LSAT, and professional certification exam preparation. Expert in test-taking strategies, content review, performance analytics, and score improvement methodologies. Use when: test-prep, sat, act, gre, gmat, lsat, exam-prep, test-strategies, score-improvement.
Use when determining what test coverage ALREADY exists for a specific change (a PR, Jira key, Tech Breakdown doc, Testmo CSV, changed paths, or named component). Triggers on "what's already tested", "does this PR have tests", "what coverage exists for", "is this component covered", or "which behaviors have no test today". This is a backward-looking inventory of existing coverage for a concrete change. Do NOT use it to recommend or decide which new tests to add ("should I add integration tests here", "are unit tests enough"), to design a test strategy or plan, to run or fix existing tests, or to explain testing concepts like the test pyramid or which layers a repo uses — those are all out of scope.
Systematically investigate social media claims and viral content. Use when fact-checking complex claims, when decomposing multi-part assertions, or when investigating narratives that mix facts with interpretation.
Build and publish npx-executable CLI tools using Bun as the primary toolchain with npm-compatible output. Use when the user wants to create a new CLI tool, set up a command-line package for npx execution, configure argument parsing and terminal output, or publish a CLI to npm. Covers scaffolding, citty arg parsing, sub-commands, terminal UX, strict TypeScript, Biome + ESLint linting, Vitest testing, Bunup bundling, and publishing workflows. Keywords: npx, cli, command-line, binary, bin, tool, bun, citty, commander, terminal, publish, typescript, biome, vitest.
Use when running claudikins-kernel:verify, checking implementation quality, deciding pass/fail verdicts, or enforcing cross-command gates — requires actual evidence of code working, not just passing tests
Empirically verify guideline changes by running before/after eval runs across multiple models and ensuring no regressions. Use when proposing or reviewing changes to runner/models/guidelines.ts, or when the user asks to validate guidelines.
Set up, harden, audit, or explain a Linux VPS as a Codex App SSH host for Krypton-style agent workflows. Use when the user wants to move Codex or Claude Code work from a local Mac to a VPS, connect Codex App to an SSH host, configure Ubuntu 24.04/devbox tools, clone a repo remotely, run Codex/Claude/tmux/cmux on the VPS, forward ports for localhost testing, or create repeatable founder/builder setup instructions with proof checks.
> Enforces a disciplined Red-Green-Refactor (TDD) workflow in TypeScript/Node.js. Use this whenever creating new features, fixing bugs, or migrating logic to ensure high-quality, verifiable implementations.
Use for creating custom DSPy modules, extending dspy.Module, reusable components, stateful modules, serialization, and module testing.
Use this skill when you need to QA audit and fix a plugin skill file. Provides a methodology for verifying skill content against official documentation, fixing issues in-place, and producing verification reports.
Debug GitHub Actions workflows by downloading logs, analyzing summaries, and understanding how agentic workflows and the AWF firewall work together.