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Claude Skills by Harrypatria
github.com/Harrypatria28 skills0 installs0 views
- Taste AuditReviews an existing UI, screenshot, URL or codebase for AI slop, UX debt, accessibility failures and design-system drift, then returns a scored, prioritised fix plan and (on request) applies it. Use for "review my UI", "why does this look AI-generated", "audit", "make it look professional", "roast my landing page", "is this accessible", or before a launch.Votes: 0GitHub stars: 4
- Taste BannersDesigns social covers, ad creatives, website heroes, email headers, Open Graph cards and print banners as export-ready HTML/SVG, always in three genuinely different art directions from one token set, with platform safe zones, crop tests and Playwright PNG export. Use for "banner", "OG image", "social cover", "LinkedIn/X/YouTube header", "Instagram post/story", "display ad", "hero image", "email header", "poster".Votes: 0GitHub stars: 4
- Taste BrandDefines positioning, personality, voice, tone, messaging and visual identity direction, then freezes them into brand-guidelines.md for every other Tastemaker skill. Use for "brand", "voice and tone", "messaging", "tagline", "brand guidelines", "copy feels generic", or as a 10-line Light pass before landing pages, banners and decks. Bans hype copy and imitation of real brands.Votes: 0GitHub stars: 4
- Taste BriefTurns a PRD, feature list, user story dump, screenshot or one-line app idea into a BUILD_MANIFEST.md (users, jobs, flows, views, states, data contracts, component inventory) before any UI is generated. Use when the user says "build this app", pastes requirements, or starts a new product in a vibe-coding tool. Part of the Tastemaker suite; prevents scope drift and generic layouts.Votes: 0GitHub stars: 4
- Taste ComponentsImplements accessible, token-themed interfaces with shadcn/ui, Radix and Tailwind CSS (v3 or v4) - app shells, page headers, forms with react-hook-form + zod, data tables with TanStack, charts, overlays, command palette, dark mode - plus a Portable Mode in plain HTML for .NET, Spring and air-gapped stacks. Use when building or restyling components, "use shadcn", "Tailwind", "dark mode", "make this form/table better", or wiring a design system into code.Votes: 0GitHub stars: 4
- Taste DirectionDesign decision engine of the Tastemaker suite. Chooses style, palette, typography, layout, navigation, density, motion and chart types from the product archetype and audience, with a one-line reason for each, and runs UX-law and accessibility checks before any code. Use when starting or restyling any UI, choosing colors or fonts, picking a dashboard layout or chart, or when output "looks generic". Extends ui-ux-pro-max style and palette lookups with a reasoned, slop-free decision.Votes: 0GitHub stars: 4
- Taste GuardQuality gate and fix loop of the Tastemaker suite. Runs the slop scanner, the state matrix, an accessibility trace and a compile trace on generated UI, then fixes findings until clean (max 3 cycles). Use after generating or editing any frontend code, before saying "done", when a hook reports slop findings, or when the user says "check", "verify", "lint the UI", "is this production ready".Votes: 0GitHub stars: 4
- Taste IdentityCreates original logo systems, symbols, wordmarks, favicons, app icons, icon sets and brand collateral (business card, letterhead, email signature, avatars) as clean, accessible SVG and HTML, then writes logo usage rules into brand-guidelines.md. Use for "logo", "favicon", "app icon", "brand kit", "icon set", "letterhead", "email signature". Never traces or imitates existing marks.Votes: 0GitHub stars: 4
- Taste SlidesBuilds strategic HTML presentations - pitch, QBR, strategy, launch, sales, training, status - with takeaway titles, one idea per slide, token theming, Chart.js data slides, speaker notes, keyboard/touch navigation, overview mode and clean print-to-PDF, from a ready template. Use for "slides", "deck", "presentation", "pitch deck", "board update", "turn this report into slides".Votes: 0GitHub stars: 4
- Taste TokensBuilds the design-token system every Tastemaker skill consumes - primitive, semantic and component layers in design-tokens.json, light/dark/high-contrast themes, density modes, and generated tokens.css, Tailwind v4 @theme, Tailwind v3 config and shadcn/ui variables, with automatic reference and WCAG contrast validation. Use for "design system", "tokens", "theme", "dark mode", "brand colors in Tailwind", "component specs", or before any UI build that lacks tokens.Votes: 0GitHub stars: 4
- TastemakerEntry point and router for the Tastemaker design suite. Use for any request that creates, restyles, reviews or fixes a user interface, design system, brand, logo, banner or slide deck, in Claude Code, Claude.ai, Google AI Studio, Lovable, Replit, Bolt, v0, Cursor or similar. Triggers include "build a dashboard / landing page / app", "make it look better", "it looks AI-generated", "fix the UI", "design system", "dark mode", "shadcn", "Tailwind", "deck", "banner", "logo". Routes to the right ta...Votes: 0GitHub stars: 4
- Ai Prediction ExplainerAI Prediction Explainer. Generative AI chatbot that explains machine learning predictions in plain language - RTCC prompt (Role, Task, Context, Constraints), server-built context JSON with predictions, SHAP contributions and what-if results, OpenAI or Anthropic provider adapter with streaming, timeouts, retries, caching and rate limits, numeric grounding guard against invented numbers, prompt-injection handling, template fallback, notebook demo. Use for LLM explanations of model output, expla...Votes: 0GitHub stars: 2
- Data Mining Machine LearningData Mining and Machine Learning Skill (hub). Use for any data science, machine learning, ML, data mining, predictive modeling, classification, regression, clustering, forecasting, anomaly detection, A/B test, model deployment or explainable AI task in Jupyter, JupyterLab, Google Colab, VS Code or Claude Code. Routes the request through CRISP-DM from business brief to notebook, trained pickle, FastAPI service, React what-if app and LLM prediction explainer, and blocks analysis slop and produc...Votes: 0GitHub stars: 2
- Data MiningData Mining. Unsupervised pattern discovery - clustering and customer segmentation (k-means, k-medoids, hierarchical, DBSCAN, HDBSCAN, Gaussian mixtures), association rules and market basket analysis (Apriori, FP-Growth, support, confidence, lift), anomaly and outlier detection (Isolation Forest, LOF, one-class SVM, robust z-scores, autoencoders), dimensionality reduction for exploration (PCA, t-SNE, UMAP), sequence mining, recommenders and text mining. Use for segmentation, clustering, unsup...Votes: 0GitHub stars: 2
- Data PreparationData Preparation. Clean, join and split data without leakage - data cleaning, joins and merges with row-count checks, deduplication, missing value imputation, type fixes, outlier handling, train test split, validation split, cross-validation folds, stratified, group-aware and time-series (forward-chaining) splits, scikit-learn Pipeline and ColumnTransformer. Use for data wrangling, preprocessing, data cleaning, ETL for machine learning, or any request to prepare data for a model. CRISP-DM Dat...Votes: 0GitHub stars: 2
- Data ProfilingData Profiling. Explore and profile a dataset before modeling - EDA (exploratory data analysis), distributions, skew, outliers, missingness (MCAR, MAR, MNAR), duplicates, cardinality, class balance, label quality, leakage risk, bias and representativeness, drift between periods. Use when someone says explore, profile, EDA, data quality, understand the data, check the dataset, or before any machine learning, data mining or statistical analysis. CRISP-DM Data Understanding.Votes: 0GitHub stars: 2
- Data StorytellingData Storytelling. Turn analysis into a decision-first narrative and interactive what-if analytics - SCQA story structure, findings-first headings, chart design, ipywidgets dashboards (threshold explorer, prevalence explorer, what-if scenario analyzer, cost-matrix editor, cohort filter, scenario comparison, sensitivity view), static fallback, nbconvert HTML and Voila export. Use for reports, presentations of results, executive summaries, dashboards in notebooks, interactive widgets or explain...Votes: 0GitHub stars: 2
- Feature EngineeringFeature Engineering. Create, extract and select model inputs - feature extraction (PCA, SVD, TF-IDF, embeddings, signal features), feature engineering (date parts, cyclical encoding, lags, rolling windows, ratios, interactions, binning, target encoding, frequency encoding, geospatial), feature selection (mutual information, RFE, L1, stability selection), feature dictionary and ablation. Use for feature creation, feature construction, encoding categoricals, dimensionality reduction for modelin...Votes: 0GitHub stars: 2
- Hyperparameter TuningHyperparameter Tuning. Tune machine learning models honestly - grid search, random search, successive halving, Hyperband, Bayesian optimization with Optuna and pruning, nested cross-validation, early stopping on validation folds, search spaces for logistic regression, random forest, XGBoost, LightGBM, SVM, k-NN and neural networks, compute budgets, reporting spread and parameter importance. Use for hyperparameter optimization, GridSearchCV, RandomizedSearchCV, Optuna, model tuning or improvin...Votes: 0GitHub stars: 2
- Ml Quality GuardML Quality Guard. Rigor harness for data science and machine learning work: scan notebooks, scripts, APIs and prompts for leakage, statistical errors, imbalance mistakes, reproducibility gaps, production and LLM explainer slop, then fix and score. Use when asked to check, verify, lint, review, validate, score or "is this right" for a notebook, model, pipeline, analysis, FastAPI service or explainer, and before declaring any ML deliverable done.Votes: 0GitHub stars: 2
- Ml Web ApplicationML Web Application. Build a React front end for a machine learning model with the Tastemaker design workflow - predictor workbench with schema-driven sliders, result and explanation panels, sensitivity chart, scenario comparison, AI explainer chat with streaming, model information drawer, every loading, empty, error, not-ready, rate-limited and LLM-unavailable state, typed API client from OpenAPI, Vite, TypeScript, Tailwind, shadcn/ui, TanStack Query, Recharts, Vitest. Use for a UI, dashboard...Votes: 0GitHub stars: 2
- Model DeploymentModel Deployment. Serve a trained model in production - load a pickled pipeline safely, FastAPI prediction service (predict, batch, what-if, SHAP explanations, LLM narrative), Pydantic validation from schema.json, Docker multi-stage image, docker-compose with nginx, health checks, API keys, CORS, rate limits, JSON logging, Prometheus metrics, drift monitoring (PSI, KS), batch scoring, ONNX or PMML export and air-gapped installs. Use for deploy, serve, productionize, REST API, MLOps, model ser...Votes: 0GitHub stars: 2
- Model EvaluationModel Evaluation. Evaluate classifiers and regressors honestly - confusion matrix, precision, recall, F1, F-beta, MCC, balanced accuracy, ROC-AUC versus PR-AUC on imbalanced data, accuracy paradox, threshold selection with a cost matrix, calibration (Platt, isotonic, Brier score), confidence intervals, slice analysis and fairness metrics, regression metrics (MAE, RMSE, MAPE, pinball loss). Use for model metrics, which metric to use, imbalanced classes, choosing a threshold, model comparison, ...Votes: 0GitHub stars: 2
- Model ExplainabilityModel Explainability. Explain machine learning models and export reusable knowledge - explainable AI (XAI), feature importance (permutation, impurity caveats), SHAP values, partial dependence, ALE, ICE, LIME, surrogate trees and rules, counterfactual explanations, feature dictionary, insights.json, model card, data sheet, metric glossary, slice and fairness analysis. Use for interpretability, why did the model predict this, what drives the model, model documentation, model cards or responsibl...Votes: 0GitHub stars: 2
- Model TrainingModel Training and Saving. Train, test and save machine learning models - baselines, algorithm choice, scikit-learn or imbalanced-learn Pipeline, class imbalance handling, cross-validation, threshold on validation, one-time test evaluation, final refit, learning curves, pickle persistence (.pkl or .sav), model bundle with metadata, schema, checksum and background sample, reload parity test, version guard and verify-model. Use for fit a model, train classifier or regressor, build a predictive ...Votes: 0GitHub stars: 2
- Notebook AuditNotebook Audit. Score and fix an existing Jupyter notebook, Colab notebook, Python script, ML repository or model service - leakage, statistics, imbalance, reproducibility, explainability, communication and production readiness - with a ranked findings list, RIGOR score and minimal fixes. Use for review my notebook, audit this analysis, check this model, code review for data science, is this production ready, or before sharing or deploying someone else's work.Votes: 0GitHub stars: 2
- Problem FramingProblem Framing. Turn a business brief, PRD, dataset description or stakeholder question into a one-page ANALYSIS_MANIFEST.md before any modeling. Use when someone asks what to predict, how to measure success, which metric or baseline to use, the cost of errors, or how to scope a machine learning, data science, data mining, predictive modeling, forecasting, segmentation, churn, fraud, risk scoring or A/B testing project. CRISP-DM Business Understanding.Votes: 0GitHub stars: 2
- Statistical AnalysisStatistical Analysis. Hypothesis tests, A/B tests and experiments, confidence intervals, effect sizes, power and sample size, multiple comparisons, regression diagnostics, Bayesian alternatives, time series (stationarity, ARIMA, ETS) and survival analysis (Kaplan-Meier, Cox), with causal caution (randomization, difference-in-differences, matching, instrumental variables). Use for statistics, significance testing, t-test, chi-squared, ANOVA, Mann-Whitney, correlation, regression interpretation...Votes: 0GitHub stars: 2