data-analysis
Patterns for data loading, exploration, and statistical analysis
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Patterns for data loading, exploration, and statistical analysis
Patterns for data loading, exploration, and statistical analysis
Best practices for designing reproducible experiments
Best practices for designing reproducible experiments
Enforces baseline comparisons, cross-validation, interpretation, and leakage prevention for ML pipelines
Enforces baseline comparisons, cross-validation, interpretation, and leakage prevention for ML pipelines
Build features with AI coding tools (Claude Code, Lovable, Replit, Cursor). Use when implementing specs, iterating on AI code, or choosing tools. Focuses on tool selection, effective prompting, and iteration wo...
Track production app health and catch issues before users complain. Use after deploying, to check app status, or when investigating user reports. Covers error tracking, uptime monitoring, and metrics for non-te...
Test features before users find bugs. Use when feature is built, before deploying, or when bugs reported. Covers manual testing, edge cases, cross-browser testing, and testing checklists for non-technical found...
Protect your SaaS app from common vulnerabilities. Use when building auth, handling user data, or deploying features. Covers authentication, data protection, API security, and OWASP Top 10 for non-technical fou...
Optimize your SaaS app across four dimensions - Speed (page load, API response), Code (unused files, dead code), Database (orphaned data, schema hygiene), and Dependencies (package bloat, bundle size). Use when...
Turn ideas into clear, buildable specs for AI tools or stakeholder review. Use when starting features, planning projects, or when AI keeps building the wrong thing. Creates Quick Feature Specs (10-15 min) for i...
Fix bugs systematically instead of guessing. Use when features break, users report errors, or tests fail. Covers reproducing bugs, gathering diagnostic info, and working with AI tools to fix issues efficiently...
Create and manage project-specific design systems for SaaS applications. Use when starting a new app (CREATE mode), updating an existing design system (MODIFY mode), or building features that need to follow est...
Use when user asks to leverage claude or claude code to do something (e.g. implement a feature design or review codes, etc). Provides non-interactive automation mode for hands-off task execution without approva...
Generate or edit images using Google Gemini API via nanobanana. Use when the user asks to create, generate, edit images with nanobanana, or mentions image generation/editing tasks.
Use when user wants to execute long-running tasks that require multiple sessions to complete. This skill manages task decomposition, progress tracking, and autonomous execution using Codex non-interactive mode...
Extract subtitles/transcripts from a YouTube video URL and save as a local file. Use when you need to extract subtitles from a YouTube video.
Interactive feature development workflow from idea to implementation. Creates requirements (EARS format), design documents, and implementation task lists. Use when creating feature specs, requirements documents...
GitHub Spec-Kit integration for constitution-based spec-driven development. 7-phase workflow (constitution, specify, clarify, plan, tasks, analyze, implement). Use when working with spec-kit CLI, .specify/ dire...
React and Next.js performance optimization patterns. Use BEFORE implementing any React code to ensure best practices are followed.
Bootstrap lean multi-agent orchestration with beads task tracking. Use for projects needing agent delegation without heavy MCP overhead.
Bootstrap lean multi-agent orchestration with beads task tracking. Use for projects needing agent delegation without heavy MCP overhead.
Core engineering principles for implementation tasks
Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.