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Skills IA data ai automation tools

Découvrez les skills data ai automation tools, comparez leurs dépôts et consultez leurs instructions.

Catégories: data ai automation tools

3,577–3,600 sur 8,185

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GitHub

auto-testing

timequity

Automatically generate and run tests after each code change. Use when: any code is generated or modified in the pipeline. Triggers: internal use only.

GitHub

secrets-guardian

timequity

Protect repositories from accidental secret commits. Essential when working with AI agents. Use when: setting up new project, adding pre-commit hooks, scanning for secrets, fixing leaked credentials. Triggers:...

GitHub

tidy-first

Fortiz2305

Execute structural refactoring with test verification before behavioral changes - use when tidying code before features, cleaning up duplication, or improving clarity

GitHub

claude-md

bandofai

Create and maintain CLAUDE.md files for Claude Code projects. Use when users want to create a new CLAUDE.md, improve an existing one, audit CLAUDE.md quality, set up Claude Code configuration for a repository,...

GitHub

roadmap-integration

marcusgoll

Standard Operating Procedure for /roadmap usage. Manage product roadmap via GitHub Issues (brainstorm, prioritize, track). Auto-validates features against project vision (from overview.md) before adding to road...

GitHub

project-initialization-phase

marcusgoll

Standard Operating Procedure for /init-project phase. Guide one-time project setup with interactive questionnaire, brownfield codebase scanning, 8-document generation (overview, architecture, tech-stack, data,...

GitHub

vibesafe

julep-ai

Uses the Vibesafe MCP server to scan, compile, test, save, diff, and report status for Vibesafe units. Activate when the user asks to run vibesafe CLI commands (scan/compile/test/save/diff/status), regenerate c...

GitHub

moai-cc-commands

AJBcoding

Claude Code Commands system, workflow orchestration, and command-line interface patterns. Use when creating custom commands, managing workflows, or implementing CLI interfaces.

GitHub

moai-project-template-optimizer

AJBcoding

Handle template comparison and optimization workflows including backup detection, smart merging, and update completion reporting. Enhanced with Context7 MCP for up-to-date documentation.

GitHub

moai-readme-expert

AJBcoding

README.md generation specialist with dynamic project analysis capabilities. Master professional README creation, specialized templates, and automated documentation generation. Create comprehensive, engaging R...

GitHub

moai-alfred-code-reviewer

AJBcoding

Enterprise systematic code review orchestrator with TRUST 5 principles, multi-language support, Context7 integration, AI-powered quality checks, SOLID principle validation, security vulnerability detection, and...

GitHub

moai-alfred-clone-pattern

AJBcoding

Enterprise Master-Clone pattern implementation guide for complex multi-step tasks with full project context, autonomous delegation, parallel processing, and intelligent task distribution; activates for large-sc...

GitHub

moai-change-logger

AJBcoding

Comprehensive change tracking and audit logging system that monitors file modifications, code changes, and project evolution. Use when tracking project history, maintaining audit trails, analyzing development p...

GitHub

creating-karabiner-modifications

AJBcoding

Use when configuring Karabiner-Elements keyboard customizations, before writing JSON - provides systematic workflow for complex modifications with correct structure, common patterns, and guidance on manual vs g...

GitHub

moai-essentials-refactor

AJBcoding

AI-powered enterprise refactoring with Context7 integration, automated code transformation, Rope pattern intelligence, and technical debt quantification across 25+ programming languages

GitHub

moai-essentials-review

AJBcoding

Enterprise comprehensive code review automation with AI-powered quality analysis, TRUST 5 enforcement, multi-language support, Context7 integration, security scanning, performance analysis, test coverage valida...

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.

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