autonomous-loop
Run autonomous development loops with intelligent exit detection. Use for complex multi-step tasks where Claude works iteratively until completion. Inspired by Ralph framework.
Explorez les skills IA publiés de fernando-fernandez3 et consultez leurs instructions source.
Skills indexés
11Étoiles des dépôts des skills indexés
0Forks du dépôt
0Moyenne des étoiles par skill indexé
0Les étoiles d’un dépôt peuvent se répéter entre ses skills.
Run autonomous development loops with intelligent exit detection. Use for complex multi-step tasks where Claude works iteratively until completion. Inspired by Ralph framework.
Intelligent task router that detects intent and routes to appropriate sub-workflows. Use as entry point for any development task.
Identifies high-quality leads for your product or service by analyzing your business, searching for target companies, and providing actionable contact strategies. Perfect for sales, business development, and ma...
Documentation generation for APIs, READMEs, and architecture. Audience-aware output.
Code review workflow for PRs and local changes. Integrates code-review and security-review agents.
Enforce test-driven development workflow for Python. Write tests FIRST, then implement minimal code to pass. Use when implementing new features, fixing bugs, or refactoring.
Generate pytest tests for Python functions and classes. Use when asked to write tests, add test coverage, or create unit tests.
Safe refactoring with snapshot tests, incremental changes, and verification after each step. Rollback ready.
Ruthless feature planning with exhaustive requirements gathering, architecture design, and approval gates. Use for new functionality.
Structured debugging protocol. Reproduce, isolate, trace, hypothesize, fix, prevent. Domain-aware expert thinking.
Security vulnerability detection and remediation for Python code. Use PROACTIVELY after writing code that handles user input, API endpoints, authentication, or sensitive data. Flags secrets, injection vulnerabi...
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.