Découvrez les skills Données et IA, comparez leurs dépôts et consultez leurs instructions.
Use when errors occur deep in execution and you need to trace back to find the original trigger - systematically traces bugs backward through call stack, adding instrumentation when needed, to identify source o...
Run section orchestrators to coordinate multi-component workflows. Use when starting work on a section.
Identify and document model hyperparameters from papers. Use when setting up training configurations.
Analyze SIMD usage opportunities in Mojo code. Use to find performance optimization opportunities.
Verify a PR is ready for merge (CI passing, approvals, no conflicts). Use before merging to ensure all requirements met.
Analyze ML model architecture from papers and code. Use when understanding model structure for implementation.
Create API reference documentation from docstrings. Use when documenting public module interfaces.
Measure function performance and compare implementations. Use when optimizing critical code paths.
Process and validate datasets for training. Use when setting up data pipelines.
Validate Mojo syntax against current v0.26.1+ standards. Use to catch syntax errors before compilation.
Identify performance optimization opportunities. Use when improving code efficiency.
Coordinate implementation phase by delegating tasks and ensuring code quality. Use during implementation phase to manage engineer tasks.
Examine code organization and identify structural patterns. Use when reviewing module design or understanding architecture.
Diagnose and fix CI/CD failures by analyzing logs, reproducing locally, and applying fixes. Use when CI checks fail on pull requests.
Retrieve all review comments from a pull request using the GitHub API. Use when you need to see what feedback has been provided on a PR.
Generate fix suggestions based on error patterns and best practices. Use when analyzing failures to get actionable remediation steps.
Measure test coverage and identify untested code. Use when assessing test completeness.
Execute model training with optimization algorithms. Use when running training loops on datasets.
Check Mojo code for memory safety issues (ownership violations, use-after-free, etc.). Use to catch memory bugs.
Identify code quality issues and anti-patterns. Use when reviewing code for maintainability problems.
Scan code for security vulnerabilities and unsafe patterns. Use before committing sensitive code or in security reviews.
Validate and verify dependencies are available and compatible. Use when setting up environments.
Measure model performance on test datasets. Use when assessing accuracy, precision, recall, and other metrics.
Validate GitHub Actions workflow files for syntax, best practices, and correctness. Use before committing workflow changes or when workflows fail.
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.