flaky-detect
Identify, categorize, and troubleshoot flaky tests by analyzing CI history, execution patterns, and code structure to improve test suite reliability.
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133 skills found
Identify, categorize, and troubleshoot flaky tests by analyzing CI history, execution patterns, and code structure to improve test suite reliability.
Analyze codebases to generate evidence-grounded Loa artifacts using Enterprise-Grade Managed Scaffolding for structured reality mapping.
Process and generate multimedia with Google Gemini. Analyze audio, images, videos, and PDFs with high-context windows. Supports transcription, visual QA, OCR, and AI-driven image creation.
🛡️ GDPR & LGPD Privacy Guardian: Automated compliance scanner that detects PII exposure, insecure logging, and tracking violations in your codebase to prevent regulatory fines.
Advanced visual regression testing with pixel-perfect and AI-powered diff analysis, cross-browser validation, and responsive design checks to prevent UI regressions in CI/CD pipelines.
Automated static code review for Arduino, ESP32, and RP2040 projects. Identifies memory safety issues, structure improvements, and best practices to enhance firmware quality and reliability.
Automates the documentation of solved technical issues using YAML frontmatter, categorized directories, and institutional knowledge indexing for JUCE plugin development.
Foundational mental model and operational rules for using TraceMem to ensure secure, auditable, and compliant AI agent execution.
Automated PR CI monitoring and failure resolution agent.
Systematic debugging skill to trace errors backward through call stacks, identify original triggers, and implement layered defenses instead of patching symptoms.
AI-assisted version control for code agents. Track prompts, context, and diffs automatically with MemoV to ensure full traceability without polluting your git history.
Guidance and operational tips for identifying, reviewing, and managing pull requests created by the GitHub Copilot coding agent within your repository.