This skill should be used when users need to fetch GitHub issues from the current repository, including getting the next issue to work on, retrieving specific issue fields (body, number, title, labels, etc.), or querying issues with custom sorting and filtering. Triggers on requests like "get the next GitHub issue", "what's the next issue number", "show me the issue body", or "find issues with label X".
WHAT YOU BECOME
Perfect for these scenarios
Automatically checks stories for completeness before moving to Todo.
Reformats tasks to match team’s story templates and standards.
Flags over-engineered tasks, suggests simplifications for clarity.
Ensures tasks align with SOLID principles for maintainable code.
MEASURED GAIN
Proven benefits and measurable impact
Cuts manual review time by automating validation and formatting.
Minimizes rework by enforcing standards pre-approval.
Boosts velocity by streamlining backlog grooming processes.
WHAT YOU GET
Files, tags and the three-step install
Tip: Read the documentation and the code before first use, so you know what it does and which permissions it needs.
NEXT SCRIPTS
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Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
Generate and maintain AGENTS.md files following the public agents.md convention. Use when creating documentation for AI agent workflows, onboarding guides, or when standardizing agent interaction patterns across projects.