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Found 117 Skills
Assist developers in writing clean, maintainable code following software engineering best practices. Use when conducting code reviews, refactoring code, enforcing coding standards, seeking guidance on clean code principles, or integrating automated quality checks into development workflows.
Use when working with performance testing review multi agent review
Retrieves gene expression and omics datasets from ArrayExpress and BioStudies with gene disambiguation, experiment quality assessment, and structured reports. Creates comprehensive dataset profiles with metadata, sample information, and download links. Use when users need expression data, omics datasets, or mention ArrayExpress (E-MTAB, E-GEOD) or BioStudies (S-BSST) accessions.
Test suite audit coordinator (L2). Delegates to 5 workers (Business Logic, E2E, Value, Coverage, Isolation). Aggregates results, creates Linear task in Epic 0.
Use when working with comprehensive review full review
This skill should be used when verifying that a JIRA ticket meets organizational standards for epic relationships and description quality. It checks epic parent relationships and validates description completeness for coding assistants, developers, and stakeholders.
Review code for quality, security, and performance with comprehensive feedback.
Use this skill whenever the user mentions IP geolocation feeds, RFC 8805, geofeeds, or wants help creating, tuning, validating, or publishing a self-published IP geolocation feed in CSV format. Intended user audience is a network operator, ISP, mobile carrier, cloud provider, hosting company, IXP, or satellite provider asking about IP geolocation accuracy, or geofeed authoring best practices. Helps create, refine, and improve CSV-format IP geolocation feeds with opinionated recommendations beyond RFC 8805 compliance. Do NOT use for private or internal IP address management — applies only to publicly routable IP addresses.
PluginEval quality methodology — dimensions, rubrics, statistical methods, and scoring formulas. Use this skill when understanding how plugin quality is measured, when interpreting a low score on a specific dimension, when deciding how to improve a skill's triggering accuracy or orchestration fitness, when calibrating scoring thresholds for your marketplace, or when explaining quality badges to external partners like Neon.
Use when working with error debugging multi agent review
Profile and explore datasets to understand their shape, quality, and patterns before analysis. Use when encountering a new dataset, assessing data quality, discovering column distributions, identifying nulls and outliers, or deciding which dimensions to analyze.
Detect and flag AI-generated content markers in documentation and prose. Use when reviewing documentation for AI markers, cleaning up LLM-generated content, or auditing prose quality. Do not use when generating new content (use doc-generator) or learning writing styles (use style-learner).