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Found 141 Skills
Comprehensive code review for diffs. Analyzes changed code for security vulnerabilities, anti-patterns, and quality issues. Auto-detects domain (frontend/backend) from file paths.
Evaluate scientific claims and evidence quality. Use for assessing experimental design validity, identifying biases and confounders, applying evidence grading frameworks (GRADE, Cochrane Risk of Bias), or teaching critical analysis. Best for understanding evidence quality, identifying flaws. For formal peer review writing use peer-review.
Review dependency PRs with structured research, existing-PR-discussion capture, multi-lens analysis (security, code quality, impact), and a repeatable verdict template. USE FOR: dependency update PRs, Renovate/Dependabot PRs, library upgrade reviews, "review this dependency PR", "should we merge this update". DO NOT USE FOR: feature PRs, application code reviews, dependency automation/bot configuration, or unattended merge without confirmation.
Semi-automated design quality review for Flows apps. Runs concrete repo probes (grep, lint, build) to propose a draft 1–5 score for each of the official 10 quality-guidelines questions from docs.cognite.com/cdf/flows/guides/quality-guidelines, then asks the user to confirm or override each score. Still requires the user to walk their tasks end-to-end in the running app (Step 2) since navigation and clickability feel cannot be measured statically. Writes reviews/design-review/feedback-round-<N>/design-review-report.md with an overall average and prioritized fix lists. Use when the user asks to run a Flows design review, run the design quality assessment, or run flows-design-review. Must be run AFTER flows-code-review reaches 0 Must Fix and BEFORE flows-external-app-submit.
Runs a full CORE-EEAT 80-item content quality audit, scoring content across 8 dimensions with weighted scoring by content type. Produces a detailed report with per-item scores, dimension analysis, and a prioritized action plan.
Use before merging any change. Use when reviewing code written by yourself, another agent, or a human. Use when you need to assess code quality across multiple dimensions before it enters the main branch.
Structured review process for Remotion video implementations. Analyzes spec compliance, detects common timing/easing issues, validates asset quality, and provides prioritized revision lists. Use when reviewing Remotion code against design specs or performing quality assurance on video compositions. Trigger phrases "review video code", "check spec compliance", "audit Remotion implementation".
Analyze messy and unstructured Excel files to identify data quality issues, detect format inconsistencies, find missing values, and generate comprehensive analysis reports. Use when Claude needs to work with Excel files (.xlsx, .xls) for data quality assessment, structure analysis, or when users request data auditing, cleaning recommendations, or statistical summaries of spreadsheet data.
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.
Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.
Measure and improve the quality of AI models and agents on Google Cloud using the Eval Quality Flywheel methodology. Use when evaluating an agent or model, building an eval dataset, picking or writing evaluation metrics, analyzing failures, comparing results before and after a fix, or when guidance is needed on Agent Platform eval methodology — including dataset schema, LLM-as-judge scoring, and common failure causes. For fine-tuning, use agent-platform-tuning. For deployment, use agent-platform-deploy.
Use when reviewing or scoring AI-generated unit tests/UT code, especially when coverage, assertion effectiveness, or test quality is in question and a numeric score, risk level, or must-fix checklist is needed