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Found 2,057 Skills
Novel Chapter Review - Triggered when proofreading, reviewing, or evaluating the quality of novel chapters is required. Keywords: review, proofread, manuscript evaluation, inspection, quality assessment.
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.
Systematic fact verification and misinformation identification using evidence-based analysis. Use when: verifying claims, checking facts, identifying misinformation, evaluating source credibility, or when user asks to "fact check", "verify", "is this true", or mentions claims that need validation.
Analyzes events through physics lens using fundamental laws (thermodynamics, conservation, relativity), quantitative modeling, systems dynamics, and energy principles to understand causation, constraints, and feasibility. Provides insights on energy systems, physical limits, technological feasibility, and complex systems behavior. Use when: Energy decisions, technology assessment, systems analysis, physical constraints, feasibility evaluation. Evaluates: Energy flows, conservation laws, efficiency limits, physical feasibility, scaling behavior, emergent properties.
Measure and improve how well your AI works. Use when AI gives wrong answers, accuracy is bad, responses are unreliable, you need to test AI quality, evaluate your AI, write metrics, benchmark performance, optimize prompts, improve results, or systematically make your AI better. Covers DSPy evaluation, metrics, and optimization.
Machine learning development patterns, model training, evaluation, and deployment. Use when building ML pipelines, training models, feature engineering, model evaluation, or deploying ML systems to production.
Product interaction and UX expert. Use when reviewing UI/UX, conducting heuristic evaluations, designing user journeys, applying cognitive psychology principles, or ensuring WCAG 2.2 accessibility compliance.
Evaluate skill quality against best practices. Use when asked to "rate this skill", "review skill quality", "check skill formatting", "is this skill good", "evaluate SKILL.md", "grade this skill", or when validating skill files before publishing.
Testing framework for evaluating Databricks skills. Use when building test cases for skills, running skill evaluations, comparing skill versions, or creating ground truth datasets with the Generate-Review-Promote (GRP) pipeline. Triggers include "test skill", "evaluate skill", "skill regression", "ground truth", "GRP pipeline", "skill quality", and "skill metrics".
Score assistant responses for clarity on a strict 1-5 scale, then return strict JSON only with score, rationale, and improvement suggestions. Use when the user asks to evaluate clarity, grade clarity, or critique clarity quality.
Deep-dive analysis of GitHub projects. Use when the user mentions a GitHub repo/project name and wants to understand it — triggered by phrases like "help me look at this project", "learn about XXX", "how is this project", "analyze the repo", or any request to explore/evaluate a GitHub project. Covers architecture, community health, competitive landscape, and cross-platform knowledge sources.
Build automated evaluation suites for AI agents using golden datasets, rubrics, and regression gates.