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Found 1,929 Skills
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.
Evaluates and prevents unnecessary abstractions by analyzing interfaces, layers, and patterns against concrete requirements. Use when evaluating new abstractions, reviewing architecture proposals, detecting over-engineering, or simplifying existing code. Triggers on "is this abstraction necessary", "too many layers", "simplify architecture", "reduce complexity", "over-engineered", "do we need this interface", or when reviewing design patterns.
Critically review terminal user interfaces for UX quality, responsiveness, visual design, and interactivity. Use when asked to "review my TUI", "test my TUI UX", "audit my terminal UI", "check TUI responsiveness", "review TUI keybindings", "check interactivity", or any request to evaluate the user experience quality of a ratatui/crossterm/ncurses-based terminal application. Launches the TUI in tmux, systematically tests 10 dimensions (responsiveness, input conflicts, visual clarity, navigation, feedback loops, error states, layout, keyboard design, permission flows, visual design & color), and produces a graded report with screenshots and specific findings. Benchmarks against Claude Code, OpenCode, and Codex — the three best-in-class AI terminal UIs.
Evaluate acquisition channels using unit economics, customer quality, and scalability. Recommends scale/test/kill decisions.
Markets orchestration — connects ESPN live schedules with Kalshi & Polymarket prediction markets. Unified dashboards, odds comparison, entity search, and bet evaluation across platforms. Use when: user wants to see prediction market odds alongside ESPN game schedules, compare odds across platforms, search for a team/player on Kalshi or Polymarket, check for arbitrage between ESPN odds and prediction markets, or evaluate a specific game's market value. Don't use when: user wants raw prediction market data without ESPN context — use polymarket or kalshi directly. For pure odds math (conversion, de-vigging, Kelly) — use betting. For live scores without market data — use the sport-specific skill.
Technical research methodology with YAGNI/KISS/DRY principles. Phases: scope definition, information gathering, analysis, synthesis, recommendation. Capabilities: technology evaluation, architecture analysis, best practices research, trade-off assessment, solution design. Actions: research, analyze, evaluate, compare, recommend technical solutions. Keywords: research, technology evaluation, best practices, architecture analysis, trade-offs, scalability, security, maintainability, YAGNI, KISS, DRY, technical analysis, solution design, competitive analysis, feasibility study. Use when: researching technologies, evaluating architectures, analyzing best practices, comparing solutions, assessing technical trade-offs, planning scalable/secure systems.
Evaluate and validate Claude Code rules in .claude/rules/ directories. Use when auditing rule file quality, validating frontmatter and glob patterns, or checking rules organization before deployment. Do not use when writing new rules from scratch - use rule authoring guides instead. Do not use when evaluating skills or hooks - use skills-eval or hooks-eval instead.
Open-source AI observability platform for LLM tracing, evaluation, and monitoring. Use when debugging LLM applications with detailed traces, running evaluations on datasets, or monitoring production AI systems with real-time insights.
Create or evaluate an architecture decision record (ADR). Use when choosing between technologies (e.g., Kafka vs SQS), documenting a design decision with trade-offs and consequences, reviewing a system design proposal, or designing a new component from requirements and constraints.