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Found 2,058 Skills
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.
INVOKE THIS SKILL when creating evaluation datasets, uploading datasets to LangSmith, or managing existing datasets. Covers dataset types (final_response, single_step, trajectory, RAG), CLI management commands, SDK-based creation, and example management. Uses the langsmith CLI tool.
Run a single experiment iteration. Edit the target file, evaluate, keep or discard.
Connect AI agents to your live Chrome session via CDP for real-time tab interaction, screenshots, and JS evaluation without re-login
Interact with the learning system: show stats, list/search accumulated knowledge, and graduate mature entries into agents/skills. Backed by learning.db (SQLite + FTS5). Use when user says "retro", "retro list", "retro search", "retro graduate", "check knowledge", "what have we learned", "knowledge health", "graduate knowledge".