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Found 1,943 Skills
Critically evaluate and enhance app ideas, startup concepts, and product proposals. Use when users ask to "evaluate my idea", "review this concept", "is this a good idea", "validate my startup idea", or want honest feedback on technical feasibility and market viability. Creates/updates idea.md and validate.md and always reports GitHub links to changed files.
Build, validate, and deploy LLM-as-Judge evaluators for automated quality assessment of LLM pipeline outputs. Use this skill whenever the user wants to: create an automated evaluator for subjective or nuanced failure modes, write a judge prompt for Pass/Fail assessment, split labeled data for judge development, measure judge alignment (TPR/TNR), estimate true success rates with bias correction, or set up CI evaluation pipelines. Also trigger when the user mentions "judge prompt", "automated eval", "LLM evaluator", "grading prompt", "alignment metrics", "true positive rate", or wants to move from manual trace review to automated evaluation. This skill covers the full lifecycle: prompt design → data splitting → iterative refinement → success rate estimation.
Evaluate every produced output (code, report, plan, data, API response) against type-specific quality criteria, score 1-10, make accept/reject decisions, and provide actionable improvement suggestions. Triggers on "evaluate", "check", "review", "quality control", "is this good enough", "score it", or before passing output to the next step in an agentic workflow.
Evaluate and improve user experience of interfaces (CLI, web, mobile)
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.
Analyze A/B test results with statistical significance, sample size validation, confidence intervals, and ship/extend/stop recommendations. Use when evaluating experiment results, checking if a test reached significance, interpreting split test data, or deciding whether to ship a variant.
Perform a PESTLE analysis covering Political, Economic, Social, Technological, Legal, and Environmental factors. Use when assessing the macro environment, doing strategic planning, or evaluating external factors affecting your business.
Connect AI agents to your live Chrome session via CDP for real-time tab interaction, screenshots, and JS evaluation without re-login
Evaluates interfaces, components, screens, and flows against universal UX/UI principles (heuristics, UX laws, Gestalt, cognitive psychology, accessibility) and delivers concrete, prioritized improvements. Use whenever the user shares UI code, screenshots, components, or mockups and wants feedback — even if they don't use the words "critique" or "review". Also trigger when the user asks "what's wrong with this UI", "how can I improve this", "review my component", "does this look right", "give me feedback on this design", or shares any interface and asks for thoughts. Trigger for partial slices too (a single button, form, or card) — not only full screens.
Content quality and E-E-A-T assessment for AI citability — evaluate experience, expertise, authoritativeness, trustworthiness, and content structure
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".
Use after the final approved execution scope is complete, or when the user asks whether a feature is done, ready to ship, safe to merge, or needs a quality check. Runs the post-execution quality gate: specialist review, artifact verification, and human UAT against locked decisions and the final exit state. Use for prompts like "review this feature", "is this done?", "can we ship this?", "double-check the implementation", or "run UAT".