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Write C# unit tests with NUnit using clear naming, focused assertions, and independent test cases. Covers project setup, testing with multiple input values, exception checks, and replacing dependencies with mocks to test components in isolation.
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Improve LLM prompts using Arize production traces, evaluation results, and human feedback. Extract current instructions, identify recurring failures, revise the prompt, and compare experiments to measure changes in output quality and catch regressions.
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Turn user data and interviews into research-backed personas, or representative user profiles. The toolkit also supports mapping user journeys, planning usability tests, and combining research findings to guide and validate design decisions.
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Analyze Google Analytics traffic, visitor engagement, and conversions, such as purchases or goal completions. Compare periods, identify strong and weak pages or traffic sources, and receive prioritized recommendations with implementation guidance.
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Analyze sprint history to forecast delivery ranges, score team health, and track retrospective actions. Python tools turn structured sprint data into reports that support planning, highlight recurring problems, and show whether improvement efforts are progressing.
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Turn interviews, surveys, and usage data into evidence-based user profiles, experience maps, and design recommendations. Plan usability tests, define success measures, and organize research findings to identify user problems and prioritize improvements.
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Plan and analyze A/B tests, prepare tabular data, and train and evaluate prediction models. Estimate intervention effects from observational data, track model experiments with MLflow, and translate statistical findings into business decisions.
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Plan and audit large sets of pages generated from structured data. Assess source quality, template uniqueness, URLs, and internal links, then identify thin content and unnecessary search indexing with prioritized recommendations and staged publishing guidance.
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Creates figures for machine learning papers from draft text or experiment data. Extracts components for Gemini-generated diagrams, chooses suitable charts with matplotlib or seaborn, and adjusts colors, fonts, and export formats for publication.
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Build dashboard panels that manage both display and data fetching. A shared TypeScript base provides loading states, retry controls, caching, automatic refresh, and a circuit breaker that temporarily pauses requests after repeated failures.
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Build dashboard panels in plain TypeScript with data fetching and loading states. The pattern uses cached data, fallback values, and a circuit breaker, which temporarily pauses requests after repeated failures, to handle API problems.
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Add thumbnails, colored badges, and data-driven styling to TypeScript lists, cards, and panels. Coordinate component logic with CSS, include fallbacks for missing data, and check how the updated display handles empty lists and long text.
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Full CRUD and analysis for Mixpanel dashboards. Use when the user asks to build, create, analyze, read, understand, explain, modify, update, enhance, or manage dashboards, or asks about dashboard layout, text cards, or report arrangement. Covers dashboard analysis (read + understand existing), creation (new builds), modification (update existing), and explanation (data-driven annotation).
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Turn any ordered process into a data-driven vertical or horizontal scroll story with a base line, progress fill, active step states, responsive collapse, semantic fallback, and reduced-motion behavior. Use for onboarding, checkout, roadmaps, recipes, case studies, service processes, histories, or narratives where progress through the sequence should become visible while scrolling.
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Build or refactor reusable, data-driven enemy archetype and moveset systems for Three.js action games. Use for enemy content schemas, model and rig conventions, combat move timing and contact contracts, runtime state boundaries, placeholder fallbacks, deterministic fixtures, or production playthrough validation.
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Generate or scaffold ASP.NET Core code — Razor Pages, Blazor components, MVC controllers, views, and Minimal API endpoints — without using ASP.NET Core CLI scaffolding/code-generation tools. Use when (1) adding CRUD pages, views, or API endpoints backed by Entity Framework (EF Core) and a database, (2) generating code to create, read, update, and delete data using a DbContext, (3) scaffolding UI components that match the project's existing CSS framework and coding patterns, or (4) creating data-driven forms, tables, and navigation for a model class. Do not use for non-ASP.NET projects or when CLI-based scaffolding is preferred.
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Detects duplicate boilerplate, copy-paste tests, and structural maintainability issues across .NET test suites. Use when the user asks to reduce repetition, consolidate similar test methods, convert copy-paste tests to data-driven parameterized tests, suggest a better test structure, or identify refactoring opportunities. Identifies repeated construction, assertion patterns, copy-paste methods convertible to DataRow/Theory/TestCase, redundant setup/teardown, and shared infrastructure. Produces an analysis report with concrete before/after suggestions. Works with MSTest, xUnit, NUnit, and TUnit. DO NOT USE FOR: writing new tests (use writing-mstest-tests), reviewing test quality or anti-patterns (use test-anti-patterns), or deep mock auditing (use exp-mock-usage-analysis).
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Runs your conversion data through different attribution models side by side — last click, first click, linear, time decay, position based, and data-driven. Shows you how credit shifts between campaigns depending on the model so you can make better budget decisions instead of over-investing in last-touch campaigns. Platform: Google and Meta.
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Analyze an organization's sub-transaction patterns in TRES Finance, identify groups of repetitive transactions, and propose data-driven rollup rules that consolidate them into one aggregated entry per interval (daily or monthly) for cleaner ERP integration. Also lists and deletes rollup rules. Trigger this skill whenever the user asks about rollup rules, transaction aggregation, consolidating transactions, reducing transaction noise, rolling up gas fees, rolling up staking rewards, rolling up micro-transactions, or collapsing many small ledger entries into fewer summary entries. Also trigger on "create a rollup rule", "suggest rollup rules", "what should I roll up", "show my rollup rules", "delete a rollup rule", "aggregate transactions", "too many transactions", "reduce transaction count", "roll up fees", "daily rollup", "monthly rollup". If a wallet has too many transactions, high-frequency activity, or noisy ledger entries, proactively suggest rollup rules. For computing the EXACT impact of a single already-specified rule or reviewing submitted/pending rules, use the tres-rollup-review skill instead.
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Generates publication-quality figures for ML papers from research context. Given a paper section or description, extracts system components and relationships to generate architecture diagrams via Gemini. Given experiment results or data, auto-selects chart type and generates data-driven figures via matplotlib/seaborn. Use when creating any figure for a conference paper.
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UX research and design toolkit for Senior UX Designer/Researcher including data-driven persona generation, journey mapping, usability testing frameworks, and research synthesis. Use for user research, persona creation, journey mapping, and design validation.
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World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics. Expertise in Python (NumPy, Pandas, Scikit-learn), R, SQL, statistical methods, A/B testing, time series, and business intelligence. Includes experiment design, feature engineering, model evaluation, and stakeholder communication. Use when designing experiments, building predictive models, performing causal analysis, or driving data-driven decisions.
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Clean, high-contrast enterprise design for data-driven workflows with intuitive drag-and-drop patterns and structured layouts.
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Manual and automated gating for defining cell populations in flow cytometry. Covers rectangular, polygon, and data-driven gates. Use when identifying cell populations through hierarchical gating strategies.
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