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Found 1,948 Skills
Iterative testing, verification, and improvement supervisor. Triggers when: User requests iterative testing and improvement, code quality review and assurance is needed, automated testing and feedback loops are required, or multiple rounds of refinement are specified. Commands: - /iterate <n> - Run n iterations of test-improve cycle - /iterate stop - Stop current iteration loop - /iterate status - Show current iteration status - /iterate report - Generate iteration report Capabilities: Automated test execution and result analysis, quality metrics tracking across iterations, improvement suggestion generation, convergence detection, and detailed iteration reports.
OpenResponses API compliance testing. Use when testing the Response API endpoint, running compliance tests, or debugging Response API schema issues. Triggers on 'compliance', 'response api test', 'openresponses test'.
Use when the user needs ML pipelines, statistical analysis, data preprocessing, feature engineering, model selection, experiment tracking, or data visualization. Triggers: dataset exploration, model training, feature engineering, hyperparameter tuning, experiment tracking setup, statistical hypothesis testing, visualization creation.
Patrones de integración de Prisma ORM con NestJS para aplicaciones de producción. Usar PROACTIVAMENTE cuando se trabaje con esquemas Prisma, migraciones, PrismaService, transacciones atómicas (incluyendo SELECT FOR UPDATE), repositorios como adaptadores de infraestructura en Clean Architecture, queries optimizadas (N+1, includes, paginación), seeds y testing con Prisma Mock. Activar siempre que aparezcan las palabras clave: Prisma, PrismaService, prisma.$transaction, prisma migrate, schema.prisma, PrismaClient, Prisma.validator, include/select, upsert, createMany, o cualquier operación de base de datos con Prisma en un proyecto NestJS.
Guide for adding new AI function examples, for testing specific features against the actual provider APIs.
HackerOne bug bounty automation - parses scope CSVs, deploys parallel pentesting agents for each asset, validates PoCs, and generates platform-ready submission reports. Use when testing HackerOne programs or preparing professional vulnerability submissions.
Reads documented bugs from bugs.md, analyzes root causes, implements fixes with regression tests, and validates the full test suite. Prioritizes fixes by severity (high to low). Updates bugs.md with correction status and generates a final bugfix report. Use when the user asks to fix bugs, resolve issues, or run the bugfix workflow for a feature. Do not use for new feature implementation, code review, or QA testing.
Comprehensive Rust coding guidelines covering ownership, error handling, async patterns, traits, testing, performance, clippy, and documentation. Use when writing new Rust code, reviewing or refactoring existing Rust, implementing async systems with Tokio, designing error hierarchies, choosing between borrowing and cloning, setting up tests or benchmarks, configuring linting, or optimizing performance. Do not use for non-Rust languages or general software architecture unrelated to Rust idioms.
Systematic web application QA testing with structured issue taxonomy, health scoring, and regression tracking. Use this skill when the user asks for QA testing, systematic testing, smoke testing, regression testing, web app testing, browser testing, or says "QA this", "test the app", "smoke test", "run QA", "systematic test", "check the site", "regression test", "full QA", "/qa-systematic". Supports full, quick, and regression modes.
Conduct statistical hypothesis testing including null/alternative hypothesis formulation, p-values, Type I/II errors, and test statistic selection. Use this skill when the user needs to determine whether a result is statistically significant, choose the right statistical test, interpret p-values correctly, or evaluate research findings — even if they say 'is this result significant', 'which statistical test should I use', or 'what does this p-value mean'.
Complete toolkit for Huawei Ascend NPU model conversion and end-to-end inference adaptation. Workflow 1 auto-discovers input shapes and parameters from user source code. Workflow 2 exports PyTorch models to ONNX. Workflow 3 converts ONNX to .om via ATC with multi-CANN version support. Workflow 4 adapts the user's full inference pipeline (preprocessing + model + postprocessing) to run end-to-end on NPU. Workflow 5 verifies precision between ONNX and OM outputs. Workflow 6 generates a reproducible README. Supports any standard PyTorch/ONNX model. Use when converting, testing, or deploying models on Ascend AI processors.
Use this skill when working with coordinates, vectors, matrices, shapes, hit testing, or layout rectangles in PixiJS v8. Covers Point/ObservablePoint, Matrix (2D affine, decompose, apply, applyInverse), shapes (Rectangle, Circle, Ellipse, Polygon, RoundedRectangle, Triangle), Rectangle layout helpers (pad, fit, enlarge, ceil, scale, getBounds), strokeContains hit tests, Polygon isClockwise/containsPolygon, toGlobal/toLocal, PointData/PointLike/Size types, DEG_TO_RAD, and pixi.js/math-extras vector and intersection helpers. Triggers on: Point, ObservablePoint, Matrix, Rectangle, Circle, Polygon, Triangle, RoundedRectangle, toGlobal, toLocal, hitArea, strokeContains, pad, fit, enlarge, ceil, getBounds, containsRect, intersects, isClockwise, math-extras, lineIntersection, segmentIntersection, DEG_TO_RAD, PointData.