Results for “test”

Manage, inventory, standardize, migrate, and test reusable Codex skills for the MetaSkill repository. Use when Codex needs to inspect local skills, convert a personal workflow into a public skill candidate, normalize skill folder shape, plan skill promotion, or decide whether a skill belongs in skill-management, publication-review, or skill-examples.

Run a pre-mortem risk analysis on a PRD or launch plan. Categorizes risks as Tigers (real problems), Paper Tigers (overblown concerns), and Elephants (unspoken worries), then classifies as launch-blocking, fast-follow, or track. Use when preparing for launch, stress-testing a product plan, or identifying what could go wrong.

Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

Generate, verify, refine, and accelerate C/C++ or CUDA code from MATLAB with MATLAB Coder, Embedded Coder, GPU Coder, or MATLAB Test. Also covers writing codegen-ready MATLAB code: language constraints, coder.* directives, and optimization patterns. Triggers on: codegen, MEX, deploy MATLAB as C/C++, GPU Coder, coder.screener, coder.config, coder.gpuConfig, coder.typeof, coder.runTest, matlabtest.coder.TestCase, SIL, embedded config, no dynamic memory, EnableMexProfiling, coder.timeit, coder.perfCompare, %#codegen, writing codegen-ready MATLAB, code generation readiness, coder.varsize, coder.unroll, coder.noImplicitExpansionInFunction, coder.ceval, coder.inline, coder.extrinsic, coder.const, coder.classSignature, class codegen limitations, temporal types codegen, DMA-off, stack-only, host-target InstructionSetExtensions, SIMDAcceleration, OptimizeReductions, host SIMD tuning, host OpenMP, codegen performance.

Causal inference framework for answering "does X cause Y?" beyond correlation. DoWhy (Microsoft Research) provides the identify-estimate-refute loop: define a causal graph (DAG), identify the causal effect using backdoor/frontdoor/instrumental variable criteria, estimate treatment effects with multiple estimators, and validate results with automated refutation tests. Use when: distinguishing causation from correlation, estimating treatment effects (ATE, ATT, CATE), designing and analyzing A/B tests with confounders, using instrumental variables, performing counterfactual reasoning ("what would have happened if..."), validating causal claims with sensitivity analysis, working with observational data where randomization is impossible, or any analysis where the question is "what is the CAUSAL effect of X on Y" rather than just "how do X and Y relate?"

Runs a pre-deployment security audit of Solidity smart contracts in a Foundry project, combining static analysis (Slither, Aderyn), symbolic execution (Mythril), and property-based testing to catch reentrancy, access-control, and arithmetic bugs before deploying to an EVM chain.

Integrates Static Application Security Testing (SAST) tools—CodeQL and Semgrep—into GitHub Actions CI/CD pipelines, configuring automated code scanning, tuning rules, uploading SARIF results, and establishing quality gates that block merges on high-severity vulnerabilities.

Finds and safely removes dead code across a codebase: unreachable paths, unused exports, functions, variables, CSS selectors, dependencies, env vars, and commented-out blocks. Builds an import graph, verifies no dynamic references, and runs build and tests after cleanup.

Use for writing, reviewing, debugging, or testing bounded retry policies in Python with Tenacity, including retry predicates, stop and wait strategies, jitter, exception propagation, callbacks, Retrying, and AsyncRetrying. Do not use for generic loops, scheduled jobs, domain polling without a retryable operation, or operations whose side effects are not safe to repeat.

Resolve GitHub PR feedback end-to-end for the branch you are on. Use when you need to fetch all comments on the branch PR, evaluate whether each comment still applies, decide whether it should be addressed, implement fixes, run compile/tests with retry-on-failure, then commit and push the result.

Evaluates likelihood of primary aldosteronism by measuring plasma aldosterone suppression after oral captopril; normal suppression ≥30% makes PA unlikely, while lack of suppression with persistently suppressed plasma renin activity suggests PA. Use when assessing captopril challenge test (CCT) results for PA diagnosis in patients with positive aldosterone-to-renin ratio.

Suggests a 3- to 6‑month trial of testosterone dosing to achieve a mid‑normal premenopausal value in a reference assay for postmenopausal women with properly diagnosed HSDD and no contraindications. Triggered when a postmenopausal woman requests therapy for low sexual desire after appropriate HSDD workup and exclusion of contraindications.

Recommends measurement of plasma ACTH to establish primary adrenal insufficiency (PAI) diagnosis in patients with confirmed cortisol deficiency; a plasma ACTH concentration ≥2-fold the upper limit of the reference range supports PAI. Use when evaluating plasma ACTH in a patient with low morning cortisol or abnormal corticotropin stimulation test.

Auditoria de clareza textual do requirements. Verifica se a prosa é boa o bastante para gerar plano sem ambiguidade. NÃO mistura com auditoria de testes de implementação. Use quando o usuário digitar "/reversa-quality", "reversa-quality" ou pedir para revisar a qualidade do requirements antes de planejar. Etapa opcional do ciclo forward.

You are an expert in Hapi.js, the configuration-centric enterprise framework for Node.js. You help developers build production APIs with built-in input validation (Joi), authentication strategies, plugin architecture, caching, rate limiting, and comprehensive request lifecycle hooks — designed for teams that need structure, security, and testability without third-party middleware sprawl.

Comprehensive results analysis for empirical research: generate publication-quality descriptive statistics and balance tables, interpret regression coefficients with economic magnitude and effect sizes, assess identification assumption diagnostics, and produce structured results memos. Use when asked to create summary statistics, Table 1, balance tests, interpret results, assess economic significance, or write results narratives.

Builds a feature specification from scratch with plan-a-feature and publishes it to a user-specified Confluence location, posting the spec as a parent page and each companion artifact (decision log, team findings, technical notes) as a child page beneath it. Use when the user wants a new feature planned, designed, scoped, or specified AND posted to a Confluence space or page. Requires a configured Atlassian MCP server. Does not plan to local files only — use plan-a-feature. Does not publish an arbitrary existing markdown file — use markdown-to-confluence. Does not refine or stress-test an existing plan — use iterative-plan-review. Does not document already-built features to Confluence — use project-documentation-to-confluence.

Use when scoping a project and before any writing, to stress-test whether a study clears The Lancet's bar — clinically or public-health important, globally relevant, and likely to change practice or policy, often with an equity lens. Decides The Lancet vs a Lancet family title vs another general medical journal.

Structured build workflow: branch, delegate to coding agent, test, screenshot, and commit. Triggers on: "$devprocess", "build this properly", "use the coding agent workflow", or any feature/refactor/infrastructure change that is too large for a direct edit. NOT for: one-line fixes, doc edits, prompt tweaks, or simple config changes — do those directly.

Use for writing, reviewing, debugging, testing, or analyzing Python SimPy discrete-event simulations. Trigger on Environment, Event, Process, timeout, Resource, PriorityResource, PreemptiveResource, Container, Store, queues, interrupts, simulation clocks, replications, or SimPy monitoring. Do not use for asyncio services, wall-clock schedulers, continuous ODE solvers, or Monte Carlo code without an event-process model.

Extract coverage-checked, independently testable MoonSpec user stories from a technical or declarative design and write breakdown output under artifacts/story-breakdowns (gitignored). Use when the user asks to run or reproduce `/moonspec.breakdown`, split a broad design into one-story candidates, preserve source coverage, or build a coverage matrix before `/moonspec.specify`.

Searches and extracts data from Burp Suite project files (.burp) using the burpsuite-project-file-parser extension, enabling regex searches on response headers and bodies, extraction of security audit findings, and analysis of proxy history and site map data.

Use for writing, reviewing, debugging, or testing Python terminal presentation built with Rich, including Console streams, Text and markup safety, tables, trees, panels, progress, status, Live displays, render protocols, and deterministic output capture. Do not use for CLI argument parsing, structured logging design, browser UI, or machine-readable protocol output that must remain plain.

Use for writing, reviewing, debugging, testing, or optimizing Python CVXPY optimization models. Trigger on Variable, Parameter, Expression, Constraint, Objective, Problem, DCP, DPP, DGP, DQCP, solver selection/status, dual values, mixed-integer, cone, or repeated parametric solves. Do not use for scipy.optimize-only, PyMC inference, symbolic algebra without optimization, or hand-written solver implementations.

Performance regression detection using the browse daemon. Establishes baselines for page load times, Core Web Vitals, and resource sizes. Compares before/after on every PR. Tracks performance trends over time. Use when: "performance", "benchmark", "page speed", "lighthouse", "web vitals", "bundle size", "load time". (gstack) Voice triggers (speech-to-text aliases): "speed test", "check performance".

Battle-tested PyTorch training recipes for all domains — LLMs, vision, diffusion, medical imaging, protein/drug discovery, spatial omics, genomics. Covers training loops, optimizer selection (AdamW, Muon), LR scheduling, mixed precision, debugging, and systematic experimentation. Use when training or fine-tuning neural networks, debugging loss spikes or OOM, choosing architectures, or optimizing GPU throughput.

Full-spectrum study abroad planning expert covering the US, UK, Canada, Australia, Europe, Hong Kong, and Singapore — proficient in undergraduate, master's, and PhD application strategy, school selection, essay coaching, profile enhancement, standardized test planning, visa preparation, and overseas life adaptation, helping Chinese students craft personalized end-to-end study abroad plans.

Threat-model and harden AI agents, RAG systems, assistants, and tool-using workflows against direct, indirect, stored, cross-agent, and multimodal prompt injection. Use when reviewing an agent architecture, isolating untrusted content, constraining tools and egress, protecting secrets, adding injection-focused tests, investigating a suspected injection incident, or documenting residual prompt-injection risk.

Use when a feature's screens and flows are drafted and the design phase needs a navigable wireframe — a multi-screen greyscale Astro prototype where buttons and links actually work, suitable for the design gate and moderated user testing. In the A-Team pipeline it is conducted by ateam-design and consumes design.md's

Mega-composite — full greenfield feature development from idea to live. Chains research-and-decide (validate the idea + capture decision) → scope-and-execute (analysis + plan + implement) → design-build (if UI involved) → fix-the-suite (if tests need work) → merge-confidently → ship-it → knowledge-loop. The single command for "build feature X end-to-end."

Use for writing, reviewing, debugging, testing, or optimizing Python SymPy symbolic mathematics. Trigger on Symbol, assumptions, Expr, Eq, solve/solveset, simplify, factor, expand, calculus, matrices, exact arithmetic, lambdify, code generation, or symbolic-to-numeric conversion. Do not use for NumPy-only arrays, mpmath-only arbitrary-precision numerics, CVXPY optimization models, or parsing untrusted mathematical text.

Use when writing, reviewing, debugging, or testing PowerShell `.ps1` scripts and advanced functions, including parameters, pipeline objects, streams, native commands, filesystem safety, PowerShell 7 portability, Pester, and PSScriptAnalyzer. Do not use merely to run an existing script, author Bash or batch files, package a module or DSC resource, or perform live system or tenant administration.

Run differential gene expression analysis on bulk RNA-seq count matrices with PyDESeq2, the Python port of DESeq2 — size-factor normalization, dispersion estimation, Wald tests, FDR (Benjamini-Hochberg) correction, and volcano/MA plots. Use when identifying differentially expressed genes between conditions from raw bulk RNA-seq counts. Part of the AlterLab Academic Skills suite.

C library development patterns covering API design, header organization, memory management for libraries, ABI stability, build system integration, documentation with Doxygen, testing frameworks, and packaging. Use when creating C libraries, designing public APIs, managing build systems (CMake, Make, Meson), or distributing C packages. Extends lang-c-dev with library-specific tooling and patterns.

Use when triaging a study before any writing — stress-tests whether it clears Cell's bar (a complete, mechanistic, hypothesis-driven story with multiple converging lines of evidence) or belongs at a sibling. Use this first to decide Cell vs Molecular Cell vs Cell Reports vs Cell Reports Medicine before investing in framing or figures.

Composite skill — design, scaffold, build, and verify a UI in one workflow. Chains ui-ux-pro-max or web-design-guidelines (audit/principles) → shadcn or tailwind-design-system (component scaffold) → impeccable or frontend-design (build) → webapp-testing (verify). Use when the task is "build this page", "design and implement X", or any new UI surface.