Results for “test”

`analysis-agent`/`task-agent`/`review-agent`: use when fixtures, factories, seeds, isolation, cleanup, or sensitive test-data rules change; skip when test data is unaffected.

Reproduce a bug as a failing regression test, fix until green, review, and commit via a delegated pipeline.

Prevents AI-introduced regressions with sandbox-mode API testing, automated bug-check workflows, and patterns that catch blind spots where the same model writes and reviews code.

Prepare and classify local test commands before asking approval to execute them.

Analyzes test suites to measure assertion diversity, detect shallow or trivial assertions, and identify tests that lack meaningful verification.

Scaffolds eval.yaml test files for agent skills in the dotnet/skills repository, including scenario writing, assertion definition, and rubric creation.

Write robust, maintainable end-to-end tests with Playwright using Page Object Model, proper selectors, auto-waiting, and debugging workflows.

This skill should be used when the user wants to implement features or fix bugs using test-driven development. Enforces the RED-GREEN-REFACTOR cycle with vertical slicing, context isolation between test writing and implementation, human checkpoints, and auto-test feedback loops. Uses multi-agent orchestration with the Task tool for architecturally enforced context isolation. Supports Jest, Vitest, pytest, Go test, cargo test, PHPUnit, and RSpec.

Guides statistical hypothesis testing with assumption checks, effect sizes, power analysis, Bayesian alternatives, and APA-formatted reporting for research data.

Structured guide for setting up A/B tests with mandatory gates for hypothesis, metrics, and execution readiness.

Orchestrates a multi-agent build session, acting as manager to cut goals into units, spawn implementer and tester subagents, and run test-and-review loops with anti-thrash guardrails.

Automate Classmarker quiz and test management tasks through Composio's toolkit via Rube MCP.

This skill should be used when the user asks to "set up a web server", "configure HTTP or HTTPS", "perform SNMP enumeration", "configure SMB shares", "test network services", or needs guidance on configuring and testing network services for penetration testing labs.

Runs a complete quality engineering audit on any codebase, deriving behavioral requirements, generating spec-traced tests, performing multi-pass code review, and producing a consolidated bug report with verified patches.

Guides the creation, iterative improvement, and evaluation of agent skills, including drafting, testing, benchmarking, and optimizing descriptions.

Model any API with Swamp, test it, and enrich *Claw with new capabilities — full lifecycle from idea to working.

Tests and debugs web applications in real browsers using Chrome DevTools MCP, enabling DOM inspection, console error capture, network analysis, performance profiling, and visual verification with live runtime data.

Tests API endpoints for mass assignment vulnerabilities by injecting privileged fields (role, isAdmin, balance) into request bodies and verifying if the server binds them without filtering.

Coordinates blockchain operations on the Aptos testnet via MCP tools, including balance checks, transfers, swaps, staking, and approvals.

Computes the Anderson-Darling test statistic and p-value using scipy.stats.anderson for evaluating predictions against ground truth.

Operate the Unity Editor through MCP tools and resources, covering scene management, script editing, testing, and automation workflows.

Use ONLY for code review and test write/review guidance in `packages/agent-core-v2` (the DI × Scope agent engine). Does NOT apply to the legacy `packages/agent-core` or to any other package — for those, do not load this skill. Groups the review and testing lenses used for agent-core-v2 — `slop` (single-level-of-abstraction / layered error-handling review, invoked only on explicit request) and `test` (contract-driven per-test rules for both authoring and reviewing tests). Apply the sub-skill that matches the task; do not apply `slop` unprompted.

Econometrics skill for time series analysis. Activates when the user asks about: "time series", "stationarity", "unit root test", "ADF test", "KPSS test", "ARIMA", "ARMA", "autocorrelation", "ACF", "PACF", "VAR model", "VECM", "Granger causality", "cointegration", "impulse response function", "forecast", "seasonal decomposition", "ARCH", "GARCH", "时间序列", "平稳性检验", "单位根", "自回归", "格兰杰因果", "协整", "脉冲响应", "预测", "向量自回归"

Econometrics skill for time series analysis. Activates when the user asks about: "time series", "stationarity", "unit root test", "ADF test", "KPSS test", "ARIMA", "ARMA", "autocorrelation", "ACF", "PACF", "VAR model", "VECM", "Granger causality", "cointegration", "impulse response function", "forecast", "seasonal decomposition", "ARCH", "GARCH", "时间序列", "平稳性检验", "单位根", "自回归", "格兰杰因果", "协整", "脉冲响应", "预测", "向量自回归"

Run config-driven performance benchmarks against a deployed NVIDIA RAG Blueprint server, including profiling and load testing, with a unified report.

Build evaluation frameworks for agent systems, covering rubric design, test set creation, and automated evaluation pipelines.

Mines git history to find files and test suites that historically change together with specified target files (co-change relations). Use before making changes or running tests to discover coupled code.

Classifies data as pseudonymised or anonymised using Recital 26 reasonably likely test, Breyer ruling C-582/14, motivated intruder test, and WP29 Opinion 05/2014 on anonymisation techniques. Covers singling out, linkability, and inference tests. Keywords: pseudonymisation, anonymisation, Recital 26, re-identification, k-anonymity, differential privacy, WP29 Opinion 05/2014.

Guides teams in adapting engineering processes, architecture, code review, and testing for high-volume AI-assisted code generation.

Provides expert guidance on modern Julia 1.10+ development, covering performance optimization, multiple dispatch, tooling, testing, and production-ready practices.

Analiza series temporales: tendencia, estacionalidad y pronóstico con Prophet, statsmodels y ML, incluyendo descomposición, tests de estacionariedad y evaluación contra baselines.

Performs design operations in the big-data domain, including hypothesis testing, statistical analysis, and data visualization using ML frameworks.

Evaluate RAG pipelines with Ragas — measure faithfulness, answer relevancy, context precision/recall, and noise sensitivity using LLM-as-judge metrics; run automated test suite generation with TestsetGenerator; integrate with LangChain, LlamaIndex, and CI pipelines.

Test-Driven Development workflow principles. RED-GREEN-REFACTOR cycle.

Automates hypothesis generation and testing on tabular datasets using LLMs, combining data-driven discovery with literature integration for scientific research.

Build evaluation frameworks for agent systems with deterministic checks, regression suites, multi-dimensional rubrics, quality gates, production monitoring, and outcome measurement.