This skill should be used when the user asks to "check accessibility", "audit WCAG compliance", "scan HTML for a11y issues", "check color contrast", or "find accessibility violations in web pages".
日本語の概要は準備中です。原文の説明を表示しています。
Guide red-green-refactor TDD with test generation, coverage-gap analysis, and multi- framework support. Use when writing tests first, analyzing coverage, generating test stubs, or converting tests between Jest, Pytest, JUnit, and Vitest.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
The agent guides red-green-refactor TDD workflows, generates framework-specific test stubs from requirements, parses coverage reports to identify prioritized gaps, and calculates test quality metrics including smell detection and assertion density. Supports Jest, Pytest, JUnit, Vitest, and Mocha.
Before generating tests or analyzing coverage, confirm these inputs. If any is unknown or vague, ASK — do not assume:
Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.
# Generate test cases from requirements (Python API)
from test_generator import TestGenerator, TestFramework
gen = TestGenerator(framework=TestFramework.PYTEST, language="python")
cases = gen.generate_from_requirements(requirements)
# Analyze coverage gaps from LCOV report
from coverage_analyzer import CoverageAnalyzer
analyzer = CoverageAnalyzer()
analyzer.parse_coverage_report(content, "lcov")
gaps = analyzer.identify_gaps(threshold=80.0)
# Guide TDD cycle
from tdd_workflow import TDDWorkflow
wf = TDDWorkflow()
wf.start_cycle("User can reset password via email")
Load the reference that matches the task — keep this file lean and pull detail on demand:
This skill covers:
This skill does NOT cover:
senior-qa for E2E patterns and senior-devops for load testingsenior-security and senior-secops skills| Skill | Integration | Data Flow |
|---|---|---|
senior-qa | Generated test stubs feed into QA review workflows; QA coverage standards inform threshold settings | test_generator.py output → QA review → approved test suite |
code-reviewer | Metrics calculator output provides quantitative data for code review checklists | metrics_calculator.py quality report → code review scoring |
senior-fullstack | Scaffolded projects include test infrastructure; TDD guide generates tests for scaffolded modules | project_scaffolder.py output → test_generator.py input |
senior-devops | Coverage reports from CI pipelines are parsed by coverage analyzer; recommendations feed back into pipeline gates | CI coverage artifact → coverage_analyzer.py → pass/fail gate |
senior-security | Edge-case fixtures for auth and API scenarios complement security-focused test plans | fixture_generator.py auth/API edge cases → security test plan |
tech-stack-evaluator | Framework detection informs stack evaluation; test quality metrics feed into technology assessment | format_detector.py analysis → stack evaluation input |
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
This skill should be used when the user asks to "check accessibility", "audit WCAG compliance", "scan HTML for a11y issues", "check color contrast", or "find accessibility violations in web pages".
日本語の概要は準備中です。原文の説明を表示しています。
Design and analyze A/B tests: sample size, test duration, and statistical significance for conversion experiments. Use when setting up an A/B test, calculating sample size, designing an experiment, or analyzing results.
日本語の概要は準備中です。原文の説明を表示しています。
Design and run statistically rigorous A/B tests and experiments. Use when planning experiments, calculating sample sizes, designing test variants, selecting metrics, analyzing results, or when someone says "let's test that."
日本語の概要は準備中です。原文の説明を表示しています。
Sales execution across pipeline, discovery, demos, negotiation, and closing. Use when qualifying opportunities, running MEDDIC discovery, building account plans, handling objections, structuring proposals, or forecasting pipeline.
日本語の概要は準備中です。原文の説明を表示しています。
Design ad creative across Google, Meta, LinkedIn, Twitter/X, and TikTok with platform format specs, headline formulas, and A/B testing. Use when writing ad copy, generating headline variations, creating ad sets, or validating creative.
日本語の概要は準備中です。原文の説明を表示しています。
Answer Engine Optimization (AEO): optimize content to be cited by LLMs (ChatGPT, Claude, Perplexity, Gemini) in their answers. Use when designing content for LLM citation, auditing citability, or structuring Q&A schema.
日本語の概要は準備中です。原文の説明を表示しています。