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概要と使いどころ
This skill should be used when developing or modifying styles for TDesign React components. It provides guidance on BEM naming conventions, CSS Variables usage, state classes, and the relationship between component code and common submodule styles.
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。
Run conversion rate optimization through hypothesis-driven testing including audit, hypothesis generation, test design, statistical analysis, and rollout decisions. Use this skill whenever the user wants to optimize conversion, run A/B tests, audit a funnel, generate test hypotheses, design experiments, or analyze test results. Triggers on conversion optimization, CRO, A/B test, split test, multivariate test, hypothesis, conversion funnel, funnel audit, experiment design, statistical significance, lift, optimization. Also triggers when the user has a conversion problem and isn't sure where to start, or when test results are ambiguous and need interpretation.
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。
Review web application code for bugs, security issues, performance problems, and stack-specific anti-patterns. Use this skill whenever the user wants to review code, debug a production issue, investigate a build failure, audit security, or check a PR before merging. Triggers on code review, review my code, debug, build error, broken, not working, why is X failing, check this code, security check, PR review, audit code, refactor. Also triggers when investigating 4xx or 5xx errors, deploy failures, environment variable issues, and CMS integration problems.
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。
How to read experiment results without fooling yourself. Confidence intervals, p-values, multiple testing, sequential testing, CUPED, heterogeneous treatment effects, ratio metrics, network effects, dashboard reconciliation, and the interpretation failures that produce confidently wrong shipping decisions. Use this skill whenever the user is reading a finished experiment result panel and about to make a ship, kill, or iterate decision, or when an experiment number does not match the dashboard number. Triggers on read experiment results, result panel, ship or kill decision, p-value, confidence interval, statistical significance, multiple testing, peeking, sequential testing, CUPED, variance reduction, heterogeneous treatment effects, ratio metric, network effects, inconclusive test, experiment versus dashboard mismatch. Use `experiment-design` instead when the test has not run yet and the question is hypothesis, sample size, duration, or what to test.
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。
Expert WCAG (Web Content Accessibility Guidelines) advisor covering WCAG 2.0, 2.1, and 2.2 — the W3C international accessibility standards. Use this skill whenever a user asks about WCAG success criteria, conformance levels (A/AA/AAA), accessibility audits, POUR principles, accessibility statements, ARIA patterns, colour contrast, keyboard accessibility, screen reader compatibility, mobile accessibility, cognitive accessibility, WCAG 2.2 new criteria, WCAG 3.0 preview, legal requirements referencing WCAG (EN 301 549, EAA, ADA, Section 508), or mapping WCAG to accessibility laws. Trigger for any web or digital content accessibility question — even if the user doesn't say "skill" or "WCAG".
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。
Install an improved variant of one named skill: resolve it by local path, repo, or name, run skill-creator's retrofit on a throwaway copy, then install the improved result. Don't use for improving in place, upstream PRs, or plain asm install.
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。
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, update 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.
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。
DNA sequence profiling and genetic variant characterization
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。
Query ClinVar for genomic variant clinical significance data
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。
Query gnomAD for population-level genomic variant frequencies
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。
Pre-landing PR review with two-pass checklist. Pass 1 critical: SQL safety, race conditions, LLM trust boundaries, enum completeness. Pass 2 informational: side effects, magic numbers, dead code, test gaps. Adversarial review auto-scaled by diff size. Scope drift detection.
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。
Multi-AI second opinion via OpenAI Codex CLI. Three modes: Review (pass/fail gate), Challenge (adversarial edge-case finder), Consult (freeform with session continuity). Read-only with streaming output and thinking traces. Cross-model analysis when review has already run.
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。
Fully automated ship workflow: pre-flight, merge base, run tests, coverage audit, pre-landing review, design review, adversarial review, version bump, CHANGELOG generation, TODOS.md update, bisectable commits, push and PR creation, auto-sync docs via document-release.
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。
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。
Performs financial ratio analysis, DCF valuation, budget variance analysis, and rolling forecast construction for strategic decision-making
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。
Adversarial verification for AI-generated legal content. Use when fact-checking legal documents, validating citations, detecting hallucinations, scoring document quality, or assessing distribution readiness.
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。
Cross-functional what-if modeling for compound adversity -- shows how one problem cascades into the next. Use when facing complex risk scenarios, strategic decisions with major downside, or multi-variable threats.
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。
This skill should be used when the user asks to "scan AI systems for security threats", "check for prompt injection vulnerabilities", "assess model security posture", "detect data poisoning risks", or "audit AI/ML pipeline security".
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。
Adversarial thinking partner that stress-tests plans, prepares board meetings, and surfaces blind spots. Use when you need plan validation, board prep, hard-decision frameworks, or pre/post-mortem failure analysis.
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。
Build workflows with AWS Step Functions state machines using the JSONata query language. Covers Amazon States Language (ASL) structure, state types, variables, data transformation, error handling, AWS service integration, and migrating from the JSONPath to the JSONata query language.
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 for running acceptance tests for a Terraform provider. Use this when asked to run an acceptance test or to run a test with the prefix `TestAcc`, when a test fails with missing environment variables, or when diagnosing a failing or suspiciously passing acceptance test.
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。
Pretest 2-6 message variants on CRM-grounded synthetic panels. "which of these headlines will win"
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。
A/B test content variants by eval score and declare a winner with confidence. "compare two versions of this copy"
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。