2,111 件 ・ 関連度順
概要と使いどころ
Methodology for auditing A/B testing of consent banners to ensure compliance with equal ease of acceptance and rejection. Covers CNIL enforcement patterns including the EUR 150M Google fine, dark pattern detection methodology, manipulative design identification, and regulatory-compliant experimentation boundaries.
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Comprehensive methodology for auditing website cookies and tracking technologies. Covers automated scanning, cookie categorization, lifecycle documentation, and compliance gap analysis referencing the Planet49 CJEU ruling (C-673/17).
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Implements CPRA Section 1798.135 opt-out preference signal handling, covering Global Privacy Control (GPC) technical detection, automated signal honoring, cross-device consistency, and the relationship between browser signals and explicit consumer choices. Activate for GPC, opt-out preference signal, CPRA 1798.135, browser privacy signal, Do Not Sell queries.
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Guides the combined DPIA and AI Act conformity assessment for AI systems processing personal data. Covers EDPB-EDPS Joint Opinion 5/2021, training data lawfulness under Art. 6 and Art. 9, Art. 22 automated decision-making, algorithmic bias detection, and NIST AI RMF MAP function. Keywords: AI privacy, DPIA, AI Act, algorithmic bias, automated decision-making, Art. 22, training data, NIST AI RMF.
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Assesses AI bias risks for GDPR Art. 9 special category data and AI Act Art. 10 data governance. Covers fairness metrics, bias detection methods, mitigation strategies, and documentation requirements for protected characteristics. Keywords: AI bias, special category, fairness metrics, discrimination, Art. 9, Art. 10.
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Use when generating or refactoring Spring Boot 4 JPA entities, repositories, queries, projections, persistence tests, entity relationships, embeddables, IDs, or Hibernate mappings. Covers Jakarta Persistence 3.2 imports, Hibernate 7 entity modeling, new-state detection, N+1 prevention, projections, keyset pagination, batch writes, and common agent mistakes.
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AI-powered E2E web testing — eyes and hands for AI coding tools. Declarative YAML scenarios, Playwright execution, visual matching (OpenCV + OCR), platform auto-detection (Flutter/React/Vue), learning DB. Install: npx skills add ksgisang/awt-skill --skill awt -g
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Generate project-specific AGENTS.md and companion rules by analyzing a codebase. Supports full, minimal, update, and dry-run modes with package-manager detection, monorepos, backups, managed blocks, confidence scoring, and command validation.
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Creates and maintains project documentation for features, systems, and components. Use when documenting how a feature, system, or component works — including writing, updating, or organizing docs. Does not scan or detect the project's technology stack — use project-discovery for repository analysis and config detection. Does not create architectural decision records — use architectural-decision-record for ADRs. Does not create or update coding standards — use coding-standard instead. Does not generate PR descriptions — use update-pr-description for that. Does not produce runbooks for operational scenarios — use runbook for that. Does not rewrite existing prose for readability — use edit-for-readability for that. Does not produce an ephemeral, understand-now overview of code or a PR — use code-overview for that.
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Use when a skill is brittle, drifting, repeatedly failing, or needs a Reliable Skill Contract. Trigger for phrases like skill lifeguard, reliable skill, self-maintaining skill, negative examples, verification checkpoints, drift signals, replay hooks, or failure log to skill patch. Audits or patches skills so high-value workflows include explicit forbidden behaviors, checkpoints, machine-checkable done conditions, replay or smoke hooks, and drift detection.
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Fix JavaScript regex failures when matching CJK (Chinese/Japanese/Korean) text using \b word boundaries. Use when: (1) Regex pattern with \b silently fails to match Japanese, Chinese, or Korean text, (2) Pattern works for Latin/ASCII text but not CJK, (3) hasDriverLicenceCue or similar text-detection function returns false for CJK input despite correct characters, (4) inferIssuerFromTitleText or pattern-matching functions fail on non-Latin scripts. Root cause: JavaScript \b only recognizes [a-zA-Z0-9_] as "word characters" — CJK characters are classified as \W (non-word), so \b before/after CJK always sees a non-word/non-word boundary and fails to match.
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Automatically repair C/C++ code violations detected by TLA+ model checking. Takes a program, TLA+ specification, and TLC counterexample trace as input, then generates minimal code modifications to eliminate the violation. Use when: (1) TLC model checker reports an invariant violation, deadlock, or temporal property failure, (2) You have a counterexample trace and need to fix the corresponding code, (3) You need to understand how a TLA+ violation maps to program-level bugs, (4) You want to validate repairs by re-running TLC. Supports safety properties (invariants), liveness properties (temporal logic), and deadlock detection.
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Instruments code to track the flow of untrusted or sensitive data at runtime, enabling detection of injection vulnerabilities, data leaks, and privilege violations. Use when users need to: (1) Track untrusted input propagation through code, (2) Detect SQL injection, XSS, or command injection vulnerabilities, (3) Identify sensitive data leaks, (4) Monitor privilege escalation paths, (5) Perform dynamic taint analysis for security testing. Supports Python, Java, JavaScript, and C/C++ with configurable taint sources and sinks.
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Identify and replace deprecated API usage in source code with modern alternatives. Use when: (1) Modernizing legacy codebases, (2) Upgrading framework versions (React, Django, Spring, etc.), (3) Fixing deprecation warnings in build output, (4) Preparing for major version upgrades, (5) Ensuring code uses current best practices. Supports Python, JavaScript/TypeScript, Java, and other major languages with both AST-based detection and pattern matching for accurate identification and automated replacement with validation.
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Identifies boundaries between modules or components in software systems through static code analysis and dependency detection. Use when Claude needs to analyze software architecture, identify module boundaries, detect boundary violations, find circular dependencies, or assess component coupling. Supports Python (packages and imports) and Java (packages and dependencies). Trigger when users ask to "identify boundaries", "find component boundaries", "detect boundary violations", "analyze module structure", "check architecture", or "find circular dependencies".
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Analyze CVE reachability in software repositories by examining how vulnerable dependencies are imported and used. Determines whether vulnerable components, classes, or functions are reachable from project code through call chain analysis, reflection detection, dynamic loading patterns, and configuration-gated behavior. Classifies each CVE as likely reachable, possibly reachable, or likely unreachable with supporting evidence. Use when analyzing security vulnerabilities in dependencies, performing post-disclosure CVE triage, assessing vulnerability impact, or when users ask to analyze CVE reachability, check if vulnerabilities are exploitable, or evaluate dependency security risks.
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Summarizes the complete lifecycle of a bug across code versions, tracking its introduction, detection, fixing attempts, and regression history. Use when users need to: (1) Understand how a bug evolved over time, (2) Trace when and how a bug was introduced, (3) Analyze fix attempts and their effectiveness, (4) Identify regression patterns, (5) Generate bug lifecycle reports for documentation or post-mortems. Takes a repository, bug identifier, and version history as input.
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Apply statistical methods including descriptive stats, trend analysis, outlier detection, and hypothesis testing. Use when analyzing distributions, testing for significance, detecting anomalies, computing correlations, or interpreting statistical results.
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QA an analysis before sharing with stakeholders — methodology checks, accuracy verification, and bias detection. Use when reviewing an analysis for errors, checking for survivorship bias, validating aggregation logic, or preparing documentation for reproducibility.
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Takes an app from hardcoded strings to a localized, continuously translated one with i18next (Locize optional): stack detection, config, wrapping strings in t(), key extraction, Locize sync, and AI translation. Use when the user asks to add or set up i18n, internationalization, localization, translations, or multi-language support, including phrasings like "add i18n", "internationalize my app", "make my app multilingual", "make my app translatable", "localize my app", "find hardcoded strings", "wrap my strings", and library-specific ones like "set up i18next", "add react-i18next", "use next-i18next". Also use when the user names languages they want to support ("I want German and French", "support more languages"), when an app already has i18next but strings are still hardcoded, or when translations need to move to a managed backend. Do not use for translating or reviewing strings in a project whose i18n is already set up.
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Semi-automated fact-checking, source credibility evaluation, misinformation detection, prebunking, and evidence-ledger based claim verification. Use when a user asks to verify a claim, article, social post, screenshot, URL, quote, data point, compare sources, detect manipulation, evaluate source reliability, or generate an HTML fact-check card/share-safe correction. Supports text, URLs, screenshots/images, two-source comparison, and topic-level prebunking in Bulgarian, English, Russian, EU policy, health, science, and geopolitical contexts. Trigger phrases: "is this true?", "is this fake?", "fact check this", "compare these sources", "проверка на факти", "истина ли е", "дезинформация ли е", "сравни тези източници".
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Implement complete dark mode for any frontend project. Auto-detects theme system (Flutter ThemeData, Tailwind dark class, CSS variables, MUI, Chakra UI, styled-components), generates a dark color palette with proper surface elevation hierarchy and desaturated brand colors, creates a theme toggle with system preference detection and persistence, migrates all hardcoded colors to theme tokens, handles special cases like images, shadows, form inputs, and charts, then verifies WCAG AA contrast ratios for both modes. Use when you need to add dark mode, fix dark theme contrast issues, create a theme switcher, or migrate hardcoded colors to theme-aware tokens.
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Comprehensive autonomous design quality audit across accessibility (WCAG 2.2), performance, theming, responsive/adaptive design, and anti-pattern detection. Produces a prioritized report with severity ratings and fix recommendations. Use when: 'audit design', 'check accessibility', 'a11y audit', 'design review', 'UI quality check', 'WCAG audit', 'design quality'.
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Drift is the divergence between declared infrastructure and live infrastructure.
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。