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概要と使いどころ
Detect anomalies and outliers in construction data: unusual costs, schedule variances, productivity spikes. Statistical and ML-based detection methods.
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Detect and analyze geometric clashes between BIM elements. Identify hard clashes, soft clashes, and workflow conflicts using spatial analysis and rule-based detection.
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Detects AI-generated writing patterns in prose. Use when reviewing docs for slop, vague language, or identity leaks before publishing.
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Use Detectron2 for object detection, segmentation, configuration, datasets, training, inference, export, and extension workflows.
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Build automated PII detection and redaction pipelines using spaCy NER, Microsoft Presidio, and AWS Macie integration. Includes confidence scoring, custom entity type definitions, batch processing workflows, and multi-format document scanning for structured and unstructured data sources.
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Classifies sensitive data in AI/ML training datasets including bias detection for Art. 9 categories, data card documentation, provenance tracking, and consent verification for model training. Keywords: AI training data, ML dataset, bias detection, data card, model training, Art 9, consent, GDPR AI.
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Detects and removes AI writing patterns from text. Use when polishing drafts, cleaning AI-generated content, or ensuring writing sounds authentically human. Invoke with "run through slop detector", "clean this up", or "remove AI fingerprints".
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Analyze source code statically to detect potential functional bugs including null dereferences, incorrect condition checks, unreachable code, inconsistent state updates, logic errors, resource leaks, and type mismatches. Report suspicious code locations with detailed explanations, severity levels, and confidence assessments. Use when reviewing code for bugs, performing code audits, or when the user asks to find bugs, detect issues, analyze code for problems, or perform static analysis.
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Detect semantic-level bugs by analyzing whether code behavior matches its intended purpose inferred from function/variable names, comments, docstrings, and documentation. Use when users need to: (1) Find logic errors where implementation contradicts stated intent, (2) Identify off-by-one errors and boundary mismatches, (3) Detect inverted logic or wrong operators, (4) Catch missing edge case handling, (5) Verify code matches its documentation. Highlights mismatches between intent and implementation across multiple programming languages.
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Identify design quality issues in code including high coupling, low cohesion, God classes, long methods, and other code smells. Use when: (1) Reviewing code architecture and design quality, (2) Identifying refactoring opportunities, (3) Detecting God classes or classes with too many responsibilities, (4) Finding high coupling or low cohesion issues, (5) Analyzing code maintainability and technical debt. Detects coupling smells, cohesion problems, complexity issues, size violations, and encapsulation problems with actionable refactoring suggestions.
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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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Detect and remove doublets from flow and mass cytometry data. Covers FSC/SSC gating and computational doublet detection methods. Use when filtering out cell aggregates before clustering or quantitative analysis.
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Detects somatic mutations in circulating tumor DNA using variant callers optimized for low allele fractions with UMI-based error suppression. Reliably detects mutations at VAF above 0.5 percent using consensus-based approaches. Use when identifying tumor mutations from plasma DNA or tracking specific variants.
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Detect AI-generated text using rule-based analysis, LLM-as-judge scoring, and optional external APIs. Use when: auditing content for AI authorship, academic integrity checks, editorial review, SEO content audits.
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Runs a full dead-end page audit for any website. Discovers all blog/content pages, detects which ones have zero outgoing links to any URL on the same domain, fetches keywords, clusters pages by topic, and generates 3 outgoing link suggestions per dead-end page with anchor text, placement guidance, and ready-to-paste context copy. Outputs a styled HTML report matching report-style-reference.html. Automatically detects site framework (Next.js, static, WordPress) and uses the correct detection method: RSC header for Next.js/Vercel sites, standard curl for static and traditional CMS sites. Trigger when a user provides a domain or URL and asks for a dead-end page audit, outgoing internal links audit, "which pages don't link out", "find pages with no internal links in the content", "pages that are link dead-ends", or any variation of finding pages that have zero outgoing links to the same domain. Always use this skill for dead-end page audits — never attempt the workflow without following every step here.
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Engineering detection rules (Sigma/YARA), detection coverage mapping, threat hunting hypotheses, Purple Team Blue side, Detection-as-Code CI/CD. Use when defensive verification is needed.
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Detect design patent infringement risks by comparing a product image against a global design patent database across 25+ jurisdictions. Triggered when users mention design patent detection, patent infringement check, patent risk analysis, TRO case lookup, design patent search, product design patent screen, or visual patent similarity. Even if the user does not explicitly say "design patent," this skill should be triggered whenever the need involves checking whether a product image may infringe existing design patents.
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Detect image copyright infringement risks by comparing against a database of registered copyrighted works with similarity scoring, TRO litigation history, and radar-based infringement assessment. Triggered when users mention copyright detection, image infringement check, copyright risk analysis, TRO risk, copyright similarity search, rights owner lookup, or copyright compliance verification. Even if the user does not explicitly say "copyright," this skill should be triggered whenever the need involves checking whether an image may infringe registered copyright works.
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Uses AWS CloudWatch SDK (boto3) to configure anomaly detection bands on metrics via PutAnomalyDetector API. Integrates with SNS for notifications and CloudWatch Synthetics for canary-based uptime monitoring.
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Detect project maturity stage (new/early/mid/mature) from file structure and route to appropriate onboarding workflow
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Detect missing docs, undocumented files, project health issues, and coverage gaps on session start or on-demand. Scans for README gaps, TODO/FIXME counts, and test coverage holes.
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Detect when agents are stuck in repetitive action loops using a rolling 20-action window with escalating nudges — replan after 3 similar actions, explore after 5, force-done after 8.
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Design and operate a data integrity monitoring programme based on ALCOA+ principles. Covers detective controls, audit trail review schedules, anomaly detection patterns (off-hours activity, sequential modifications, bulk changes), metrics dashboards, investigation triggers, and escalation matrix definition. Use when establishing a data integrity monitoring programme for GxP systems, preparing for inspections where data integrity is a focus area, after a data integrity incident requiring enhanced monitoring, or when implementing MHRA, WHO, or PIC/S guidance.
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AI situational awareness — internal threat detection for hallucination risk, scope creep, and context degradation. Maps Cooper color codes to reasoning states and OODA loop to real-time decisions. Use during any task where reasoning quality matters, when operating in unfamiliar territory, after detecting early warning signs such as an uncertain fact or suspicious tool result, or before high-stakes output like irreversible changes or architectural decisions.
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。