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概要と使いどころ
In recent years a wealth of biological data has become available in public data repositories. Easy access to these valuable data resources and firm integration with data analysis is needed for comprehensive bioinformatics data analysis. bio
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Expert knowledge for Azure AI Vision development including decision making, limits & quotas, configuration, integrations & coding patterns, and deployment. Use when using Image Analysis, Read OCR containers, smart-crop thumbnails, background removal, or video frame analysis, and other Azure AI Vision related development tasks. Not for Azure AI Custom Vision (use azure-custom-vision), Azure AI Video Indexer (use azure-video-indexer), Azure AI Document Intelligence (use azure-document-intelligence), Azure AI Immersive Reader (use azure-immersive-reader).
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。
Amazon negative keyword management and optimization. Search term analysis, waste reduction, savings estimation, and automated negative keyword strategies. Use when the user asks about negative keywords, search term analysis, ad waste reduction, or PPC optimization.
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Amazon keyword position monitoring and SERP analysis. Real-time position tracking, competitor movement alerts, search result changes, and keyword performance optimization. Use when the user asks about keyword monitoring, SERP tracking, position alerts, or search performance 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。
Assess the health and consistency of an existing brand — identity, messaging, voice, positioning, and market perception. Use when the user says "brand audit", "brand review", "assess our brand", "is our brand consistent", "brand health check", "brand analysis", "something's off with our brand", "our brand feels outdated", "brand diagnosis", "review our brand", "brand gap analysis", or wants to understand where their brand is strong and where it needs work before investing in brand development.
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。
Scan social platforms for top-performing content by engagement before you create anything. Use this skill when the user wants to see what content is winning in a niche, find viral content patterns, research what's working on YouTube/TikTok/X/Reddit, benchmark engagement, discover content gaps, or says "what content is working for [topic]", "show me top performing content about [keyword]", "what's trending in [niche]", "find viral content about [product]", "content research for [keyword]", "what gets views in [niche]", "engagement analysis for [topic]", "scout the competition", "what videos are getting the most views about [keyword]", "social listening for [topic]", "trending content in [niche]", "top content analysis", "what hooks work for [keyword]", "content intelligence", "find winning formats".
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。
Estimate pages needed for topical authority. Go/no-go decision before investing months in content. Triggers on: "how much content do I need", "topical authority estimate", "content moat", "how many articles", "content gap analysis", "can I compete in this niche", "content investment calculator", "is this niche worth the effort", "SEO feasibility", "how many pages to rank", "content volume needed", "competitive content analysis", "moat calculation", "authority gap", "should I invest in this niche".
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。
Pull Google Search Console data and perform search performance analysis. Use when asked about search rankings, clicks, impressions, CTR, index coverage, Core Web Vitals, or sitemap status. Trigger phrases: "search console", "GSC", "search performance", "clicks and impressions", "CTR analysis", "index coverage", "core web vitals", "URL inspection", "sitemap status", "ranking data", "search queries", "keyword positions".
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。
Analyze Google search results (SERP) for any keyword. Use when the user says "analyze the SERP", "what ranks for", "SERP analysis", "competitive analysis for keyword", "content brief", "what's ranking", "search results for", "who ranks for", or asks about ranking content patterns for a keyword.
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。
Identify content gaps between your site and competitors. Use when the user says "content gaps", "what am I missing", "competitor content", "content opportunities", "topics I should cover", "content gap analysis", or asks about finding topics and keywords their site doesn't cover but competitors do.
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。
Use when analyzing suspected malware through static, dynamic, and behavioral techniques, including IOC extraction, YARA or Sigma rules, sandboxing, and anti-analysis behavior.
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Use this skill whenever the user wants to analyze binaries with radare2/r2 from the command line, including reverse engineering, disassembly, function analysis, strings/import inspection, patching, binary diffing, hex inspection, or r2 scripting. Also use it when the user mentions PE/ELF/Mach-O/DEX/WASM files together with CLI analysis, `rabin2`, `rasm2`, `radiff2`, `r2pipe`, or asks for radare2 command help on Windows/Linux/macOS.
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 nf-core bioinformatics pipelines (rnaseq, sarek, atacseq) on sequencing data. Use when analyzing RNA-seq, WGS/WES, or ATAC-seq data—either local FASTQs or public datasets from GEO/SRA. Triggers on nf-core, Nextflow, FASTQ analysis, variant calling, gene expression, differential expression, GEO reanalysis, GSE/GSM/SRR accessions, or samplesheet creation.
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Performs quality control on single-cell RNA-seq data (.h5ad or .h5 files) using scverse best practices with MAD-based filtering and comprehensive visualizations. Use when users request QC analysis, filtering low-quality cells, assessing data quality, or following scverse/scanpy best practices for single-cell analysis.
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Advanced architecture stress analysis methodology for designing systems that survive unknown stresses. Load when --residuality flag is used or when designing high-uncertainty, mission-critical systems.
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Structured persona creation and JTBD analysis methodology - persona templates, ODI job step tables, pain point mapping, success metric quantification, and multi-persona segmentation
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Conducts Jobs-to-be-Done analysis, UX journey design, and requirements gathering through interactive discovery. Use when starting feature analysis, defining user stories, or creating acceptance criteria.
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Use when raw data exists — Meta, Google, TikTok, GA4, Shopify, a CRM export, or a spreadsheet — and has to become insight and decisions: descriptive, diagnostic, predictive, and prescriptive layers, cuts by channel, campaign, creative, audience, and time, cohorts, and a decision log. Trigger on 'analyze this data', 'read these numbers for me', 'what does this export say', 'pull insight from GA4', 'cohort analysis', 'here is the spreadsheet'. Also use when the user pastes a table and asks what it means. Not for — diagnosing ad root cause, see `03-performance-eval-global`; writing the report a stakeholder reads, see `07-marketing-report-global`; auditing account setup, see `21-ads-audit-global`.
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。
Evaluate product investment options across build, buy, partner, defer, and do-nothing paths, setting incremental accounting boundaries and cash-versus-capacity rules without fabricated ROI. Use for product investment analysis / product ROI scenario. (triggers: `product investment analysis`, `product ROI scenario`)
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Conduct evidence-backed product market research and competitive analysis with explicit provenance, substitute identification, and counterevidence tracking. Use when evaluating market dynamics, competitor positioning, direct or indirect substitutes, or buyer alternatives. (triggers: `product market research`, `product competitive analysis`)
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Audit test coverage health, gaps, and QE debt for Jira stories or epics. Produces coverage_analysis_report.md with AC-to-TC heatmap, risk scores, and prioritized action plan. Use when assessing coverage percentage, pre-release readiness, sprint readiness, or identifying missing test cases. Do NOT use for TC creation — use zephyr-test-generation instead.
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Dart static analysis, linting, formatting, and code-generation standards. Use only for analysis_options.yaml, build_runner, dart format, DCM, lefthook, or analyze/format CI failures; defer Dart language, null-safety, Flutter tests, and generic CI questions.
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。
Systematic experimental results analysis workflow for ML/AI research papers. Connects experimental data to publication-ready Results sections with statistical validation, visualizations, and quality checks. Triggers on "analyze experimental results", "generate results section", "statistical analysis of experiments", "compare model performance", "create results visualization".
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。
Find similar vulnerabilities and bugs across codebases using pattern-based analysis. Use when hunting bug variants, building CodeQL/Semgrep queries, analyzing security vulnerabilities, or performing systematic code audits after finding an initial issue.
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。