3,382 件 ・ 関連度順
概要と使いどころ
Quality control and exploration of RNA-seq count matrices before differential expression. Check for outliers, batch effects, and sample relationships. Use when assessing count matrix quality before DE 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。
Generates standardized quality control reports by aggregating metrics from FastQC, alignment, and other tools using MultiQC. Use when summarizing QC metrics across samples, creating shareable quality reports, or building automated QC pipelines.
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。
ATAC-seq library quality control -- TSS enrichment, FRiP, fragment-size periodicity, library complexity (NRF/PBC1/PBC2), mitochondrial fraction, and ENCODE 4 thresholds. Use when assessing whether an ATAC-seq library passes ENCODE acceptance criteria, diagnosing transposition artefacts, comparing Omni-ATAC vs standard prep quality, or selecting which replicates to drop before peak calling.
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。
Filter alignments by flags, mapping quality, and regions using samtools view and pysam. Use when extracting specific reads, removing low-quality alignments, or subsetting to target regions.
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 genome assembly quality using QUAST for contiguity metrics and BUSCO for completeness. Essential for evaluating assembly success and comparing assemblers. Use when evaluating assembly completeness and quality.
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。
Validate alignment quality with insert size distribution, proper pairing rates, GC bias, strand balance, and other post-alignment metrics. Use when verifying alignment data quality before variant calling or quantification.
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。
Protein design quality control, filtering thresholds, and ranking guidance. Use this skill when: (1) Evaluating design quality for binding, expression, or structure, (2) Setting filtering thresholds for pLDDT, ipTM, PAE, (3) Checking sequence liabilities (cysteines, deamidation, polybasic clusters), (4) Creating multi-stage filtering pipelines, (5) Computing PyRosetta interface metrics (dG, SC, dSASA), (6) Checking biophysical properties (instability, GRAVY, pI), (7) Ranking designs with composite scoring. This skill provides research-backed thresholds from binder design competitions and published benchmarks.
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。
Quality control metrics and filtering thresholds for protein design. Use this skill when: (1) Evaluating design quality for binding, expression, or structure, (2) Setting filtering thresholds for pLDDT, ipTM, PAE, (3) Checking sequence liabilities (cysteines, deamidation, polybasic clusters), (4) Creating multi-stage filtering pipelines, (5) Computing PyRosetta interface metrics (dG, SC, dSASA), (6) Checking biophysical properties (instability, GRAVY, pI), (7) Ranking designs with composite scoring. This skill provides research-backed thresholds from binder design competitions and published benchmarks.
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 and visualize QA dashboards and reports with Allure Report, Grafana, and ReportPortal. Covers test execution visualization, stakeholder-facing quality reports, trend/flakiness panels, release-readiness gates, alerting, and CI integration for automated report generation. Use when: "test dashboard," "Allure," "test report," "quality dashboard," "Grafana," "ReportPortal," "test results visualization." Not for: defining which KPIs to measure or how to interpret them — use qa-metrics (this skill builds the panels; qa-metrics decides what they should show). Related: qa-metrics, ci-cd-integration, ai-bug-triage.
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。
Test AI/LLM features that ship in your product. Covers prompt regression testing, response quality evaluation, tool-call validation, hallucination and RAG grounding checks, nondeterministic-output strategies, red-team/safety scans, eval frameworks, and agent-as-target injection (indirect injection via tool output / RAG / scan reports, self-propagating payloads, data exfiltration via an agent) plus a bundled detector for untrusted content. Use when: "test our LLM feature," "prompt regression test," "eval framework," "hallucination test," "RAG grounding," "nondeterministic output," "AI feature testing," "red-team our chatbot," "indirect prompt injection," "agent reading untrusted tool output," "production AI quality." Not for: using AI to generate your own test code — use ai-test-generation. Not for: classifying CI failures with AI — use ai-bug-triage. Not for: EU AI Act / GDPR conformity of an AI feature — use compliance-testing. Not for: canary/flag rollout of an AI feature — use testing-in-production. Related: ai-test-generation, ai-qa-review, api-testing, compliance-testing, security-testing, risk-based-testing, test-data-management.
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。
Remove AI-generated patterns from QA reports, bug reports, test summaries, status updates, and quality communications. Detects and rewrites robotic test-result language, template-sounding status updates, inflated severity descriptions, and generic stakeholder reports — without inventing facts. Makes QA writing sound like a real engineer wrote it. Use when: "humanize report," "rewrite QA summary," "fix test report," "make this sound human," "clean up status update." Not for: general prose, blog, or marketing-copy cleanup — use the global humanizer skill. Not for: classifying or routing CI failures — use ai-bug-triage. Related: ai-bug-triage, qa-metrics, qa-dashboard, quality-postmortem.
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 EXISTING test code for quality, smells, and testability issues. Detects test smells across six dimensions — readability, reliability, diagnostic value, design, AI-generated, and coverage — analyzes testability of application code, and backs the qualitative smells with mutation testing. Use when: "review my tests," "test quality audit," "test smells," "testability analysis," "are these tests any good." Not for: generating new tests — use `ai-test-generation`. Not for: testing AI features in your product — use `ai-system-testing`. Related: unit-testing, shift-left-testing, coverage-analysis, ai-test-generation.
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。
ESLint is the standard linting tool for JavaScript and TypeScript projects. It statically analyzes your code to find problems, enforce coding conventions, and catch bugs before they reach production. ESLint 9 introduced the flat config format, replacing the legacy `.eslintrc` system with a simpler, more composable `eslint.config.js` file. With a rich plugin ecosystem covering React, TypeScript, accessibility, and import ordering, ESLint is the backbone of code quality in modern web 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。
Expert guidance for DeepEval, the open-source framework for unit testing LLM applications. Helps developers write test cases, define custom metrics, and integrate LLM quality checks into CI/CD pipelines using a pytest-like interface.
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。
Perform thorough code reviews with actionable, prioritized feedback. Use when a user asks to review code, check code quality, find bugs, review a pull request, audit code for issues, or get feedback on implementation. Covers correctness, security, performance, readability, and best practices across languages.
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。
Coordinate multiple AI agents working together on complex tasks — routing, handoffs, consensus, memory sharing, and quality gates. Use when tasks involve building multi-agent systems, coordinating specialist agents in a pipeline, implementing agent-to-agent communication, designing swarm architectures, setting up agent orchestration frameworks, or building autonomous agent teams with supervision and quality control. Covers hierarchical, mesh, and pipeline topologies.
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 distinctive, production-grade frontend interfaces with high design quality. Covers design thinking, typography pairing, color theory, motion design, spatial composition, and code quality. Generates real working code — HTML/CSS/JS or React — with intentional aesthetic direction, not generic AI output.
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 implementing data governance frameworks, building data catalogs, establishing data lineage, defining data quality rules, or setting up data stewardship programs - covers metadata management, data quality, and complianceUse when ", " mentioned.
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。
Writing maintainable code - readability principles, SOLID patterns applied pragmatically, and the judgment to know when rules should bendUse when "code quality, clean code, readability, naming, SOLID, refactor, code review, best practices, maintainable, how should I structure, clean-code, solid, readability, maintainability, code-review, naming, functions, principles" mentioned.
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。
The art of reviewing code that improves both the codebase and the developer - sharing knowledge, maintaining standards, and building cultureUse when "code review, PR review, pull request, merge request, review comments, LGTM, review feedback, approve PR, request changes, review checklist, code quality, review standards, code-review, pull-request, PR, quality, standards, feedback, collaboration, mentoring" mentioned.
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 a fast 6-point quality check for technical documents (specs, design docs, READMEs). Use after editing any specification or design document.
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。
Sentence-level and section-level craft for instructions Claude will actually follow, in any container — project instructions, a standalone prompt, or skill body text. Use when the text exists or is being drafted and the question is quality: "why is Claude ignoring this instruction", "rewrite this prompt so it works", "improve my project instructions", "make this instruction stick", "is this too prescriptive", "how much structure does Sonnet need versus Opus". Covers imperative construction, positive framing, strategic-versus-procedural calibration per model, extended thinking, complexity scaling, and a quality checklist. For deciding WHICH container the instruction belongs in, use crafting-instructions. For building, testing and packaging a whole skill directory, use creating-skill.
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。
Comprehensive SEO analysis for any website or business type. Full site audits, single-page analysis, technical SEO (crawlability, indexability, Core Web Vitals with INP), schema markup, content quality (E-E-A-T), image optimization, sitemap analysis, and GEO for AI Overviews/ChatGPT/Perplexity. Industry detection for SaaS, e-commerce, local, publishers, agencies. Triggers on: SEO, audit, schema, Core Web Vitals, sitemap, E-E-A-T, AI Overviews, GEO, technical SEO, content quality, page speed, structured 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。
Content quality and E-E-A-T analysis with AI citation readiness assessment. Use when user says "content quality", "E-E-A-T", "content analysis", "readability check", "thin content", or "content audit".
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。