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Help quality leaders at medical technology companies prepare management reviews, define quality objectives, and track performance indicators. Organize audit findings, complaints, corrective actions, and regulatory status into reports and assigned improvement actions.
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Check single-cell RNA-seq data quality, filter low-quality cells using median absolute deviation, a measure of spread, and compare plots before and after filtering. Outputs include filtered data and the original dataset with quality annotations.
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Build QA quality dashboards, calculate defect metrics, analyze escape rates, and produce quality trend reports. Use this skill whenever a QA lead or manager needs to measure and communicate quality outcomes. Trigger on phrases like "quality metrics", "defect density", "escape rate", "bug trends", "quality dashboard", "sprint quality report", "test coverage metrics", "QA KPIs", "how is quality trending", "defect leakage", "production bugs this quarter", "mean time to detect", "quality scorecard", or any request to quantify, visualize, or report on the quality of a product or testing process. Also trigger when a manager asks "how is QA performing?" or an engineering lead asks "what does our defect data tell us?".
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用于制造现场的不合格品管理，梳理隔离、影响范围调查、原因分析、处置及纠正预防措施的效果验证，并协助解读控制图和改善供应商质量。
Check code and agent outputs, track reliability scores, and roll back changes that fail a chosen threshold. Includes quality reports, live monitoring, and integration with automated build and deployment workflows.
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Evaluate ENCODE experiment quality using standard metrics and audit flags. Use when the user asks about data quality, wants to filter for high-quality experiments, needs to interpret quality metrics (FRiP, NSC, RSC, NRF, IDR, TSS enrichment, fragment size), wants to understand ENCODE audit warnings, needs to compare quality across experiments, or is deciding whether data is usable for their analysis. Also use when the user mentions QC, quality control, or data filtering.
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Patterns for vibe coding that produce production-quality output instead of garbage. Covers prompt engineering for AI coding, verification without reading every line, accept/reject/refine decision framework, architectural guardrails, context management (CLAUDE.md, .cursor/rules), TDD-driven vibe coding, screenshot-driven development, diff review discipline, and the anti-patterns that destroy quality. Activate on: "vibe coding", "AI coding quality", "Claude Code workflow", "AI-assisted development", "how to use AI for coding", "coding with AI", "agentic engineering workflow", "AI code quality", "vibe code without garbage", "production quality AI code", "CLAUDE.md setup", "cursorrules setup", "pre-commit hooks for AI", "TDD with AI", "test-driven vibe coding". NOT for: multi-person collaborative coding sessions (use cooperative-vibe-coding), building AI/LLM applications (use ai-engineer), prompt engineering for non-coding LLM use cases (use prompt-engineer).
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Work with FASTQ quality scores using Biopython - access Phred scores, filter and trim by quality, compute per-position profiles, and convert between Sanger/Phred+33, Solexa, and Illumina/Phred+64 encodings. Use when analyzing read quality, filtering or trimming low-quality bases, generating quality reports, or deciding which FASTQ quality encoding a file uses before parsing.
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Convert feature requirements and implementation plans into a test strategy, task checklist, and quality assurance plan for GitHub projects. Define test coverage, priorities, dependencies, estimates, and quality checkpoints using established testing and software quality frameworks.
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Build and maintain a medical device quality management system using ISO 13485:2016 workflows. It supports gap analysis, controlled documentation, internal audits, corrective and preventive actions, process validation, and supplier evaluation, with templates and an audit checklist generator.
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Validate FASTQ sequencing files, run local quality checks, and interpret reports to decide whether trimming is needed. Optionally remove adapters or low-quality read ends while preserving original reads and their quality reports.
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Review website code across performance, accessibility, search engine optimization, and web best practices. Receive findings ranked by severity, explanations of their impact, concrete fixes with code examples, and a recommended order for addressing them.
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Work with FASTQ quality scores using Biopython. Use when analyzing read quality, filtering by quality, trimming low-quality bases, or generating quality reports.
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Work with FASTQ quality scores using Biopython. Use when analyzing read quality, filtering by quality, trimming low-quality bases, or generating quality reports.
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The systematic discipline of reviewing AI-generated and human-written marketing content for quality, accuracy, consistency, and effectiveness. This isn't proofreading—it's strategic quality control that catches issues before they damage brand, waste spend, or miss opportunities. In an era where AI can generate content at scale, QA becomes the bottleneck— and the moat. Speed without quality is just fast failure. Every piece of content is a brand promise, and QA ensures that promise is kept. Content QA operates at multiple levels: tactical (spelling, grammar), strategic (message alignment), technical (platform compliance), and brand (voice, tone). The best QA catches what the creator couldn't see because they were too close. Use when "review content, QA, quality check, proofread, check copy, content review, brand compliance, verify content, fact check, review before publish, content approval, final review, qa, content-review, quality-assurance, brand-compliance, proofreading, content-quality, review" mentioned.
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Expert-level air quality engineering covering pollutant sources, atmospheric dispersion, air quality standards, emission controls, and air quality modeling.
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Full adversarial quality loop — implement, self-attack, parallel verification, quality gates, final validation. 全品質循環：實現、自攻、並行驗證、質量關卡、最終確認. Use when: implement task with quality, run adversarial review, verify code quality, run quality gates, complete workflow task
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Design continuous agent workflows with quality checks, evaluations, and recovery controls. Helps choose a loop pattern for the task and define how to pause, audit, narrow scope, and retry when work stalls or fails.
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Run a project's existing type checks, code analysis, tests, unused code detection, and shell checks. Combine results into a weighted score from 0 to 10 and track code quality trends over time.
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Define project quality standards in CONSTRAINTS.md, connect measurable targets to checks, and inspect changes for weakened safeguards. It helps teams choose thresholds and catch skipped tests, suppressed warnings, unfinished code, or lowered standards during agent development.
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Run scoped quality checks without changing product code. Record the exact revision, commands, logs, and outcomes, then report failures, incomplete checks, and existing unresolved issues to support reviews or release preparation.
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Check security audit findings against code evidence and trace how untrusted input moves through a system. Guide a second review, document verification gaps, and assess draft reports against a minimum quality score before delivery.
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Check Agent Skills for documentation structure and Python script behavior, then score quality and security posture. Get grades and improvement priorities, and use pass criteria in pre-commit checks or continuous integration, which runs checks automatically.
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Create Terraform files that define alerts for AI agent latency, errors, token use, safety, security, and response quality. Check telemetry and existing policies first, then validate the generated configuration. Quality alerts require Vertex AI.
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