Results for “sequencing-quality-control”

Stitched Image Quality Control

用于审计Claude技能和命令的质量。支持快速扫描（仅变更技能）和全面盘点模式，采用顺序子代理批量评估。

Process and analyze high-throughput sequencing data with deepTools for quality control, normalization, comparison, and publication-quality visualizations of ChIP-seq, RNA-seq, and ATAC-seq experiments.

Use when auditing Claude skills and commands for quality. Supports Quick Scan (changed skills only) and Full Stocktake modes with sequential subagent batch evaluation.

Enforces bulk-safety rules, sharing model requirements, CRUD/FLS security, SOQL injection prevention, PNB test coverage, and modern Apex idioms for Salesforce development.

Runs a complete quality engineering audit on any codebase, deriving behavioral requirements, generating spec-traced tests, performing multi-pass code review, and producing a consolidated bug report with verified patches.

Diagnoses Qdrant production issues using metrics and observability tools, covering optimizer problems, memory spikes, and slow queries.

Validates data quality with completeness, consistency, accuracy checks and generates quality reports

Measures code quality, identifies code smells, and implements quality gates using static analysis and technical debt tracking.

Hunt for supply chain compromise indicators including trojanized software updates, compromised dependencies, unauthorized code modifications, and tampered build artifacts.

Single-cell RNA-seq analysis. Load .h5ad/10X data, QC, normalization, PCA/UMAP/t-SNE, Leiden clustering, marker genes, cell type annotation, trajectory, for scRNA-seq analysis.

Runs a structured quality control audit on codebases, covering tests, imports, static analysis, type checking, smoke tests, UI verification, file consistency, and documentation, producing a PASS/WARN/FAIL report.

Linting, formatting, and encoding standards for the AegisNex codebase. Covers ruff, mypy, eslint, editorconfig, pre-commit hooks, and how to add new quality checks.

CQO quality and operational governance lead. Owns gates, regression strategy, memory hygiene, port/service policy, and archive approval.

Python code-quality anti-patterns and review checks: exception-hierarchy correctness, singleton identity comparison, narrow exception handling, wildcard-import avoidance, magic-number naming, and dead-local removal. Use when reviewing or self-reviewing Python code for correctness and readability defects that linters and reviewers should catch.

Use when you need to remove adapter sequences from high-throughput sequencing reads, trim low-quality bases, or filter reads by length. Supports single-end and paired-end FASTQ/FASTA input with error-tolerant adapter matching.

Provides code review best practices, checklists, and example feedback for quality, security, performance, and maintainability.

Provides a structured code review checklist covering security, performance, correctness, maintainability, testing, accessibility, and documentation, with a three-pass review process and guidance on severity levels and anti-patterns.

Single-cell RNA-seq analysis. Load .h5ad/10X data, QC, normalization, PCA/UMAP/t-SNE, Leiden clustering, marker genes, cell type annotation, trajectory, for scRNA-seq analysis.

Reviews cohesion, naming, responsibility, complexity, and maintainability. Do not use as a default companion to every code change.

コード品質改善の全サイクルを実行するスキル。品質スイープ→コードレビュー→Issue化→自律実装ループ→完了通知の5フェーズ。ユーザーが「dev-cycle」「開発サイクル」「品質改善」「自律ループ」などと言った時、または /dev-cycle を呼び出した時にトリガーする。

Review test quality using best practices validation. Use when user says "lets review tests" or "I want to evaluate test quality"