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概要と使いどころ
Create a Conventional Commit message for staged changes and run git commit. Use when changes are staged and you need a commit message in the form type(scope): subject, with a <=100 character imperative header and optional 'ref AL-###' body line. Retained for reference; use only when explicitly asked for this commit workflow.
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。
Explicit color reference for palettes, semantic tokens, format conversion, and contrast. Use only after Core's Zinc semantic tokens. Do not replace the product palette.
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。
Explicit polish reference for concentric radius, optical alignment, surface depth, contextual icons, and hit areas. Use only after Core frontend rules and packages/ui. Do not restyle product apps from these examples.
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。
Best practices for single-cell RNA-seq cell type annotation including marker-based, reference-based, and automated classification approaches.
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。
Automated scRNA-seq cell type annotation via pre-trained logistic regression. 45+ models: immune, gut, lung, brain, fetal, cancer microenvironments. Input normalized AnnData; outputs per-cell labels, majority-vote cluster labels, confidence scores. Use for fast, reference-backed annotation without manual marker inspection.
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。
Cell type annotation strategy for scRNA-seq: name clusters manually from canonical markers, transfer labels from a reference (CellTypist, popV), or do both and validate one against the other. Covers matching tissue and developmental stage, marker evidence, annotation confidence, doublets, unresolved clusters. Use when planning or troubleshooting how to annotate cells.
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。
Compute the bacterial pan-genome from Prokka/Bakta GFF3 annotations with Roary's CD-HIT + BLAST + MCL clustering pipeline. Builds gene presence/absence matrices, core/soft-core/shell/cloud partitions, multi-FASTA core gene alignments (with `-e`), and a pan-genome reference. Use Panaroo for higher-accuracy pan-genomes from highly fragmented assemblies, PIRATE for paralog-aware clustering, or PPanGGOLiN for graph-based partitioning.
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。
Detect somatic CNVs from WES/WGS/targeted BAMs (CNVkit v0.9.x). Bin coverage in target/antitarget regions, normalize vs reference, segment with CBS/HMM, call amps/dels, scatter/diagram plots, purity/ploidy, VCF/SEG export. CLI plus Python API (cnvlib). Use GATK CNV for deep WGS with population controls; use CNVkit for targeted/exome where antitarget bins matter.
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。
Consensus cell type annotation: runs 10+ algorithms (KNN-Harmony/BBKNN/Scanorama/scVI, CellTypist, ONCLASS, Random Forest, SCANVI, SVM, XGBoost) on a labeled reference and transfers labels via majority voting. Outputs per-method labels, consensus, agreement score. Use when single-method annotation is insufficient or you need ensemble uncertainty for novel states.
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。
Query OpenAlex REST API for 250M+ scholarly works, authors, institutions, journals, concepts. Search by keyword, author, DOI, ORCID, or ID; filter by year, OA, citations, field; retrieve citations, references, author disambiguation. Free, no auth. For PubMed use pubmed-database; preprints use biorxiv-database.
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 the discourse-level (structural) signs of AI writing that survive surface editing: stated lessons and moral-of-the-story closers, tidy single-track arcs, embodied-emotion performance ("chest tightened"), vague allusions instead of named references, unbroken linear structure, and shape convergence across pieces. Grounded in the StoryScope study (Russell et al. 2026): narrative structure alone detects AI text at 93.2% F1, and professional stylistic rewriting moved detection only 1.6 points. Use as the SECOND pass after the humanizer skill (which handles words and phrasing) whenever writing or revising LinkedIn posts, course lessons, blog posts, essays, newsletters, or emails that must read as human. Triggers: "humanize", "de-slop", "AI tells", "make this sound human", "structural pass", "deep humanize".
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。
Provides AWS CloudFormation patterns for DynamoDB tables, GSIs, LSIs, auto-scaling, and streams. Use when creating DynamoDB tables with CloudFormation, configuring primary keys, local/global secondary indexes, capacity modes (on-demand/provisioned), point-in-time recovery, encryption, TTL, and implementing template structure with Parameters, Outputs, Mappings, Conditions, cross-stack references.
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。
Provides AWS CloudFormation patterns for CloudFront distributions, origins (ALB, S3, Lambda@Edge, VPC Origins), CacheBehaviors, Functions, SecurityHeaders, parameters, Outputs and cross-stack references. Use when creating CloudFront distributions with CloudFormation, configuring multiple origins, implementing caching strategies, managing custom domains with ACM, configuring WAF, and optimizing performance.
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。
Provides AWS CloudFormation patterns for security infrastructure including KMS encryption, Secrets Manager, IAM security, VPC security, ACM certificates, parameter security, outputs, and secure cross-stack references. Use when implementing security best practices, encrypting data, managing secrets, applying least privilege IAM policies, securing VPC configurations, managing TLS/SSL certificates, and implementing defense in depth strategies.
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。
Provides AWS CloudFormation patterns for CloudWatch monitoring, metrics, alarms, dashboards, logs, and observability. Use when creating CloudWatch metrics, alarms, dashboards, log groups, log subscriptions, anomaly detection, synthesized canaries, Application Signals, and implementing template structure with Parameters, Outputs, Mappings, Conditions, cross-stack references, and CloudWatch best practices for monitoring production infrastructure.
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。
Provides AWS CloudFormation patterns for ECS clusters, task definitions, services, container definitions, auto scaling, blue/green deployments, CodeDeploy integration, ALB integration, service discovery, monitoring, logging, template structure, parameters, outputs, and cross-stack references. Use when creating ECS clusters with CloudFormation, configuring Fargate and EC2 launch types, implementing blue/green deployments, managing auto scaling, integrating with ALB and NLB, and implementing ECS best practices.
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。
Provides AWS CloudFormation patterns for EC2 instances, Security Groups, IAM roles, and load balancers. Use when creating EC2 instances, SPOT instances, Security Groups, IAM roles for EC2, Application Load Balancers (ALB), Target Groups, and implementing template structure with Parameters, Outputs, Mappings, Conditions, and cross-stack references.
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。
Provides AWS CloudFormation patterns for Amazon S3. Use when creating S3 buckets, policies, versioning, lifecycle rules, and implementing template structure with Parameters, Outputs, Mappings, Conditions, and cross-stack references.
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。
Provides AWS CloudFormation patterns for Amazon RDS databases. Use when creating RDS instances (MySQL, PostgreSQL, Aurora), DB clusters, multi-AZ deployments, parameter groups, subnet groups, and implementing template structure with Parameters, Outputs, Mappings, Conditions, and cross-stack references.
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。
Provides AWS CloudFormation patterns for Auto Scaling including EC2, ECS, and Lambda. Use when creating Auto Scaling groups, launch configurations, launch templates, scaling policies, lifecycle hooks, and predictive scaling. Covers template structure with Parameters, Outputs, Mappings, Conditions, cross-stack references, and best practices for high availability and cost optimization.
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。
Provides a structured 8-phase workflow for resolving GitHub issues in Claude Code. Covers fetching issue details, analyzing requirements, implementing solutions, verifying correctness, performing code review, committing changes, and creating pull requests. Use when user asks to resolve, implement, work on, fix, or close a GitHub issue, or references an issue URL or number for implementation.
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 swift-snapshot-testing to visually verify SwiftUI views during implementation. Renders views to PNG for comparison against design references. Fast feedback loop (~25s/cycle).
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。
Design, implement, audit, or review Shift user interfaces and shared UI components. Use for React components, Base UI primitives, Tailwind styling or theme tokens, Figma/reference matching, accessibility, icon-only actions, tooltips, menus, popovers, dialogs, forms, layout, and visible UI regressions.
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 existing source code as the specification when the user can't describe what they want in words. Use when the user points at a library, module, folder, or site and says "like this," even if it's in a different language or stack.
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。