AI Coding Skills for Express

Review API design against REST best practices and internal consistency. Audits naming conventions, HTTP method semantics, status code correctness, pagination and filtering patterns, error response format, versioning strategy, rate limiting, idempotency keys, HATEOAS links, and content negotiation. Works with Express, Fastify, Hono, Flask, Django REST, Spring, Rails, Gin, and any HTTP API framework. Use when you need to review an API, audit REST endpoints, check API consistency, fix HTTP status codes, add pagination, or prepare an API for public release.

Reverse-engineer any codebase into a complete Product Requirements Document (PRD). Analyzes routes, components, state management, API integrations, and user interactions to produce business-readable documentation detailed enough for engineers or AI agents to fully reconstruct every page and endpoint. Works with frontend frameworks (React, Vue, Angular, Svelte, Next.js, Nuxt), backend frameworks (NestJS, Django, Express, FastAPI), and fullstack applications. Trigger when users mention: generate PRD, reverse-engineer requirements, code to documentation, extract product specs from code, document page logic, analyze page fields and interactions, create a functional inventory, write requirements from an existing codebase, document API endpoints, or analyze backend routes.

Generate OpenAPI 3.1 documentation from your API codebase. Auto-detects Express, Fastify, NestJS, Django, FastAPI, Flask, Rails, Spring, Go, and more. Extracts routes, request/response schemas, auth requirements, and validation rules. Sets up interactive docs with Swagger UI, Redoc, or Scalar. Use when you need API documentation, OpenAPI spec, Swagger docs, endpoint reference, or REST API docs.

Scaffold a production-ready backend REST API -- generate a complete server project with routes, controllers, service layer, repository pattern, database models with migrations, JWT authentication with RBAC, request validation, global error handling with custom error classes, structured JSON logging, rate limiting, CORS, health check endpoint, OpenAPI/Swagger documentation, multi-stage Dockerfile, and docker-compose with PostgreSQL and Redis. Supports Fastify 5, NestJS, Express, FastAPI, Django REST, Gin, Chi, Echo, and Rails -- auto-detects framework from context. Build a backend, create an API, generate server, scaffold REST service, new backend project.

Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.

Generate integration tests for APIs, databases, and external services. Auto-detects backend stack (Express, Fastify, NestJS, Django, FastAPI, Rails, Go), ORM (Prisma, TypeORM, SQLAlchemy, GORM), and database (PostgreSQL, MongoDB, Firestore), then creates tests covering endpoint CRUD, auth flows, DB transactions, and external service failures with proper fixtures and mocks. Self-heals failing tests up to 3 iterations. Use when you need to test API endpoints end-to-end, verify database operations, test service interactions, or validate auth and error handling.

Generate consumer-driven contract tests for APIs. Auto-detects framework (Express, Fastify, NestJS, Django, FastAPI, Go), selects Pact, OpenAPI validation, or schema snapshots, verifies backward compatibility, catches breaking changes before deploy, and configures CI verification. Use when you need to validate API schemas, prevent breaking changes, verify backward compatibility, or set up consumer-driven contracts.

Create clustered heatmaps with row/column annotations using ComplexHeatmap, pheatmap, and seaborn for gene expression and omics data visualization. Use when visualizing expression patterns across samples or identifying co-expressed gene clusters.

Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.

Production-ready single-cell and expression matrix analysis using scanpy, anndata, and scipy. Performs scRNA-seq QC, normalization, PCA, UMAP, Leiden/Louvain clustering, differential expression (Wilcoxon, t-test, DESeq2), cell type annotation, per-cell-type statistical analysis, gene-expression correlation, batch correction (Harmony), trajectory inference, and cell-cell communication analysis. NEW: Analyzes ligand-receptor interactions between cell types using OmniPath (CellPhoneDB, CellChatDB), scores communication strength, identifies signaling cascades, and handles multi-subunit receptor complexes. Integrates with ToolUniverse gene annotation tools (HPA, Ensembl, MyGene, UniProt) and enrichment tools (gseapy, PANTHER, STRING). Supports h5ad, 10X, CSV/TSV count matrices, and pre-annotated datasets. Use when analyzing single-cell RNA-seq data, studying cell-cell interactions, performing cell type differential expression, computing gene-expression correlations by cell type, analyzing t

Expert knowledge for Azure Dedicated HSM development including troubleshooting, decision making, architecture & design patterns, security, and deployment. Use when designing HSM topologies, ExpressRoute IP configs, HA/failover, secure VNets, or migrating to Managed/Cloud HSM, and other Azure Dedicated HSM related development tasks. Not for Azure Cloud Hsm (use azure-cloud-hsm), Azure Key Vault (use azure-key-vault), Azure Payment Hsm (use azure-payment-hsm).

Workflow from differential expression results to functional enrichment analysis. Covers GO, KEGG, Reactome enrichment with clusterProfiler and visualization. Use when taking DE results to pathway enrichment.

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.

Differential abundance and state analysis for cytometry data. Compare cell populations between conditions using statistical methods. Use when testing for significant changes in cell frequencies or marker expression between groups.

Score and prioritize neoantigens and epitopes for immunogenicity using multi-factor models combining MHC binding, processing, expression, and sequence features. Rank candidates for vaccine design. Use when prioritizing epitopes for vaccine development or identifying the most immunogenic neoantigens.

RNA-seq specific quality control including rRNA contamination detection, strandedness verification, gene body coverage, and transcript integrity metrics. Use when validating RNA-seq libraries before differential expression analysis.

Expert knowledge for Azure Peering Service development including best practices. Use when designing Peering Service prefixes, routing policies, prefix validation rules, or connectivity constraints, and other Azure Peering Service related development tasks. Not for Azure Internet Peering (use azure-internet-peering), Azure Virtual Network (use azure-virtual-network), Azure Virtual WAN (use azure-virtual-wan), Azure ExpressRoute (use azure-expressroute).

Perform differential expression analysis using edgeR in R/Bioconductor. Use for analyzing RNA-seq count data with the quasi-likelihood F-test framework, creating DGEList objects, normalization, dispersion estimation, and statistical testing. Use when performing DE analysis with edgeR.