2.8K skills · sorted by relevance
Build or review NestJS backends with clear module boundaries, request validation, access controls, and consistent error handling. The skill guides TypeScript service structure, environment configuration, database integration, and testing for production use.
Build data quality checks with Great Expectations, dbt tests, and data contracts, which define shared expectations between teams. Check missing values, duplicates, and freshness, then integrate validation into data pipelines and CI/CD.
Review source code for MCP, a protocol connecting AI applications to tools. Check authentication, input validation, unsafe execution, and other security risks, then report findings with file and line references and separate manual follow-ups.
Identify checks and changing global state that block fuzzing, then adjust them in fuzzing builds. The skill covers targeted validation bypasses, repeatable behavior, coverage comparisons, and checks for false crash reports introduced by patches.
[omh] Unvalidated early product idea: test whether a customer problem, segment, and business hypothesis deserve product investment, ending in kill, pivot, persevere, or inconclusive before any PRD. Use when the user says: product-discovery-validation, product discovery validation, product discovery, customer discovery, customer discovery plan, zero to one validation, validate the problem before building, problem solution interview.
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Extends a mechanistic or association-level biomedical finding into a staged validation pathway that moves from descriptive evidence toward stronger functional support, mechanistic specificity, and clinical relevance. Use this skill when a user has a pathway, biomarker, cell-state, target, mechanism, or association finding and needs to decide what should be validated next, in what order, and which evidence layers are necessary versus optional. Do not default to maximal validation stacks. Build a structured validation ladder with a primary route, stronger upgrade route, and optional extensions.
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Generates complete comorbidity-oriented shared-biomarker bioinformatics research designs from a user-provided disease pair and validation direction. Use when a study links two clinically related diseases through shared DEGs, enrichment, PPI hub genes, machine-learning feature selection, public diagnostic validation, gene-regulatory networks, immune infiltration, and optional downstream follow-up. Covers five study patterns (shared-DEG discovery, hub-gene prioritization, machine-learning biomarker selection, immune/regulatory interpretation, multi-layer validation) and always outputs Lite / Standard / Advanced / Publication+ with a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path, and strictly verified literature retrieval.
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Generates complete NHANES-style cross-sectional epidemiology + retrospective clinical validation research designs from a user-provided disease and biomarker direction. Always use this skill whenever a user wants to design, plan, or build a population-level biomarker association study using NHANES or similar survey datasets, especially when the article logic includes disease definition, biomarker formula derivation, multivariable logistic regression, restricted cubic spline analysis, subgroup stability testing, and a secondary hospital-based retrospective validation cohort. Covers five study patterns (cross-sectional association, dose-response / RCS, subgroup-stability, NHANES + retrospective validation, preliminary screening-performance) and always outputs four workload configs (Lite / Standard / Advanced / Publication+) with recommended primary plan, step-by-step workflow, figure plan, validation strategy, minimal executable version, publication upgrade path...
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Generates complete process-related diagnostic biomarker bioinformatics research designs from a user-provided disease context, gene-family or pathway theme, and validation direction. Use when a study centers on process-related genes, DEG and WGCNA integration, machine-learning feature selection, nomogram-based diagnostic modeling, immune infiltration, regulatory-network analysis, and optional external or experimental validation. Covers five study patterns (process-DEG discovery, co-expression-module integration, machine-learning biomarker selection, diagnostic model/nomogram workflow, immune-regulatory interpretation and validation) and always outputs Lite / Standard / Advanced / Publication+ with a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path, and strictly verified literature retrieval.
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Creates reusable Zod v4 schemas, validates API payloads, forms, and configuration input, transforms and coerces data safely, and handles validation errors with strong type inference for TypeScript applications. Use when designing validation layers, parsing `z.string()`, `z.object()`, or `z.email()` schemas, or implementing runtime type-safe data validation.
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TypeScript-first schema validation and type inference. Use for validating API requests/responses, form data, env vars, configs, defining type-safe schemas with runtime validation, transforming data, generating JSON Schema for OpenAPI/AI, or encountering missing validation errors, type inference issues, validation error handling problems. Zero dependencies, compact core (~5kb gzipped; zod/mini ~1.9kb).
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Define runtime-validated types with ArkType — TypeScript's 1:1 validator. Use when someone asks to "validate data in TypeScript", "runtime type checking", "ArkType", "type-safe validation", "alternative to Zod", "faster validation library", or "validate with TypeScript syntax". Covers type definitions, validation, morphs (transforms), scopes, and comparison with Zod.
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Generates FluentValidation validators for commands and queries. Includes common validation rules, custom validators, async validation, and integration with MediatR pipeline behaviors.
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Use when building or reviewing substantial forms with React Hook Form, Zod schemas, typed validation, dynamic fields, controlled third-party inputs, file upload, multi-step flows, dependent validation, or form performance. Do not use for trivial 1-3 field forms without validation; Chinese triggers include form, form validation, dynamic fields.
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Redis caching patterns, cache-aside, write-through, TTL strategies, and invalidation. Activate on: caching, Redis, cache invalidation, cache-aside, write-through, TTL, CDN cache, stale-while-revalidate. NOT for: CDN/reverse proxy setup (use api-gateway-reverse-proxy-expert), database query optimization (use data-warehouse-optimizer).
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Exploratory data-migration validation and QA ideation workflow. Use when you need to discover hidden risks, generate new validation angles, and converge mapping specs/validation reports into a prioritized QA backlog for CRM migrations. Version 2.0, updated 2025-12-28.
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Implement API schema validation using OpenAPI specifications and JSON Schema to enforce input/output contracts and prevent injection, data exposure, and mass assignment attacks.
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Implements input and output validation guardrails for LLM-powered applications to prevent prompt injection, data leakage, toxic content generation, and hallucinated outputs. Builds a security validation pipeline using NVIDIA NeMo Guardrails Colang definitions, custom Python validators for PII Tespit and content policy enforcement, and the Guardrails AI framework for structured output validation. The guardrails system intercepts both user inputs (blocking injection attempts, stripping PII, ...
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Redis caching patterns, cache-aside, write-through, TTL strategies, and invalidation. Activate on: caching, Redis, cache invalidation, cache-aside, write-through, TTL, CDN cache, stale-while-revalidate. NOT for: CDN/reverse proxy setup (use api-gateway-reverse-proxy-expert), database query optimization (use data-warehouse-optimizer).
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Review Perl code that handles user input, files, external commands, databases, and web output. Use concrete examples and a security checklist to identify unsafe patterns and apply input validation, parameterized queries, and appropriate output encoding.
Implement and review security measures in Quarkus applications. Covers sign-in, access permissions, input validation, password hashing, secret storage, request limits, audit logs, and checks for known vulnerabilities in dependencies, with implementation and configuration examples.
Build and maintain MCP servers that let AI assistants use tools, read data, and access reusable prompts. Covers Node/TypeScript implementation, input validation, transport choices for local or remote clients, and registration or connection troubleshooting.
Build clinical decision support for electronic medical records, covering drug interactions, dose validation, clinical risk scores, and alerts. Provides patterns for testable checking functions, severity-based interface behavior, and safety tests that detect missed warnings.
Create acceptance criteria and a manual validation plan for feature changes. Cover successful flows, unusual inputs, failures, and performance limits, with observable outcomes and clear evidence requirements so reviewers can decide whether each criterion passes.