1.4K skills · sorted by relevance
METAINFORMANT rules for directory examples/ml. Use when editing, adding tests, or reviewing code under this path. Read the linked AGENTS.md first; use uv only, write outputs to output/, real implementations.
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METAINFORMANT rules for directory examples/visualization. Use when editing, adding tests, or reviewing code under this path. Read the linked AGENTS.md first; use uv only, write outputs to output/, real implementations.
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METAINFORMANT rules for directory examples. Use when editing, adding tests, or reviewing code under this path. Read the linked AGENTS.md first; use uv only, write outputs to output/, real implementations.
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METAINFORMANT rules for directory examples/ecology. Use when editing, adding tests, or reviewing code under this path. Read the linked AGENTS.md first; use uv only, write outputs to output/, real implementations.
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METAINFORMANT rules for directory examples/simulation. Use when editing, adding tests, or reviewing code under this path. Read the linked AGENTS.md first; use uv only, write outputs to output/, real implementations.
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METAINFORMANT rules for directory examples/math. Use when editing, adding tests, or reviewing code under this path. Read the linked AGENTS.md first; use uv only, write outputs to output/, real implementations.
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METAINFORMANT rules for directory examples/quality. Use when editing, adding tests, or reviewing code under this path. Read the linked AGENTS.md first; use uv only, write outputs to output/, real implementations.
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Collapse meaningful ambiguity with contrastive examples. Use when a plan/design has several plausible meanings, requirements feel vague, examples would clarify intent faster than grilling, or the user asks to disambiguate, find ambiguity, use behavioral kernels, or ask contrastive questions.
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Normalizes documentation and examples for consistency in style, format, and accuracy. Ensures code matches existing patterns and documentation. Use when reviewing docs, examples, or writing new code that should follow established patterns.
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MUST USE for deploying Aerospike on Kubernetes. Contains CE-specific YAML templates, validated AerospikeCluster CR examples, and critical constraints that prevent enterprise-only config mistakes (feature-key-file, security sections crash CE pods). Without this skill, deployments fail on first attempt due to CE 8.1 breaking changes (data-size not memory-size, no info port 3003) or webhook map/list shape rules (service/network must be maps; logging must be a list). Triggers on: deploy/create/set up Aerospike on K8s, kind, minikube, EKS, GKE; AerospikeCluster CR; ACKO operator; spec.operations / WarmRestart / PodRestart YAML; NoSQL database on Kubernetes. 9 ready-to-use YAML examples from minimal single-node to full-featured multi-rack.
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Generates OpenAPI 3.0/3.1 specs, TypeScript client types, and curl examples from existing route handler code. Supports Express, Next.js API routes, Fastify, and Hono. Produces complete documentation including auth requirements, rate limits, error codes, and request/response examples. Activate on: 'generate API docs', 'OpenAPI from code', 'document endpoints', 'API reference', 'swagger generation', 'endpoint documentation'. NOT for: API design from scratch (use api-architect), deployment (use devops-automator), testing (use test-automation-expert).
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INVOKE THIS SKILL when creating, managing, or querying Arize datasets and examples. Covers dataset CRUD, appending examples, exporting data, and file-based dataset creation using the ax CLI.
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Check skills before distributing them to Codex or Cursor. Identify unsupported metadata fields, Claude-specific variables in shell examples, and inconsistencies between source skills and their installation copies using the project's compatibility checker.
Structure server-side applications with practical patterns for Node.js, Express, and Next.js API routes. Covers API design, database queries, caching, authentication, error handling, and background jobs, with examples that separate business rules from data access.
Review security measures in Java Spring Boot services, including identity checks, access permissions, input validation, and secret storage. Provides configuration examples and a release checklist covering API request limits, browser protections, logging, and dependency vulnerabilities.
Build and review SwiftUI interfaces for iOS and macOS with guidance on state management, reusable views, navigation, and rendering performance. Examples show how to organize data flow and reduce unnecessary screen updates.
Review authentication, payments, and APIs for security risks using implementation examples and a predeployment checklist. Covers input validation, access permissions, secret storage, request limits, and checks for sensitive information in logs and errors.
Build or review Java Spring Boot backends using examples for web APIs, separate application layers, and database access. Covers input validation, error handling, caching, background processing, and logging to guide maintainable implementations.
Build or review Java services with Quarkus 3.x using patterns for REST APIs, database access with Panache, and messaging with Apache Camel. Examples cover layered code structure, validation, transactions, and asynchronous processing, where work completes separately from the main flow.
Design and review Prisma database code for TypeScript backends. Use examples for schemas, queries, transactions, pagination, migrations, and connection management to identify common pitfalls in bulk operations and serverless deployments.
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.
Create videos in React with Remotion using focused implementation guidance and code examples. The skill covers animation, audio, captions, charts, 3D content, and scene transitions, along with handling media assets and timing.
Write, review, and modernize Perl 5.36+ applications using clear function parameters, focused error handling, and organized modules. Includes examples for classes, regular expressions, file operations, formatting, code checks, and dependency management.
Helps write and review PyTorch models, training loops, and data loading code. Covers repeatable experiments, saving training progress, and techniques for reducing GPU memory use or improving training speed, with practical code examples.