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Found 2,772 Skills
Provides comprehensive guidance for Gin-Gonic framework including routing, middleware, validation, and best practices. Use when the user asks about Gin-Gonic, needs to create Go web applications, or implement Gin patterns.
Systematically discover and define your Ideal Customer Profile with firmographic criteria, buyer personas, scoring matrices, anti-ICP signals, and validation methodology.
Reduce controller bloat using Form Requests for auth/validation, small Actions/Services with DTOs, and resource/single-action controllers
Integration patterns for Mapbox MCP DevKit Server in AI coding assistants. Covers setup, style management, token management, validation workflows, and documentation access through MCP. Use when building Mapbox applications with AI coding assistance.
Create an actionable Laravel implementation plan—bite-sized tasks with TDD-first steps, migrations, services, jobs, and validation points
Migrate GPU/CUDA Triton operators to Triton-Ascend, or rewrite Python/PyTorch operators into Triton-Ascend implementations that can run on Ascend NPU. When clear optimization opportunities are identified, directly output the optimized code, minimal validation script, and troubleshooting instructions. This skill should be prioritized when users mention 昇腾 (Ascend), Ascend, NPU, triton-ascend, Triton operator migration, PyTorch operator rewriting, coreDim, UB overflow, 1D grid, physical core binding, block_ptr, stride, memory access alignment, mask performance, dtype degradation, operator optimization, or directly ask questions like "How to use this skill", "How to run it in the command line", "How to perform migration/validation in a container", even if users do not explicitly say "write a skill" or "perform migration".
Performs a comprehensive health check of a CockroachDB cluster. Gathers deployment context first, then provides tier-appropriate diagnostics. Self-Hosted uses SQL against node-level system tables and CLI. Advanced/BYOC use Cloud Console and SQL with node visibility. Standard monitors provisioned compute and workload via Cloud Console. Basic monitors Request Unit consumption and connectivity. Use for daily checks, pre-maintenance validation, post-incident verification, or production readiness assessment.
Generate REST API endpoints with proper structure, validation, error handling, and types. Use when creating new API routes, endpoints, or backend services.
Deliver Python backends across async FastAPI and Django or Flask service styles while keeping API design, validation, auth, and service behavior explicit.
Use when deploying your agent to AWS, or when a deploy has failed. Handles pre-flight validation, CDK/IAM/quota error diagnosis, version management, rollback, and canary deployments. Triggers on: "deploy my agent", "agentcore deploy", "deploy failed", "CDK error", "rollback", "canary deploy", "pin version", "redeploy", "deploy stuck". Not for production hardening — use agents-harden. Not for adding capabilities before deploy — use agents-build or agents-connect. Not for VPC configuration errors — use agents-build.
Submit and manage protocols on Ginkgo Bioworks Cloud Lab (cloud.ginkgo.bio), a web-based interface for autonomous lab execution on Reconfigurable Automation Carts (RACs). Use when the user wants to run cell-free protein expression (validation or optimization), generate fluorescent pixel art, or interact with Ginkgo Cloud Lab services. Covers protocol selection, input preparation, pricing, and ordering workflows.
Refactor Pandas code to improve maintainability, readability, and performance. Identifies and fixes loops/.iterrows() that should be vectorized, overuse of .apply() where vectorized alternatives exist, chained indexing patterns, inplace=True usage, inefficient dtypes, missing method chaining opportunities, complex filters, merge operations without validation, and SettingWithCopyWarning patterns. Applies Pandas 2.0+ features including PyArrow backend, Copy-on-Write, vectorized operations, method chaining, .query()/.eval(), optimized dtypes, and pipeline patterns.