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Found 27 Skills
Best practices for contributing code to TensorRT-LLM. Covers the official contribution process (issue tracking, fork workflow, DCO signing), coding guidelines, implementation workflow, common mistakes, testing strategy, commit hygiene, and review readiness. Incorporates rules from CONTRIBUTING.md and CODING_GUIDELINES.md plus lessons distilled from real PR retrospectives. Use when implementing new features, optimizations, or bug fixes in the TensorRT-LLM codebase.
Executes an implementation plan task-by-task with validation at every step. Use when you have a completed feature plan and want to implement it in one pass.
Manage major dependency version upgrades with compatibility analysis, staged rollout, and comprehensive testing. Use when upgrading framework versions, updating major dependencies, or managing breaking changes in libraries.
Modern TypeScript project architecture guide for 2025. Use when creating new TS projects, setting up configurations, or designing project structure. Covers tech stack selection, layered architecture, and best practices.
Creates test documentation (testing-strategy.md + tests/README.md). Establishes testing philosophy and Story-Level Test Task Pattern. L2 Worker in ln-100-documents-pipeline workflow.
Expert senior-level Go guidance for architecture, API-first design/codegen, advanced concurrency, performance tuning, testing/quality, cloud-native 12-factor practices, and Go 1.24+ tooling for large-scale systems.
Complete workflow for developing new features from design to deployment. Use when starting a new feature, adding functionality, or building something new.
Create detailed technical specifications, requirements documents, design documents, and system architecture specs. Use when writing technical specs, requirements docs, or design documents.
Use when creating implementation plans for features or tasks. Focuses on tactical execution planning with clear tasks, dependencies, and success criteria.
Implement a full-stack feature following an approved plan. Builds database layer, backend API, frontend components, and tests. Use after plan-feature has produced an approved implementation plan, or when you have a clear set of tasks to implement across the stack.
Python skill router. Use when planning, implementing, or reviewing Python changes and you need to select focused skills for workflow, design, typing/contracts, reliability, testing, data/state, concurrency, integrations, runtime operations, or notebook async behavior.
Architecture principles, module boundaries, folder structure, and project type profiles