dart-lang/dart-build-cli-app
Entrypoint structure, exit codes, cross-platform scripts. Use when building command line utilities, scripts, or applications.
Entrypoint structure, exit codes, cross-platform scripts. Use when building command line utilities, scripts, or applications.
npx skills add https://github.com/dart-lang/skills/tree/main/skills/dart-build-cli-appEntrypoint structure, exit codes, cross-platform scripts. Use when building command line utilities, scripts, or applications.
This repo contains 8 individual skills — each has its own dedicated page.
Write and organize unit tests for functions, methods, and classes using `package:test`. Use when creating new logic or fixing bugs to ensure code remains correct and regression-free.
Collect coverage using the coverage packge and create an LCOV report
Uses get_runtime_errors and lsp to fetch an active stack trace, locate the failing line, apply a fix, and verify resolution via hot_reload.
Define and generate mock objects for external dependencies using `package:mockito` and `build_runner`. Use when unit testing classes that depend on complex external services like APIs or databases.
Replace the usage of `expect` and similar functions from `package:matcher` to `package:checks` equivalents.
Workflow for fixing package version conflicts. Use this when `pub get` fails due to incompatible package versions.
Execute `dart analyze` to identify warnings and errors, and use `dart fix --apply` to automatically resolve mechanical lint issues. Use during development to ensure code quality and before committing changes.
Use switch expressions and pattern matching where appropriate
泰逢 · 动 agent 的天地气 —— 可嵌入的 Python LLM Agent 微内核(skill=markdown,LLM=调度器,cache-aware 压缩)。
Personal engineering configs and tooling: coding standards, Claude Code configs (CLAUDE.md), and skills.
CLI for bootstrapping and managing wordspace projects
Automate Renderform tasks via Rube MCP (Composio). Always search tools first for current schemas.
Provides chunking strategies for RAG systems. Generates chunk size recommendations (256-1024 tokens), overlap percentages (10-20%), and semantic boundary detection methods. Validates semantic coherence and evaluates retrieval precision/recall metrics. Use when building retrieval-augmented generation systems, vector databases, or processing large documents.
Mindful skills and agents for hybrid human/AI intelligence