dart-run-static-analysis 是做什麼的？
執行 dart analyze 以識別警告與錯誤，並使用 dart fix --apply 自動修復機械性的 lint 問題。在開發過程中用以確保程式碼品質，並在提交變更前執行。
執行 `dart analyze` 以識別警告與錯誤，並使用 `dart fix --apply` 自動修復機械性的 lint 問題。在開發過程中用以確保程式碼品質，並在提交變更前執行。
此技能專門針對 Dart 語言開發，透過執行 `dart analyze` 靜態分析工具，幫助開發者快速識別出原始碼中的警告（warnings）與錯誤（errors）。它能提升程式碼的可讀性與穩定性，尤其是在提交變更（commit）前使用，可有效防止有問題的程式碼進入版本控制。此外，技能還整合了 `dart fix --apply` 指令，能自動套用 Dart 官方推薦的 lint 規則修復，大幅減少手動修正機械性格式或風格問題的時間。該技能支援所有 Dart 與 Flutter 專案，且與命令行環境（如 Claude Code、Cursor 或 Codex）無縫整合，開發者只需下達對應指令即可觸發完整的靜態分析與修復流程。優點在於能自動化程式碼品質檢查流程，將 lint 問題的修復從繁瑣的手動操作中解放出來。
npx skills add https://github.com/dart-lang/skills/tree/main/skills/dart-run-static-analysisInstalled? Explore more 研究與資料分析 skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/literature-review · View all 6 →
執行 dart analyze 以識別警告與錯誤，並使用 dart fix --apply 自動修復機械性的 lint 問題。在開發過程中用以確保程式碼品質，並在提交變更前執行。
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.
Entrypoint structure, exit codes, cross-platform scripts. Use when building command line utilities, scripts, or applications.
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.
Use switch expressions and pattern matching where appropriate
USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.
Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.
Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.
The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
Agent skill repository discovered by 10x-chat research.
Review backend code for quality, security, maintainability, and best practices based on established checklist rules. Use when the user requests a review, analysis, or improvement of backend files (e.g., `.py`) under the `api/` directory. Do NOT use for frontend files (e.g., `.tsx`, `.ts`, `.js`). Supports pending-change review, code snippets review, and file-focused review.