菜单
Use when editing base prompts, tool descriptions, skill descriptions, subagent prompts, compaction summaries, or prompt wording experiments such as soft vs hard rules, compact language, mood, or persona framing.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Continuously modernize Golang code to use the latest language features, standard library improvements, and idiomatic patterns. Use this skill whenever writing, reviewing, or refactoring Go code to ensure it leverages modern Go idioms. Also use when the user asks about Go upgrades, migration, modernization, deprecation, or when modernize linter reports issues. Also covers tooling modernization: linters, SAST, AI-powered code review in CI, and modern development practices. Trigger this skill proactively when you notice old-style Go patterns that have modern replacements.
Provides a comprehensive guide for writing production-ready Golang tests. Covers table-driven tests, test suites with testify, mocks, unit tests, integration tests, benchmarks, code coverage, parallel tests, fuzzing, fixtures, goroutine leak detection with goleak, snapshot testing, memory leaks, CI with GitHub Actions, and idiomatic naming conventions. Use this whenever writing tests, asking about testing patterns or setting up CI for Go projects. Essential for ANY test-related conversation in Go.
Use when selecting, compacting, or reviewing repository context for an implementation, review, or investigation task. Helps build high-signal context packs from file trees, diffs, symbols, tests, docs, slices, and search hits while avoiding noisy full-repo reads.
Use when adding, removing, or changing permanent documentation, architecture decisions, examples, CLI commands, configs, skill docs, or maintenance guidance. Keeps docs indexed, non-duplicative, and useful for future agents.
Use when changing prompts, tools, context packing, provider behavior, permissions, subagents, search, or other agent quality-sensitive logic. Guides focused tests, SanityHarness runs, metric capture, and adoption decisions.
Use when adding or modifying model provider support, model profiles, streaming, tool-call normalization, reasoning effort, cost accounting, retries, rate limits, or secret handling.
| name | prompt-lab |
| description | Use when editing base prompts, tool descriptions, skill descriptions, subagent prompts, compaction summaries, or prompt wording experiments such as soft vs hard rules, compact language, mood, or persona framing. |
Treat prompts like code.
