Golang performance optimization patterns and methodology - if X bottleneck, then apply Y. Covers allocation reduction, CPU efficiency, memory layout, GC tuning, pooling, caching, and hot-path optimization. Use when profiling or benchmarks have identified a bottleneck and you need the right optimization pattern to fix it. Also use when performing performance code review to suggest improvements or benchmarks that could help identify quick performance gains. Not for measurement methodology (see golang-benchmark skill) or debugging workflow (see golang-troubleshooting skill).

Go (Golang) naming conventions — covers packages, constructors, structs, interfaces, constants, enums, errors, booleans, receivers, getters/setters, functional options, acronyms, test functions, and subtest names. Use this skill when writing new Go code, reviewing or refactoring, choosing between naming alternatives (New vs NewTypeName, isConnected vs connected, ErrNotFound vs NotFoundError, StatusReady vs StatusUnknown at iota 0), debating Go package names (utils/helpers anti-patterns), or asking about Go naming best practices. Also trigger when the user mentions MixedCaps vs snake_case, ALL_CAPS constants, Get-prefix on getters, or error string casing. Do NOT use for general Go implementation questions that don't involve naming decisions.

Idiomatic Golang error handling — creation, wrapping with %w, errors.Is/As, errors.Join, custom error types, sentinel errors, panic/recover, the single handling rule, structured logging with slog, HTTP request logging middleware, and samber/oops for production errors. Built to make logs usable at scale with log aggregation 3rd-party tools. Apply when creating, wrapping, inspecting, or logging errors in Go code.

Comprehensive documentation guide for Golang projects, covering godoc comments, README, CONTRIBUTING, CHANGELOG, Go Playground, Example tests, API docs, and llms.txt. Use when writing or reviewing doc comments, documentation, adding code examples, setting up doc sites, or discussing documentation best practices. Triggers for both libraries and applications/CLIs.

Idiomatic Golang design patterns — functional options, constructors, error flow and cascading, resource management and lifecycle, graceful shutdown, resilience, architecture, dependency injection, data handling, and streaming. Apply when designing Go APIs, structuring applications, choosing between patterns, making design decisions, architectural choices, or production hardening.

Golang data structures — slices (internals, capacity growth, preallocation, slices package), maps (internals, hash buckets, maps package), arrays, container/list/heap/ring, strings.Builder vs bytes.Buffer, generic collections, pointers (unsafe.Pointer, weak.Pointer), and copy semantics. Use when choosing or optimizing Go data structures, implementing generic containers, using container/ packages, unsafe or weak pointers, or questioning slice/map internals.

Idiomatic context.Context usage in Golang — creation, propagation, cancellation, timeouts, deadlines, context values, and cross-service tracing. Apply when working with context.Context in any Go code.

Golang concurrency patterns. Use when writing or reviewing concurrent Go code involving goroutines, channels, select, locks, sync primitives, errgroup, singleflight, worker pools, or fan-out/fan-in pipelines. Also triggers when you detect goroutine leaks, race conditions, channel ownership issues, or need to choose between channels and mutexes.

Golang code style, formatting and conventions. Use when writing code, reviewing style, configuring linters, writing comments, or establishing project standards.

Golang benchmarking, profiling, and performance measurement. Use when writing, running, or comparing Go benchmarks, profiling hot paths with pprof, interpreting CPU/memory/trace profiles, analyzing results with benchstat, setting up CI benchmark regression detection, or investigating production performance with Prometheus runtime metrics. Also use when the developer needs deep analysis on a specific performance indicator - this skill provides the measurement methodology, while golang-performance provides the optimization patterns.

GitHub Pull Request上でのレビュー操作を行うスキル。ghコマンドでPR情報取得、差分確認、コメント投稿・取得、インラインコメント、コメント返信を実行する。PRにコメントを投稿したい、差分を行番号付きで確認したい、レビューコメントに返信したいときに使用。

あらゆる git/GitHub 操作のエントリーポイント。commit する、PR を作る・更新する、rebase する、スタックを整理する、PR をレビューする、CI を直す — git や GitHub に関係する作業が発生したら、個別スキルを探す前に必ずこのスキルを参照すること。このスキルに従えば、どのスキルを使うか意識しなくても操作が完結する。「コミットして」「PR 出して」「最新に追いついて」「スタック整理して」「CI 直して」など、ユーザーの発言に git/GitHub のにおいがあればこのスキルを使う。

Use this to view the timeline of GitHub issues or pull requests. Note that it does not fetch the title and description, unlike `gh pr view`.

GitHub CLI の extension を使って `gh sub-issue` コマンド経由で Sub Issue を操作する Skill. You can connect existing issues as sub-issues to a parent issue,create new issues directly linked to a parent, unlink sub-issues from their parent without deleting the issues, and view all sub-issues connected to a parent issue.

Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications. Generates creative, polished code that avoids generic AI aesthetics.

GitHub Actions CI の失敗を調査して修正するためのスキルです。CI ログを分析して失敗箇所・原因を特定し、そのまま修正作業まで行います。

agent 向けテキスト指示（skill / slash command / task プロンプト / CLAUDE.md 節 / コード生成プロンプト）を、バイアスを排した実行者に動かしてもらい、両面（実行者の自己申告 + 指示側メトリクス）で評価して反復改善する手法。改善が頭打ちになるまで回す。プロンプトや skill を新規作成・大幅改訂した直後、またはエージェントの挙動が期待通りにならない原因を指示側の曖昧さに求めたいときに使う。

Always use when user asks to create, generate, draw, or design a diagram, flowchart, architecture diagram, ER diagram, sequence diagram, class diagram, network diagram, mockup, wireframe, or UI sketch, or mentions draw.io, drawio, drawoi, .drawio files, or diagram export to PNG/SVG/PDF.

Systematically explore and test a web application to find bugs, UX issues, and other problems. Use when asked to "dogfood", "QA", "exploratory test", "find issues", "bug hunt", "test this app/site/platform", or review the quality of a web application. Produces a structured report with full reproduction evidence -- step-by-step screenshots, repro videos, and detailed repro steps for every issue -- so findings can be handed directly to the responsible teams.

Datadog CLI (Go). OAuth2 auth with token refresh.

Monitor management - create, update, mute, and alerting best practices.

Log management - search, pipelines, archives, and cost control.

File GitHub issues to the right repository (pup CLI or plugin)

Datadog docs lookup using docs.datadoghq.com/llms.txt and linked Markdown pages.