Development skills

Call the OpenAI Platform API from the terminal via TypeScript on deno — chat completions, Responses API, reasoning (o-series), embeddings, image generation, audio (Whisper transcription, TTS), files, batches, moderation, and model listing. Two-tool stack: mise + deno (no node_modules, no pnpm, no bash). Models are resolved dynamically per session against `client.models.list()` — never hardcoded — and any detected upgrade prompts the user via AskUserQuestion (even in auto-mode). Secrets stay outside the agent: existence-only key checks, scoped `--allow-env=OPENAI_API_KEY` and `--allow-net=api.openai.com` flags, no `.env` files. Scoped to the Platform API specifically — for open-ended chat/conversation ("ask gpt", "chat with gpt") prefer the codex-server skill instead, which uses the user's ChatGPT subscription rather than API-key billing. Use this skill only when the request is explicitly about the Platform API surface (embeddings, image generation, audio, moderation, batches, files, or a chat/Responses call made programmatically as part of a script or pipeline). Trigger patterns (match any variation): openai api / openai platform api / open-ai api / embeddings / dall-e / gpt-image / image generation / generate image / whisper / transcribe audio / speech-to-text / tts / text-to-speech / read aloud / moderation / content moderation / openai cli / openai-cli / call openai api / use openai api / openai chat completions api / openai responses api / responses api / openai batch / openai files / openai embeddings api. Does NOT access ChatGPT chat history (the API does not expose it; use the Settings → Data Controls export instead). Does NOT handle open-ended chat/conversation requests — see codex-server for that.

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Run OpenAI Codex CLI from the terminal for batch / one-shot `codex exec` invocations with output captured to an `-o` file. Use for fire-and-forget runs, scripted CI, code review batches (`codex review`), and resuming a specific session by id (`codex exec resume`). Always use this skill instead of `mcp__codex__codex` and `mcp__codex__codex-reply` MCP tools — CLI is faster and more capable. For streaming UX, multi-turn dialogues that need live state, structured (JSON schema) output, attaching images, or programmatic event handling → **use codex-server instead**. codex-server uses the user's ChatGPT subscription via Codex App Server and decoupled async invocation that bypasses the Bash 2-minute timeout entirely. Trigger patterns (batch-only — chat/streaming triggers belong to codex-server): codex-cli / codex CLI / codex exec / codex review / codex resume / codex fork / batch + {codex, gpt} / one-shot + {codex, gpt} / non-interactive + codex / codex {to a file, -o, output file, captured output} / CI + {codex, gpt} / scripted + {codex, gpt} / fork codex session / resume codex session by id

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Review a teammate's pull request. The reviewer role (multi-lens analysis, severity calibration, suggestion writing) lives in the `pr-review:pr-review` subagent; this skill is the orchestrator — it resolves the PR, fetches diff and changed files, delegates analysis to the subagent, stages the resulting comments for user approval, then submits a batch review via the GitHub API. NOT for self-review of your own PR (use `self-pr-review` instead). Trigger phrases include "review PR #123", "code review PR", "peer review", "check this PR", "give PR feedback", "review pull request", "review someone's PR".

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Generate AGENTS.md (cross-tool agent context — Codex CLI, Aider, Cursor, etc.) and wire CLAUDE.md as a symlink so Claude Code reads the same file. Delegates content generation to Claude's built-in /init when needed, then renames + symlinks. Idempotent. Triggers — /agents-init, "generate agents.md", "create agents.md", "share claude and codex context", "set up agents file", "init agents", "agents-init", "cross-tool agent context", "wire claude and agents".

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Use @pchuri/jira-cli to view, search, create, update, and delete Jira issues, comments, sprints, and projects from the terminal. Prefer this skill over `mcp__claude_ai_Atlassian__*` MCP tools when you need: JQL search, sprint/board management, markdown export, file-based descriptions, issue deletion, comment management, or shell pipelines. Trigger phrases include "jira", "jira-cli", "jira issue", "create jira issue", "search jira", "jira sprint", "jira board", "jira project", "jira comment", "jira JQL", "list jira issues", "view jira issue", "jira backlog".

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Create project-local Claude Code skills (.claude/commands/) in any project directory. Analyzes the project's tech stack, structure, and conventions, then generates tailored local skills (custom slash commands) scoped to that project. Like /my-skill-factory but output stays local — no marketplace installation. Trigger patterns: - local skill / local command / project skill / project command - create local skill / make local skill / add local command - "make a skill for this project" / "create command for this repo" - suggest skills / suggest commands / what skills would help - update local skill / edit local command / modify project skill - /local-skill-factory

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Operate ONLY the user's private (personal) Google Drive via `rclone`, scoped to a single pinned remote (default `my-gdrive:`, override with the `MY_GDRIVE_REMOTE` env var). A safety wrapper around the `gdrive-cli` skill: refuses other remotes (e.g., the user's work/Workspace `tx-gdrive:`), strips Shared Drive and shared-with-me paths, and walks first-time users through OAuth setup with a personal Gmail account — including how to add a *second* rclone remote alongside an existing company one. Use whenever the user wants to act on their *private* / *personal* Drive specifically — not their work or shared Drive. Trigger phrases include "my gdrive", "my-gdrive", "my google drive", "my private google drive", "personal google drive", "personal gdrive", "private google drive", "private gdrive", "my private drive", "my personal drive", "personal drive", "private drive", "upload to my drive", "download from my drive", "backup to my google drive", "list my drive", "search my drive", "my drive quota", "sync to my gdrive", "use my personal google drive", "use my private google drive", "/my-gdrive".

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Use confluence-cli to read, search, create, update, move, and delete Confluence pages and attachments from the terminal. Prefer this skill over `mcp__claude_ai_Atlassian__*` MCP tools when you need: page export with attachments, copy-tree, attachment management, profile-based multi-instance access, read-only mode, CQL search, or shell pipelines. Trigger phrases include "confluence", "confluence-cli", "confluence page", "read confluence", "search confluence", "create confluence page", "update confluence page", "confluence attachment", "confluence spaces", "export confluence", "confluence comments", "confluence copy-tree".

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Review local git changes from 8 expert perspectives by spawning the matching reviewer subagents in parallel. Produces a consolidated report with Critical/Important/Nice-to-have severity. Lightweight pre-commit or pre-push quality gate — no PR or branch push required. Use when the user asks to review local changes, check changes before committing, get a team review of working tree changes, or run a pre-commit review. Trigger phrases include "review local", "review my changes", "review local changes", "pre-commit review", "review before commit", "review before push", "team review my changes", "check my changes", "review working tree", "local code review", "review diff", "review my diff".

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Operate Google Drive from the terminal via `rclone` — list, upload, download, search, sync, mount, check quota, export Google Docs/Sheets/Slides, browse Shared Drives, and manage trash. Use whenever the user mentions Google Drive, Drive folders, or anything stored in Google Workspace files from the command line. Trigger phrases include "google drive", "g drive", "gdrive", "drive cli", "upload to drive", "download from drive", "list drive", "search drive", "sync to drive", "backup to drive", "mount google drive", "mount drive", "check drive quota", "drive storage", "drive space", "google docs export", "shared drive", "shared with me", "drive trash", "empty drive trash", "google workspace files", "drive folder", "rclone drive", "rclone gdrive".

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Run OpenAI Codex via the Codex App Server (JSON-RPC-over-stdio) for streaming multi-turn chat sessions with persistent threads, structured output, image input, and rich event streams. Default entry point for ChatGPT/GPT/Codex-like conversations from the terminal. Uses the user's ChatGPT subscription (Plus / Pro / Team) exclusively via `codex login` — never consumes OPENAI_API_KEY billing, by design. Implementation: TypeScript on deno with npm:@openai/codex-sdk; spawns the existing system codex binary. Decoupled async invocation: every turn returns turn-id in <1 s; the actual SDK work runs in a detached worker that streams to per-turn files under ~/.codex-server/turns/<turn-id>/ — Bash's 2-min timeout never applies. Prefer this over codex-cli for streaming UX, multi-turn dialogues that need live state, structured (JSON schema) output, attaching images, or programmatic event handling. Use codex-cli only for one-shot batch invocations that pre-capture to an -o file. For OpenAI Platform API calls specifically (embeddings, image generation, audio, moderation, batches, files) — not open-ended chat — use openai-cli instead; that path bills OPENAI_API_KEY, this one uses the ChatGPT subscription. Trigger patterns (match any variation): codex / codex-server / codex app server / codex app-server / openai / open ai / OpenAI / chatgpt / chat gpt / ChatGPT / GPT / ask gpt / chat with gpt / gpt session / codex session / streaming chat / multi-turn chat / multi turn chat / second opinion / cross-check / cross check / ai pair programming / another AI / external AI / ask another model / get GPT's take / what does openai think / show me what gpt thinks / structured output from gpt / json schema gpt / resume codex thread / continue codex conversation / continue codex session / /codex-server

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Find deepening opportunities in a codebase, informed by the domain language in CONTEXT.md and the decisions in docs/adr/. Use when the user wants to improve architecture, find refactoring opportunities, consolidate tightly-coupled modules, or make a codebase more testable and AI-navigable.

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Swift の repo で、新しいメソッド (ヘルパー・初期化・値を組み立てる関数) を書く前に同じものが既にないかを dupscan index で引き、実装が終わったセルフレビューで dupscan gate を回して増えた写しを寄せる。関連の実装 (定義・参照・ファイルの中身・呼び出し元) を grep より先に探すときにも使う (def / refs / callers)。「dupscan」「重複したメソッド」「類似メソッドの統合」「既にあるヘルパーを探して」「セルフレビュー」「定義元」「参照」「呼び出し元」「似た関数」で発火。寄せるときはドメインの境界を確かめる (4 節)。Swift 以外の repo では使わない (対応する言語は Swift だけ)。

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Use when the user starts a cross-functional program, names milestones and owners, or asks what is at risk across workstreams and which gate can move.

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Use when selling media, sponsorships, or programmatic where the cycle runs plan to proposal to insertion order to measured renewal.

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Use when an account needs a running executive program: C-level connects, sponsorship gaps, briefings, and governance cadence.

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Use when the user names developers, API adoption, docs, SDKs, or a bottom-up motion: the developer motion plan with funnel, content spine, and conversion points.

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Master effective code review practices to provide constructive feedback, catch bugs early, and foster knowledge sharing while maintaining team morale. Use when reviewing pull requests, establishing review standards, or mentoring developers.

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Set up review and QA work from the repository, test setup, CI, release process, and merge rules.

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一時領域 (/private/tmp の Claude Code の作業場所・$TMPDIR・dotfiles の tmp/) に溜まったものを bin/tmpclean で数え、消す候補を種類ごとに見せて、ユーザーが承認した plan の古くて使用中でないものだけをゴミ箱へ移す (dotfiles の tmp/ は別の承認を取って make clean-tmp で消す)。「/tmp を掃除して」「一時ファイルを片付けて」「TMPDIR が溜まっていないか見て」「cleanup-tmp-dir」で発火。消す前に必ず承認を取る。

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Conventions for using in all JEE frameworks. These are common standards for CDI

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Conventions for using in all JVM languages

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Conventions for using sh, bash, bat, python scripts

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Conventions for using Java

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