Hideki Koike

Generate a persistent PROJECT-KNOWLEDGE.md profile for any codebase that gives subagents instant deep domain expertise. Analyzes project structure, architecture, code conventions, tech stack, data flow, and domain concepts. The profile is designed to be loaded into Agent tool prompts so subagents can answer deep codebase questions like an expert teammate. Use when the user asks to profile a project, generate project knowledge, create a codebase profile, build a context pack, analyze a codebase for subagents, or says "profile this project". Trigger phrases include "profile project", "generate project knowledge", "codebase profile", "subagent context", "knowledge profile", "project profile", "profile this codebase", "build project knowledge", "create context pack", "generate codebase knowledge", "index this project", "make subagent", "generate subagent", "deep knowledge", "project expert".

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Test-driven development with red-green-refactor loop. Use when user wants to build features or fix bugs using TDD, mentions "red-green-refactor", wants integration tests, or asks for test-first development.

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Turn a feature idea into an execution-ready document set through an adversarial PRD "council": grill the user for requirements, draft a PRD, then debate it with OpenAI Codex round-by-round until BOTH sides approve, and finally emit a Technical PRD (one overall summary + one per UseCase) plus a task list with agent assignments, dependencies, and acceptance criteria — designed so a PdM-role agent could later distribute the work to specialist agents. Document-generation only (no agent execution). Writes local files under docs/prd/<slug>/; never publishes to external trackers. Uses codex-server (the user's ChatGPT subscription) for the debate loop. Use when the user wants a PRD reviewed/approved by Codex, a "PRD council", a Technical PRD for task distribution, or a task breakdown from a PRD. Trigger patterns (match any variation): prd-council / prd council / PRD council / PRD + {Codex, GPT, ChatGPT} + {議論, 相互承認, レビュー, 承認, debate, review, approve} / CodexとPRDを議論 / CodexとPRDを相互承認 / PRDをCodexと詰める / technical prd / technical product requirement document / 全体サマリー + UseCase + PRD / ユースケース別PRD / タスク振り分けドキュメント / PdM agent task distribution doc / PRDからタスクリスト / break a PRD into tasks / task breakdown from PRD / /prd-council

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Orchestrate multiple coding-agent sessions (Claude Code, Codex, Gemini) over the mcp_agent_mail MCP server, observe their progress on a recurring auto-loop, and prevent inconsistencies (file/edit conflicts and contradictory decisions) between concurrent tasks. Auto-manages the agent-mail server, derives the shared room key from the git repo, and runs either as the orchestrator/observer or as a participating worker. Handles the operational details (server startup, room key, reservation conventions) so the user does not have to remember them. Use when coordinating several agent sessions on different tasks at once, watching a swarm of sessions for conflicts, or wiring already-running sessions into a shared coordination room. Trigger phrases: "agent-swarm", "/agent-swarm", "orchestrate agents", "orchestrate multiple claude sessions", "coordinate sessions", "watch for conflicts between agents", "observe the swarm", "prevent inconsistency between sessions", "multi-agent observe", "複数セッションを協調", "エージェント協調", "セッション間の不整合監視", "スウォーム監視", "複数のClaudeを束ねて".

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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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Drive an already-running, LOGGED-IN Chromium browser on macOS via AppleScript — navigate, click, fill forms, scroll, and extract page content from the user's REAL session, with the browser left open and NO profile copy and NO restart. Use when the task needs the existing logged-in session (cookies, auth, current tabs) reused as-is. Trigger phrases: "chrome-use", "ブラウザから取得", "ログイン済みの Chromeで", "ブラウザを操作して", "このページ読んで", "Xのスレッド読んで", "ログインしたまま取得", "自分のセッションでスクレイプ", "read this page in my browser", "scrape with my session", "drive my chrome", "execute JS in my (logged-in) browser", "automate my logged-in browser". Chromium family (Chrome default; Brave/Edge/Arc/Vivaldi via --app). NOT for isolated/throwaway automation where login reuse is unneeded — use playwright-cli for that. macOS only.

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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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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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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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Rewrites a task prompt one layer deeper, not bigger. Use only when the user asks for a deeper or expert-grade framing of a prompt they already phrased: /deepen followed by the task, "deeper not bigger", "deepen this prompt", "deepen my prompt", 「このプロンプトをもっと深く」「このプロンプトを深い版にして」「プロならどう頼むか」「大きくせず深く」. Before rewriting, answers four questions (Felix Rieseberg's Depth technique): what a top professional would do that is not being asked for; the rawest material they would start from (logs, DB rows, traces, git history, primary sources) instead of a digest; the specific instruments they would use; and what, other than the model or the user, shows the result is right. Then rewrites the prompt to ask for exactly that, adding no scope, and stops to ask before running it. Not for ordinary task execution, "look into this more deeply" requests, or enlarging scope.

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