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概要と使いどころ
Assesses post-emergence equity with clean balance sheet analysis, improved capital structure, and re-rating potential. Use when evaluating post-reorg equity, analyzing emergence opportunities, or assessing restructured company value.
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All-in-one fullstack dev engine. /aura: 46 modes (build/fix/clean/deploy/review/spec/lore/ax/experiment/payment/debug/qa/orchestrate/escalate+), 6-layer security with 32 hooks, tiered models (ZERO/ECO/PRO/MAX), 8 languages, 16 specialized agents, SPEC/EARS/TRUST5/XLOOP/RALF/Autopus absorbed. ~55% token savings.
日本語の概要は準備中です。原文の説明を表示しています。
AI-assisted debugging specialist with deep knowledge of modern debugging tools, observability platforms, and automated root cause analysis. Implements Cursor Debug Mode methodology — structured hypothesis ranking, targeted code instrumentation, human-in-the-loop reproduction gate, log-confirmed root cause, and mandatory cleanup.
日本語の概要は準備中です。原文の説明を表示しています。
Feature flag lifecycle management -- safe feature toggling, gradual rollouts, A/B testing patterns, flag cleanup strategies, and technical debt prevention. Covers LaunchDarkly, Unleash, OpenFeature, and custom implementations.
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Applies best practices for logging (zerolog/zap), project structure (cmd/internal/pkg), middleware registration order, and environment configuration in Go Fiber v2/v3 applications.
日本語の概要は準備中です。原文の説明を表示しています。
Specifies the preferred syntax for asynchronous operations using async/await and onMount for component initialization. This results in cleaner and more readable asynchronous code.
日本語の概要は準備中です。原文の説明を表示しています。
Comprehensive Android development expert covering Jetpack Compose, Kotlin coroutines/Flow, Architecture Components, Hilt DI, Navigation, testing, performance, Material Design 3, and modern Android patterns (MVI, Clean Architecture).
日本語の概要は準備中です。原文の説明を表示しています。
Code quality expert including clean code, style guides, and refactoring
日本語の概要は準備中です。原文の説明を表示しています。
After an issue's PR has squash-merged on GitHub and its issue has closed: fast-forward the default branch, remove the issue-numbered nested worktree (.worktrees/<issue#>), and delete the local feature branch. Use when the user says to finish, tear down, or clean up a lane or worktree for a merged issue.
日本語の概要は準備中です。原文の説明を表示しています。
After /start-task: park the current clean, pushed feature branch in its own issue-numbered nested worktree (.worktrees/<issue#>) and return the primary checkout to current main, so the next parallel lane can start. Use when the user says to move the branch or task to a worktree or open a parallel lane.
日本語の概要は準備中です。原文の説明を表示しています。
Remove the git worktrees open-pr review checked PR code out into, each a full checkout on disk. Use when reclaiming disk space after reviews. Asks first; takes no URL.
日本語の概要は準備中です。原文の説明を表示しています。
Run the open-pr e2e fixture through a real review in a fresh subagent, grade it against e2e/checklist.md with a second independent subagent, diagnose each failure to the prompt file that owns it, fix, and repeat until clean or the round budget runs out. Use when changing anything under src/ and you want evidence the review still behaves, not just green unit tests.
日本語の概要は準備中です。原文の説明を表示しています。
Complete feature development with structured options for merge, PR, or cleanup
日本語の概要は準備中です。原文の説明を表示しています。
Enforce Biome formatting and lint discipline in this repository. Use when making any file edits, when asked to run format/lint, and immediately before any commit. Always execute `bun run lint` (from package.json), apply resulting fixes, and re-run until clean. Treat `bun run lint:links` as optional by default, and recommend it when files under `content/` are changed.
日本語の概要は準備中です。原文の説明を表示しています。
Create standout, technically ambitious interfaces that feel extraordinary through cinematic transitions, advanced motion, heavy rendering, or other high-ambition implementation work. Use when the user wants to wow, impress, go all-out, or push a feature beyond conventional UI polish—not when simple visual confidence or routine cleanup is enough.
日本語の概要は準備中です。原文の説明を表示しています。
Add moments of joy, personality, and surprise through success states, empty states, loading moments, hover details, and celebratory polish once the fundamentals already work. Use when the user wants delight, charm, personality, or memorable micro-interactions—not when the main need is baseline cleanup or stronger visual impact.
日本語の概要は準備中です。原文の説明を表示しています。
Dev-Critic loop: implement, review, fix — repeat until clean
日本語の概要は準備中です。原文の説明を表示しています。
Use when EZ_math_model needs to read, preview, clean, write, or summarize CSV, XLS, or XLSX attachments and result tables for modeling tasks.
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This skill should be used when the user wants to capture a web page / article / docs into their second brain vault as clean Markdown — stripping ads, navigation, and sidebars to save tokens. Trigger on "clip this URL", "save this article to my vault", "ingest this page into raw/", or when gathering source material for the knowledge base.
日本語の概要は準備中です。原文の説明を表示しています。
Scrape web pages to clean markdown using Firecrawl v2 — handles JS-heavy pages, site crawls, URL mapping, document parsing (PDF/DOCX/XLSX), LLM-powered extraction, autonomous agent scraping, and post-scrape browser interaction (Interact API). Free keyless mode available for search/scrape/parse/interact (no API key needed). Prefer over WebFetch for quality and completeness. Triggers on scrape URL, fetch page, crawl site, extract content, parse document, web to markdown, DeepWiki, Firecrawl.
日本語の概要は準備中です。原文の説明を表示しています。
Enable, disable, and manage Claude Code skills. Use when the user asks to enable/disable/toggle/manage skills, check skill status, list skills, manage skill collections, or clean up the skills directory.
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Strip AI writing patterns from prose across five registers — casual, professional, academic, technical, and narrative. Applies 200+ pattern rules to eliminate predictable LLM tells. Triggers on clean up writing, remove AI slop, edit prose, make this sound less AI.
日本語の概要は準備中です。原文の説明を表示しています。
Audits and optimizes the five global Claude layers (User Preferences, Styles, Global Memory, Skills, MCP Connectors) using the Global Layer Scorecard (six dimensions, anchored 1-5 rubrics). Detects eight cross-layer failure modes and produces evolutionary recommendations (Promotion, Demotion, Codification, Skill Extraction). Use when user says "audit my global setup," "optimize my preferences," "review my Claude configuration," "check my cross-project setup," "are my preferences working," "clean up my global memory," or "what should be in my preferences vs my project." Also use when a user has 3+ Projects and wants to improve their shared foundation. Do NOT use for single-Project audits, Project Memory optimization, or full-stack audits (use rootnode-project-audit, rootnode-memory-optimization, or rootnode-full-stack-audit respectively, if available). Run on Opus 5 or Sonnet 5 at `high` effort (both defaults); depth reduces on legacy models.
日本語の概要は準備中です。原文の説明を表示しています。
Designs Claude Code prompts and CC environments. Produces CLAUDE.md drafts, agent topologies, scope-authorization frameworks, halt triggers, Skills/hooks/MCP plans, chat-to-CC handoff specs, and EXECUTION_PLAN.md remediation plans. Five modes: DESIGN (new CC deployments), EVOLVE (updates from friction), RESEARCH (evaluate a CC tool/pattern), TEMPLATE (reusable artifacts), REMEDIATE (consume hygiene findings → produce + execute plan). Do NOT use REMEDIATE for direct cleanup (Cat 1–10 — use rootnode-repo-hygiene Phase 2). Do NOT use for hygiene scanning (rootnode-repo-hygiene), chat prompts (rootnode-prompt-validation), or chat Projects (rootnode-project-audit).
日本語の概要は準備中です。原文の説明を表示しています。