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概要と使いどころ
Improve interface resilience through better error handling, i18n support, text overflow handling, and edge case management, making interfaces robust and production-ready. Use when the user asks to harden, make production-ready, handle edge cases, add error states, or fix overflow and i18n issues.
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。
Improve how interfaces use photos, screenshots, icons, illustrations, and user-uploaded media so visuals support clarity instead of undermining it. Use when the user mentions images, screenshots, background photos, hero images, icon sizing, user-uploaded media, crops, overlays, or image readability.
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。
Design or improve data visualizations, charts, and data presentation interfaces. Use when the user asks to add charts, build dashboards, visualize data, choose chart types, make data accessible, or present metrics and analytics.
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。
Plan, implement, or improve an internationalization and localization strategy for UI content, formatting, and regional adaptation. Use when the user asks to add i18n, localize, translate, support multiple languages, handle regional formats, manage locale switching, or build a multilingual product.
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。
Design and build cinematic websites, interactive portfolios, product stories, and real-time 3D experiences with subject-specific art direction and scroll choreography. Use for new builds, redesigns, motion improvements, scroll audits, or storyboards in standalone HTML and existing apps; includes responsive, reduced-motion, and static fallbacks. Not for ordinary dashboards or unrelated animation.
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 Claude Code in autonomous iteration loops until task completion — PRD-based feature development, test coverage improvement, lint fixing, documentation generation, dataset creation, or framework migrations. Uses in-session loop mechanism with specialized templates. Triggers on autonomous loop, iterate until done, run until passing, keep going until, ralph, PRD execution.
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。
Apply React and Next.js performance optimization guidelines from Vercel Engineering — 40+ rules covering components, data fetching, bundle optimization, and rendering. Triggers on React components, Next.js pages, bundle optimization, performance improvements, refactoring React 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。
Audits and scores Claude Projects using a six-dimension Scorecard with anchored 1-5 rubrics and detects seven structural anti-patterns. When global layer info is provided, adds cross-layer alignment findings. Use when user says "audit my project," "review my custom instructions," "score my project," "evaluate my Claude project," "improve my system prompt," "what's wrong with my project," "why is my project underperforming." Also trigger on symptom-phrased: "my project doesn't work right," "Claude is inconsistent in my project," "my Project used to work and now doesn't." Also use when the user pastes Custom Instructions asking why output is poor or generic. Do NOT use when the user's primary request is a global-layer audit (use rootnode-global-audit if available), single-prompt evaluation (use rootnode-prompt-validation if available), or Memory-only optimization (use rootnode-memory-optimization 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。
Analyzes Claude Project context budget under the automatic-RAG-by-window model: knowledge files vs. threshold-exempt overhead (Skills, MCPs, CI, Memory). Two modes: Quick Diagnostic and Full Budget Audit. Use when user says "check my context budget," "how much context am I using," "is my project too big," "optimize my token usage," "tier my files," "optimize for RAG," "improve retrieval quality," "should I keep compressing," "am I over-compressing," "should I accept RAG mode." Also trigger on context pressure symptoms: "Claude forgets my instructions," "responses getting generic," "content not found in my knowledge files." Also use when a project audit scores Knowledge Architecture ≤ 3. Do NOT use for content placement decisions (use rootnode-memory-optimization if available), full project audits (use rootnode-project-audit if available), or behavioral tuning (use rootnode-behavioral-tuning 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。
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。
Evaluates and scores Claude prompts and system prompts using a six-dimension Scorecard with anchored 1-5 rubrics. Trigger on: "review my prompt," "score this prompt," "rate my prompt," "evaluate this prompt," "improve my prompt," "what's wrong with my prompt." Also trigger on: "why isn't my prompt working," "my prompt produces bad output," "help me fix this prompt," "is this prompt any good." Scorecard covers Objective Clarity, Context Specificity, Reasoning Fit, Output Precision, Behavioral Calibration, and Architectural Efficiency, plus a five-question Output Evaluation Rubric for assessing actual output. Also use when the user pastes a system prompt and asks for feedback. Activate whenever structured quality assessment of a single prompt is the primary need. Do NOT use for project-level audits involving Custom Instructions architecture, knowledge file organization, or multi-file Project structure (use rootnode-project-audit if available).
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。
Assemble the human genetic evidence for and against a target before a programme commits to it — the evidence class that most improves the odds of surviving clinical development. Use this skill to pull gnomAD constraint metrics (LOEUF, pLI, observed/expected) that show whether loss of function is tolerated in people, retrieve GWAS Catalog associations and fine-mapped credible sets for a gene, and read a natural human knockout as a safety readout. Also trigger on gnomAD, LOEUF, pLI, loss-of-function intolerance, mutational constraint, GWAS Catalog, credible set, human knockout, genetic support, or target safety dossier.
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 new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
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 or improve documentation based on the current conversation. Use this when a plan has been created and changes were requested because it didn’t follow the repository’s conventions.
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。
Assess and improve garden soil through testing, amendment, composting, and biodynamic preparations. Covers jar test, spade test, earthworm count, amendment by soil type (clay, sandy, depleted, compacted), composting methods (hot, cold, vermicomposting), no-till practices, cover cropping, and biodynamic preparations 500-508. Use when starting a new garden bed, when plants underperform despite adequate water and light, when transitioning to organic or biodynamic practice, when soil has become compacted or depleted, or when building a composting system.
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。
Review software architecture for coupling, cohesion, SOLID principles, API design, scalability, and technical debt. Covers system-level evaluation, architecture decision record review, and improvement recommendations. Use when evaluating a proposed architecture before implementation, assessing an existing system for scalability or security, reviewing ADRs, performing a technical debt assessment, or evaluating readiness for significant scale-up.
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。
Design and execute chaos engineering experiments using Litmus or Chaos Mesh. Test system resilience through controlled fault injection, validate hypothesis-driven tests, and improve failure recovery. Use before major product launches, after architecture changes to validate resilience, during GameDays or disaster recovery drills, to validate assumptions about failure modes, or as part of an SRE maturity program.
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。
Review web design for layout quality, typography, colour usage, spacing, responsive behaviour, brand consistency, and visual hierarchy. Covers design principles evaluation and improvement recommendations. Use when reviewing a design mockup before development, assessing an implemented site for design quality, providing feedback during a design review session, evaluating brand consistency, or checking responsive behaviour across breakpoints.
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。
Guide a person in developing active listening skills. AI coaches receptive mindset preparation, full-presence attending, reflective paraphrasing, clarifying questioning, synthesis of understanding, and integration of insights into communication practice. Use when a person wants to improve communication, when someone keeps misunderstanding others or feels misunderstood, when preparing for a difficult conversation such as a feedback session or conflict resolution, or when a person notices they talk more than they listen.
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。
Improve an existing R-based pictogram glyph for the visualization layer. Covers visual audit of the current glyph, diagnosis of specific issues (proportions, readability, glow balance), targeted modifications to the glyph function, re-rendering, and before/after comparison. Works for skill, agent, and team glyphs. Use when a glyph renders poorly at small sizes, its visual metaphor is unclear, it has proportion issues, the neon glow effect is unbalanced, or after adding new palettes or changing the rendering pipeline.
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。
Conduct a blameless post-mortem analysis after an incident. Build timeline reconstruction, identify contributing factors, and generate actionable improvements. Focus on systemic issues rather than individual blame. Use after any production incident or service degradation, following a near-miss, when investigating recurring issues, or to share systemic learnings across teams.
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。
Coach the user through a rigorous, repository-grounded interview to create or improve program.md. Use when a target repository lacks program.md, its goals, metrics, data, or guardrails are unclear, or the user asks to be grilled about an autoresearch setup.
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 or improve a Senpai target repository's program.md. Use this skill whenever the user wants to point Senpai at a fresh ML or research target repository, define the research objective, primary metric, benchmark contract, allowed edit boundaries, W&B reporting contract, or prepare a repo for autonomous advisor/student experiment loops.
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。
Review code for quality, bugs, and improvements. Use when user wants code review or quality assessment.
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。