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概要と使いどころ
Deconstructs customer cohort behavior with retention curves, LTV progression, and vintage-over-vintage comparison analysis. Use when analyzing cohort data, assessing customer quality, or modeling lifetime value trajectories.
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。
Execute plans via autonomous agents with two-stage review per task. Use for complex implementations requiring quality gates.
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 CodeQL and Semgrep static analysis with SARIF output for vulnerability detection, code quality assessment, and security compliance scanning across multiple languages.
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 completed agent pipelines across 5 scoring dimensions and produces a composite verdict with actionable recommendations
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。
Independent LLM judge evaluates task completion separately from the executing agent, catching false success claims by reviewing task goal, actions taken, final state, and evidence. Produces PASS/FAIL with confidence score and reasoning.
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 distinctive, production-grade web interfaces with intentional aesthetic direction. Avoids generic AI aesthetics (centered layouts, Inter/Arial, purple gradients, uniform corners). Use for UI components, dashboards, landing pages, React/HTML/CSS layouts where design quality matters.
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。
Compress large context before reasoning to reduce token usage while preserving evidence. Use this whenever the user mentions huge files, long prompts, RAG payloads, prompt caching, expensive sessions, codebase context, chat history compaction, or wants the same answer quality with fewer tokens.
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。
Collaborative document creation via a structured three-stage workflow. Use for writing specs, PRDs, design docs, proposals, RFCs, and any long-form document where quality and clarity matter. Brainstorms 5-20 options per section, builds iteratively, and tests with reader sub-agents.
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。
Two-agent cleanup pattern. Implementer works freely, then a dedicated cleanup agent removes unused imports, console.logs, commented-out code, dead code, and formatting issues. Produces a diff of what was cleaned.
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 analyzing published content (blog posts, social media, newsletters) for sentiment, structural quality, hook effectiveness, readability, topic classification, and engagement correlation. Invoke for daily post analysis, content audits, or engagement driver identification.
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。
Generate context-aware quality checklists for code review and QA using IEEE 1028 base standards plus LLM contextual additions
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 you want to improve response quality through meta-cognitive reasoning. Applies 15+ reasoning methods to reconsider and refine initial outputs.
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。
N-round opposing-stance debates for trade-off analysis. Assigns pro/con roles to agents, runs structured debate rounds with quality scoring, and produces a moderator synthesis with confidence-rated recommendation. Generalizable to architecture, technology, security, and design decisions.
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。
Validate a bug bounty finding before writing a report — the 7-Question Gate, 4 pre-submission gates, always-rejected list, conditional chain table, CVSS 3.1 quick reference, and severity decision guide. Use BEFORE writing any report to avoid N/A and low quality submissions.
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。
Perform a final quality pass fixing alignment, spacing, consistency, and micro-detail issues before shipping. Use when the work is functionally complete and needs finishing touches—not when the hierarchy, structure, tone, or technical foundation still need major changes.
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 technical quality checks across accessibility, performance, theming, responsive design, and anti-patterns. Generates a scored report with P0-P3 severity ratings and actionable plan. Use when the user wants measurable accessibility, performance, responsive, theming, or anti-pattern findings—not when they mainly want an overall UX critique or final visual/detail polish.
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。
Evaluate an interface from a UX perspective, assessing hierarchy, information architecture, emotional resonance, cognitive load, and overall quality with quantitative scoring and actionable feedback. Use when the user wants an overall design or UX review—not when the main need is measurable accessibility/performance diagnosis, or final micro-detail polish.
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。
Deep single-page SEO analysis covering on-page elements, content quality, technical meta tags, schema, images, and performance. Use when user says "analyze this page", "check page SEO", or provides a single URL for review.
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。
Local SEO analysis covering Google Business Profile optimization, NAP consistency, citation health, review signals, local schema markup, location page quality, multi-location SEO, and industry-specific recommendations. Detects business type (brick-and-mortar, SAB, hybrid) and industry vertical (restaurant, healthcare, legal, home services, real estate, automotive). Use when user says "local SEO", "Google Business Profile", "GBP", "map pack", "local pack", "citations", "NAP consistency", "local rankings", "service area", "multi-location", or "local search".
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。
GEO+SEO audit of asdlc.io — analyzes the codebase for Generative Engine Optimization across AI citability, technical infrastructure, content quality, schema markup, and knowledge base discoverability. Produces a composite GEO Score (0-100) with prioritized action plan.
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 after EZ_math_model coding has produced src/*.py and before packaging when code quality, reuse, vectorization, naming, or unnecessary complexity should be reviewed without changing behavior.
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 after EZ_math_model writer creates paper.md and before quality audit when a long modeling paper needs less AI-like prose while preserving formulas, data, citations, tables, and figures.
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 before EZ_math_model declares quality_audit, packaging, or final delivery complete, especially when pass/fail claims must be backed by files, commands, counts, or concrete evidence.
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 solving CUMCM, MCM, ICM, or other mathematical modeling tasks that need contest problem intake, model selection, Python solving, figures, paper writing, quality audit, and packaged deliverables.
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。