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概要と使いどころ
Structured multi-criteria decision analysis for ranking options with weights, constraints, confidence, tradeoff reasoning, sensitivity analysis, and explainable recommendations. Use when the user asks for decision support, MCDA, weighted scoring, prioritization, vendor selection, route planning, hiring shortlist ranking, tool comparison, procurement decisions, or auditable agent decision logic.
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Generate marketing ad images using Nano Banana Pro (Gemini 3 Pro Image). Accepts campaign-planner creative briefs, reads brand bible for visual style, constructs marketing-optimized prompts, and produces platform-ready images at correct aspect ratios. Supports 1:1, 9:16, 16:9, 4:5 formats. Includes self-review loop to catch hallucinated logos, wrong text, and quality issues. Draft-first workflow (1K fast iteration, 4K final). Outputs to /tmp/marketing/assets/images/.
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Use this skill when users need to search academic papers, download research documents, extract citations, or gather scholarly information. Triggers include: requests to "find papers on", "search research about", "download academic articles", "get citations for", or any request involving academic databases like arXiv, PubMed, Semantic Scholar, or Google Scholar. Also use for literature reviews, bibliography generation, and research discovery. Requires OpenClawCLI installation from clawhub.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。
You are an academic writing expert specializing in scholarly papers, literature reviews, research methodology, and thesis writing. You must adhere to strict academic standards in all outputs.## Core Requirements1. **Output Format**: Use Markdown exclusively for all writing outputs and always wrap the main content of your response within <ama-doc></ama-doc> tags to clearly distinguish the core i...
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。
Professional LaTeX writing assistant. Capabilities include: scanning existing LaTeX templates, reading reference materials (Word/Text), drafting content strictly following templates, and compiling PDFs. Triggers include: 'write thesis', 'draft section', 'compile pdf', 'check latex format'. Designed to work in tandem with 'academic-research-hub' for citation retrieval.
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。
Transparent, rigorous research with full methodology — not a black-box API wrapper. Conducts exhaustive investigation through mandated 2-cycle research per theme, APA 7th citations, evidence hierarchy, and 3 user checkpoints. Self-contained using native OpenClaw tools (web_search, web_fetch, sessions_spawn). Use for literature reviews, competitive intelligence, or any research requiring academic rigor and reproducibility.
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。
Master TypeScript's advanced type system including generics, conditional types, mapped types, template literals, and utility types for building type-safe applications. Use when implementing complex type logic, creating reusable type utilities, or ensuring compile-time type safety in TypeScript projects.
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。
CLI tools for Svelte 5 documentation lookup and code analysis. MUST be used whenever creating, editing or analyzing any Svelte component (.svelte) or Svelte module (.svelte.ts/.svelte.js). If possible, this skill should be executed within the svelte-file-editor agent for optimal results.
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。
Activate when user expresses intent to start Sisyphus work (e.g., "okay then work like that", "let's execute this", "start working on this"). Extract ai-todolist.md content from GitHub comments/files, create branch, commit, and trigger GitHub Actions workflow. If no ai-todolist format found, load plan-writer skill instead.
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Auto-assembles a domain-specific expert panel (7–10 experts), scores any content or strategy artifact against a typed rubric, and iterates until the aggregate hits 90+ (max 3 rounds). Use as a quality gate on copy, email sequences, landing-page drafts, strategy docs, charts, titles, or recruiting evaluations — or when another skill needs a final review gate on its output. Triggers on "expert panel this", "score this", "rate these variants", "quality check this", "panel review", "expert score", "evaluate this copy/strategy/page". For variant generation and multi-round conversion optimization see autoresearch; for live-URL CRO auditing see conversion-ops; for the scripted content-production pipeline see content-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。
Use when reviewing or auditing a library of Claude Code skills, agents, slash commands, and workflows — frontmatter correctness, routing quality, tool allowlists, command arg-hints, workflow meta/phase coherence, cross-reference coherence, single-responsibility, file structure, and anti-pattern detection. Triggers on mentions of "review skills", "audit agents", "skill library", "agent definition review", "review this command", "review this workflow", "is this skill right", or when iterating on `.claude/skills/`, `.claude/agents/`, `.claude/commands/`, or `.claude/workflows/` directories. For code review of source code see code-review-and-quality.
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。
Scientific research engine for hypothesis testing, literature gap analysis, experimental validation, and data-driven discovery. Enforces adversarial review (Reviewer 2), 32 quality gates, tree search over hypotheses, confounder harness for quantitative claims, and serendipity detection. TRIGGER when: user asks to analyze scientific data, test hypotheses, validate findings, search for research gaps, design experiments, or investigate results. DO NOT TRIGGER when: pure code review, documentation writing, devops tasks, general conversation, or non-scientific data transformation.
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。
Scientific research engine with adversarial review, tree search, and serendipity detection. Use when: exploring hypotheses, validating findings against literature, running computational experiments with quality gates, or hunting for unexpected discoveries. Do NOT use for simple Q&A, code editing, or non-research tasks.
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。
Scientific research engine v6.0 NEXUS — adversarial review (Reviewer 2), 32 quality gates, tree search, serendipity tracking, confounder harness, cross-session learning. Use for ANY scientific analysis, hypothesis testing, data validation, literature review, or task where correctness > speed.
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。
Draft the QA scope and plain-language Given/When/Then test scenarios for a Jira ticket, built from the full ticket, the decisions made in its comments, and the actual code change read commit by commit (open PR, the ticket's merged PR, the branch against its base, or a named commit range). Saves a Markdown draft under docs/, iterates with the user, then posts it to the ticket as a new Jira comment only after explicit approval. Use on "QA scope for PROJ-123", "what should QA test", "write test scenarios for QA", "hand this ticket to QA", "prep the QA handoff", "gherkin scenarios for this ticket", or when a branch is ready for manual testing — even when the user only names the ticket or says "this branch".
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。
Master TypeScript's advanced type system including generics, conditional types, mapped types, template literals, and utility types for building type-safe applications. Use when implementing complex type logic, creating reusable type utilities, or ensuring compile-time type safety in TypeScript projects.
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。
Dr. Kaia Chen - AI Architect, mentor, and strategic advisor for Marcus. Use when the user says "Kaia", "Dr. Chen", "ask Kaia", "/kaia", or wants architectural advice, trade-off analysis, production readiness guidance, mentorship, a voice of reason, or a second opinion on design decisions. Kaia grounds her advice in the actual codebase (via Chen MCP tools) and research literature, and logs important decisions and insights via Simon.
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。
Bridge the gap from product specifications to implemented features. Document requirements, acceptance criteria, tech design, and track implementation progress in Notion.
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。
Refines ideas iteratively. Refine ideas through structured divergent and convergent thinking. Use "idea-refine" or "ideate" to trigger.
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 Azure deployments against functional, security, performance, and operational criteria before production release. Use this skill when users ask for readiness checks, health verification, or production sign-off gates. Covers Well-Architected Framework validation, SLA verification, and release readiness.
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。
Enrich Jupyter notebooks with pedagogical markdown content. Arguments: [target] [--execute] [--fix-errors] [--strict] [--consecutive] [--iterate]
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。
Verify and test Jupyter notebooks with iterative fixing. Arguments: [target] [--quick] [--fix] [--python-only] [--dotnet-only]
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。
Find and list functions in a binary by name, address, regex, or byte pattern. Use as the starting point for binary analysis, to locate specific functions, or to enumerate all functions matching criteria.
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A gate before git push / deploy — assembled from your own tools (secret-grep + slop-scan + code-review + build/compile + optional worktree isolation), so AI junk (slop / bugs / secrets / uncompiled code) never reaches a remote or a production server. Turns "I hope it's clean" into a verifiable criterion. Branches: web/OSS (git push/PR) vs deploy-to-server service. Triggers — "check before push", "clean push", "gate before push", "finalize before commit", "run before deploy".
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。