「citation」の検索結果

Multi-source deep research using firecrawl and exa MCPs. Searches the web, synthesizes findings, and delivers cited reports with source attribution. Use when the user wants thorough research on any topic with evidence and citations.

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Checks whether each claim in a manuscript is supported by the source it cites, reading the project's per-source Markdown knowledge base. Flags contradicted, unsupported, overclaimed, out-of-scope, and misattributed claims with verbatim source quotes. Runs citation-check first, and stops at a pre-flight gate when the knowledge base is missing, incomplete, or holds unconverted PDFs. Use to verify that a draft's claims match the sources it cites.

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Typesets a working paper or journal submission in house-style LaTeX from Markdown, Word (.docx), TeX, ODT, RTF, or HTML. Converts with pandoc, wraps the body in an EB Garamond template, builds the PDF with latexmk, and sets journal-specific spacing, page limits, anonymization, disclosures, and citation style. Use to format or convert a paper to LaTeX, make a working paper, or prepare a submission to a named journal.

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Typeset a working paper or journal submission in house-style LaTeX from Markdown, Word, TeX, ODT, RTF, or HTML. Use to convert drafts, build PDFs, or prepare journal-specific spacing, limits, anonymization, disclosures, and citations.

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AI Engine Optimization - semantic triples, page templates, content clusters for AI citations

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Find quotes for a set of topic keywords and place them into a 2x2 matrix, spanned by the presence of an author/origin and by the literal containment of a keyword, optionally grounded in Internet/Web facts and optionally widened to the conceptual neighborhood of the topic. Use when the user wants "quotes", "sayings", "aphorisms", or "citations" on a topic.

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Insert, format, and cross-reference academic citations in Word (.docx) documents. Supports GB/T 7714-2015 (Chinese), IEEE, and APA 7th.

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Insert, format, and cross-reference academic citations in Word (.docx) documents. Supports GB/T 7714-2015, IEEE, APA 7th.

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LLM brand manipulation — build a consensus web so ChatGPT, Claude, Perplexity, and Google AI Overviews mention your brand when people ask about your category. Uses surround sound methodology, entity SEO, and LLM citation science. Use when "get mentioned in AI", "LLM brand presence", "surround sound", "AI search visibility", "Perplexity ranking", "ChatGPT mentions", "AI Overview inclusion", "entity authority", "brand mentions in AI", or any request to influence how AI systems perceive and recommend your brand.

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Build an AEO-first topical map optimized for AI search citation — entity clusters, query fan-out coverage, information gain scoring, and multi-platform distribution. Produces entity map, content node architecture, schema blueprint, and 90-day publishing calendar. Use when "topical map", "content architecture", "site structure", "topic clusters", "pillar content plan", "AEO map", "AI search architecture", "entity map", "what content should we build", or any request to plan a site's topical structure for AI search visibility.

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Pull performance data for published content across every surface — LinkedIn, Dev.to, Google Search Console, GA4, and AI citation tracking (Perplexity, Anthropic, OpenAI). Grades each piece as winner, average, or underperformer and surfaces whether an AI search engine has cited it.

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Convert a book or long document into a reusable AI mentor with source-grounded lessons, guided practice, citations, and persistent learning records.

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Extracts structured citation data from academic repositories using BeautifulSoup4 with lxml parser. Parses DOI metadata, author affiliations, and reference lists from PubMed, arXiv, and Semantic Scholar HTML.

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Use when the user asks a question about how things in the codebase or wiki relate — what connects two things, how something works end to end, what depends on what, or what a concept means in this repo. Answers from the knowledge graph in graphify-out/graph.json, with source-location citations. Trigger on "/graph-query", "ask the graph", "what connects X to Y", "trace how X works", "explain <concept> from the graph", "shortest path between".

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Comprehensive SEO analysis for any website or business type. Performs full site audits, single-page deep analysis, technical SEO checks (crawlability, indexability, Core Web Vitals with INP), schema markup detection/validation/generation, content quality assessment (E-E-A-T framework per Dec 2025 update extending to all competitive queries), image optimization, sitemap analysis, and Generative Engine Optimization (GEO) for AI Overviews, ChatGPT, and Perplexity citations. Analyzes AI crawler accessibility (GPTBot, ClaudeBot, PerplexityBot), llms.txt compliance, brand mention signals, and passage-level citability. Industry detection for SaaS, e-commerce, local business, publishers, agencies. Triggers on: "SEO", "audit", "schema", "Core Web Vitals", "sitemap", "E-E-A-T", "AI Overviews", "GEO", "technical SEO", "content quality", "page speed", "structured data".

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Push the LLM to reconsider, refine, and improve its recent output. Use when user asks for deeper critique or mentions a known deeper critique method, e.g. socratic, first principles, pre-mortem, red team

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Structures exchange offers, consent solicitations, and amend-and-extend transactions as Chapter 11 alternatives. Use when managing out-of-court workouts, designing exchange offers, or structuring pre-negotiated deals.

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Evaluates amendment and waiver requests with consent requirements, fee structures, and modified term impact analysis. Use when analyzing amendments, structuring consent solicitations, or evaluating covenant relief requests.

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Audit memory retrieval quality (drift, staleness, citation-groundedness) and produce remediation backlog.

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Use when you want to improve response quality through meta-cognitive reasoning. Applies 15+ reasoning methods to reconsider and refine initial outputs.

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LLM-as-judge evaluation framework with 5-dimension rubric (accuracy, groundedness, coherence, completeness, helpfulness) for scoring AI-generated content quality with weighted composite scores and evidence citations

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Guided requirements gathering through structured questions to create comprehensive specifications. Produces a complete requirements document via non-blocking conversational interaction.

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Content quality and E-E-A-T analysis with AI citation readiness assessment. Use when user says "content quality", "E-E-A-T", "content analysis", "readability check", "thin content", or "content audit".

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Optimize content for AI Overviews (formerly SGE), ChatGPT web search, Perplexity, and other AI-powered search experiences. Generative Engine Optimization (GEO) analysis including brand mention signals, AI crawler accessibility, llms.txt compliance, passage-level citability scoring, and platform-specific optimization. Use when user says "AI Overviews", "SGE", "GEO", "AI search", "LLM optimization", "Perplexity", "AI citations", "ChatGPT search", or "AI visibility".

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。