Results for “structured”

给定 PMID/PMCID 取结构化全文（NLM BioC XML/JSON）。封装 BioC PubMed（标题+摘要）和 BioC PMC OA（开放获取全文）端点，输出按章节切分的段落、表格上下文、RAG 友好 chunk，适合大批量入向量库。Fetches structured fulltext from NLM BioC API for PMID/PMCID, outputs section-segmented paragraphs and RAG-ready chunks. 触发词：全文抽取 / BioC / RAG 入库 / 文献全文 / PMC 开放获取 / structured fulltext / 段落抽取 / 表格抽取 / chunking / fulltext to vector store / paragraph extraction.

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ALWAYS use this skill whenever you would otherwise ask the user a question in free-form chat -- for clarifications, confirmations (especially destructive actions), missing parameters, multiple-choice decisions, or structured form input. Elicitations are routed through the Unique AI Platform UI via `unique-cli elicit create` + `elicit wait` (the default polling pattern) or a single supervised `unique-cli elicit ask` call (only when your agent instructions say your environment supervises the wait) so the user gets a proper structured prompt and you get a structured answer back. Do NOT ask the user in plain chat when you can use this skill instead.

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Extract structured data from LLM responses with Pydantic validation, retry failed extractions automatically, parse complex JSON with type safety, and stream partial results with Instructor - battle-tested structured output library

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Estimates time-scaled phylogenies, molecular clock rates, effective reproduction number R_e (or R_t), and population dynamics from dated pathogen genomes using TreeTime (maximum-likelihood) and BEAST2 (Bayesian; strict / uncorrelated lognormal / ORC clocks; constant / exponential / Bayesian Skyline / Skygrid / BICEPS / Birth-Death-Skyline / sampled-ancestor BDSKY priors; structured coalescent via MASCOT). Covers root-to-tip clock signal QC via TempEst, date-randomisation tests (Ramsden 2009; Duchêne 2015), recombination masking via Gubbins and ClonalFrameML before clock inference for recombining bacteria, BDSKY origin-vs-rootHeight pitfalls, sampling-bias correction (Volz & Frost 2014; preferential-sampling extensions), MASCOT structured coalescent for migration, BICEPS-vs-BSP skyline choice, multi-chain BEAST2 convergence diagnostics, and reconciliation between phylodynamic R_e and case-based R_t. Use when dating outbreak origins, estimating substitution rates, inferring R_e through time, building time-calibrated Nextstrain Augur trees, choosing between strict and relaxed clocks, fitting Birth-Death-Skyline (Stadler 2013) models, diagnosing temporal-signal failure, masking recombination before clock inference for *Streptococcus pneumoniae* / *Neisseria gonorrhoeae* / *Klebsiella* / *E. coli* phylodynamics, running MASCOT for structured-population analyses, or using UShER for pandemic-scale placement.

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When the user wants to add, fix, or optimize schema markup and structured data on their site. Also use when the user mentions "schema markup," "structured data," "JSON-LD," "rich snippets," "schema.org," "FAQ schema," "product schema," "review schema," "breadcrumb schema," "Google rich results," "knowledge panel," "star ratings in search," or "add structured data." Use this whenever someone wants their pages to show enhanced results in Google. For broader SEO issues, see seo-audit. For AI search optimization, see ai-seo.

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Estimates time-scaled phylogenies, molecular clock rates, effective reproduction number R_e (or R_t), and population dynamics from dated pathogen genomes using TreeTime (maximum-likelihood) and BEAST2 (Bayesian; strict / uncorrelated lognormal / ORC clocks; constant / exponential / Bayesian Skyline / Skygrid / BICEPS / Birth-Death-Skyline / sampled-ancestor BDSKY priors; structured coalescent via MASCOT). Covers root-to-tip clock signal QC via TempEst, date-randomisation tests (Ramsden 2009; Duchêne 2015), recombination masking via Gubbins and ClonalFrameML before clock inference for recombining bacteria, BDSKY origin-vs-rootHeight pitfalls, sampling-bias correction (Volz & Frost 2014; preferential-sampling extensions), MASCOT structured coalescent for migration, BICEPS-vs-BSP skyline choice, multi-chain BEAST2 convergence diagnostics, and reconciliation between phylodynamic R_e and case-based R_t. Use when dating outbreak origins, estimating substitution rates, inferring R_e through time, building time-calibrated Nextstrain Augur trees, choosing between strict and relaxed clocks, fitting Birth-Death-Skyline (Stadler 2013) models, diagnosing temporal-signal failure, masking recombination before clock inference for *Streptococcus pneumoniae* / *Neisseria gonorrhoeae* / *Klebsiella* / *E. coli* phylodynamics, running MASCOT for structured-population analyses, or using UShER for pandemic-scale placement.

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Complete knowledge domain for Firecrawl v2 API - web scraping and crawling that converts websites into LLM-ready markdown or structured data. Use when: scraping websites, crawling entire sites, extracting web content, converting HTML to markdown, building web scrapers, handling dynamic JavaScript content, bypassing anti-bot protection, extracting structured data from web pages, or when encountering "content not loading", "JavaScript rendering issues", or "blocked by bot detection". Keywords: firecrawl, firecrawl api, web scraping, web crawler, scrape website, crawl website, extract content, html to markdown, site crawler, content extraction, web automation, firecrawl-py, firecrawl-js, llm ready data, structured data extraction, bot bypass, javascript rendering, scraping api, crawling api, map urls, batch scraping

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Extract structured data from LLM responses with Pydantic validation, retry failed extractions automatically, parse complex JSON with type safety, and stream partial results with Instructor - battle-tested structured output library

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Generate JSON-LD structured data for AEM Edge Delivery Services pages. Analyzes page content and metadata to determine the appropriate schema.org types, extracts relevant properties, and produces validated JSON-LD snippets ready for implementation in head.html or scripts.js. Use when adding rich results support, improving search appearance, or auditing existing structured data.

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Extracts structured data from LLM responses using JSON schemas, Zod validation, and function calling for reliable parsing. Use when users request "structured output", "JSON extraction", "parse LLM response", "function calling", or "typed responses".

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Enforces consistent structured logging with request correlation IDs, standardized log schema, middleware integration, and best practices. Use for "structured logging", "log standardization", "request tracing", or "log correlation".

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Build a shared brand identity through structured interviews across multiple sessions. Explore purpose, audience, personality, and voice, reconcile founders’ perspectives, and produce a brandbook that designers, writers, and other collaborators can use.

• Define the identity of a new brand
• Clarify positioning during a brand refresh
• Align multiple founders’ perspectives

Design text extraction for forms, invoices, and quizzes using regex, rules that match text patterns. Check extraction confidence and send uncertain items to an LLM for review, with guidance on testing and tracking processing costs.

• Extracting quiz questions, choices, and answers
• Designing extraction for forms and invoices
• Choosing between regex and an LLM

Diagnose failed AI agent runs by capturing errors, checking likely causes, and choosing small, reversible recovery actions. The workflow produces a structured report of the failure, recovery steps, and evidence, helping explain repeated retries or stalled progress.

• Diagnosing repeated tool calls with no progress
• Investigating quality loss as context grows
• Checking file paths and environment mismatches

Develop a written brand identity through structured interviews across multiple sessions. Save answers and progress, explore founders' perspectives, and produce a master brandbook that gives designers, writers, and collaborators a shared reference.

• Defining the identity of a new brand
• Revisiting a brand's market positioning
• Preparing a brief for designers and writers

Coordinate coding projects through Claude DevFleet by splitting work into dependent tasks and dispatching Claude Code agents in separate Git working directories. Monitor progress, manage automatic task sequencing, and review structured reports of changes, tests, and errors.

• Splitting a coding project into dependent tasks
• Running coding agents in separate Git working directories
• Monitoring concurrent development tasks

Integrate single-page extraction into an application using Firecrawl scrape. Retrieve Markdown, HTML, links, screenshots, metadata, or structured data from a known URL, and configure how recently cached content may be reused.

• Preparing known web pages for summarization
• Enriching records from company or product pages
• Extracting pricing, changelogs, or documentation

Co-write proposals, specifications, and decision documents through a guided process. Gather background, draft and refine each section, then test the document with a fresh Claude conversation to find unclear explanations and missing context.

• Turning scattered background notes into a proposal
• Drafting specifications or decision documents
• Refining structure and wording section by section

Retrieve the top 30 stories from the Hacker News front page as JSON, a structured data format. Each entry includes its rank, title, link, points, and comment count for reviewing stories and their engagement.

• Review the top 30 Hacker News stories
• Compare story points and comment counts
• Collect article titles and links

Extract a Figma file into local project files containing its element hierarchy, reusable color and typography settings, and exportable assets. The structured snapshot supplies source data for later design mapping and implementation steps.

• Extract a Figma file's element hierarchy
• Collect color, typography, and spacing settings
• Prepare assets for later implementation steps

Turn patient information into a structured HTML case report for rounds, conferences, or documentation. Choose a full presentation or brief update, with organized findings, diagnostic reasoning, and educational management plans in a printable layout.

• Preparing a case presentation for a clinical conference
• Summarizing daily changes for rounds or handover
• Organizing findings into a SOAP-format report

Turn document analysis into a structured PPT presentation with images generated in a chosen style. The skill uses the NanoBanana stack to combine document-based content preparation and image generation within a presentation creation workflow.

• Turning a document into a PPT presentation
• Creating slides with styled, generated images
• Combining document analysis and presentation creation

Turn a design request into a concrete specification covering colors, fonts, layout, mood, spacing, and constraints. Generate DESIGN.md and an HTML preview, with a clear report of defaults applied to unspecified choices.

• Clarifying vague visual requirements before implementation
• Defining colors, fonts, and spacing for a landing page
• Reviewing design choices through an HTML preview

Turn academic papers into structured reviews that explain findings, strengths, weaknesses, and research methods. Assess originality and supporting evidence, compare relevant literature, and provide specific improvement suggestions in a detailed review or brief summary.

• Understanding a paper's findings and limitations
• Preparing a draft peer review
• Checking experimental design and reproducibility