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Coordinate bulk contract clause extraction with Luminance or Kira, prepare loading requests, and check returned results according to your team's review settings. Assess flagged clauses against the transaction and organize findings and required consents.
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Extract text from PDFs by trying several methods in sequence and checking the results. When ordinary extraction yields too little text, switch to OCR, which recognizes text in images, to process scanned documents.
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Provides fallback methods for extracting PDF text when the usual file reader fails or returns incomplete content. Uses command-line tools or Python libraries to retrieve text, preserve spacing, inspect metadata, and extract tables.
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Recover readable text from PDFs when read_file or Python extraction fails. Use pdftotext, a command-line text extractor, with options to preserve layout, specify character encoding, or save extracted content to a file.
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Retrieve PDFs from web sources and extract their text through an ordered sequence: direct reading, pdftotext, then PyMuPDF. Includes file validation, extraction checks, and guidance for documenting inaccessible sources and distinguishing supplementary content.
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Download PDFs or process local files, extract their text, and turn processed data into structured PDF reports with ReportLab. Includes alternative extraction methods and text output when PDF generation fails.
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Extract text from PDFs by checking each tool’s output and switching methods when needed. Guides agents from file reading to pdftotext and PyMuPDF, then checks readability, missing sections, and records extraction failures.
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Extract readable text from PDFs when normal file reading returns garbled or unusable content. The workflow switches between extraction tools, checks the results, and includes optical character recognition for scanned pages when needed.
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Extract text from downloaded PDFs by checking for invalid files, trying three extraction tools in sequence, and validating the results. This workflow helps identify download errors, empty output, and encoding problems during document retrieval.
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Extract readable text from PDFs with pdftotext when read_file returns garbled or binary content. Choose page ranges, preserve text layout where needed, and send the extracted content to standard output or a text file for further processing.
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Extract PDF text when read_file returns unreadable binary content or little usable output. Try pdftotext with layout preservation, then use pdfplumber as an alternative when the command is unavailable or produces insufficient text.
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Extract text from PDFs when the first reading attempt fails or produces poor results. The skill tries command-line tools, Python libraries, then OCR, which recognizes text in images, while checking output quality and recording successful methods.
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Download PDFs from web URLs or process local files, verify their format, and extract text with pdftotext. If extraction fails or looks poor, try PyMuPDF, check the results, and document any remaining source limitations.
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Extract text from difficult PDFs by trying several libraries and command-line tools in sequence, then using OCR, which reads text from images. Record failed attempts and check that extracted content meets the task requirements.
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Inspect unfamiliar Excel workbooks before building extraction scripts. Map sheets, headers, columns, and row patterns, then use those findings to distinguish actual records from summaries and blank rows when extracting data.
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Extract timestamped text from a YouTube video's transcript panel and turn it into summaries, chapter outlines, X threads, blog articles, or notable quotes. Use existing captions to create readable material from video content.
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Extract a webpage’s content from its URL and convert it to Markdown, a plain-text format that preserves document structure. Use it to collect articles, blog posts, tutorials, or documentation for reading and further processing.
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Extract structured specifications from electronic component datasheet PDFs — pinouts, electrical characteristics, peripherals, topology, and features. Cache extractions per project for consumption by schematic and PCB analyzers. Primary consumer infrastructure for `kicad`, `emc`, `spice`, and `thermal` analyzers. Use this skill whenever the user asks to extract, verify, or read specs from a component datasheet; when analyzers need verified IC knowledge (EN pin thresholds, PG presence, USB peripheral speed); or when a review mentions datasheet coverage, extraction quality, or per-MPN specifications. Also triggers on "extract this datasheet", "what are the specs for MPN X", "verify datasheet extraction", or "check pin functions for part Y".
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Extract structured specifications from electronic component datasheet PDFs — pinouts, electrical characteristics, peripherals, topology, and features. Cache extractions per project for consumption by schematic and PCB analyzers. Primary consumer infrastructure for `kicad`, `emc`, `spice`, and `thermal` analyzers. Use this skill whenever the user asks to extract, verify, or read specs from a component datasheet; when analyzers need verified IC knowledge (EN pin thresholds, PG presence, USB peripheral speed); or when a review mentions datasheet coverage, extraction quality, or per-MPN specifications. Also triggers on "extract this datasheet", "what are the specs for MPN X", "verify datasheet extraction", or "check pin functions for part Y".
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Pick the right LLM for LEGAL INFO EXTRACTION — pulling facts, clauses, dates, parties, obligations, and structured fields out of contracts and legal documents. Vendor-neutral routing grounded in mid-2026 benchmarks (legalbenchmarks.ai Info Extraction; CUAD/MAUD/ACORD). Asks up to 4 quick questions (cost, speed, accuracy/stakes, privacy/jurisdiction/language), then recommends a primary model + fallback + what to avoid + what a human must verify. Use when someone asks "which model should I use to extract clauses/data from these documents", "best AI for contract data extraction", "route this extraction task", or is about to pull structured fields from legal docs without a fixed model.
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Emit the content signals reimagine-it reads from an HTML file — title, anchors, proper nouns, dates, numbers, emails, links, source hex colors, and the derived palette — as JSON without generating a redesign. Use when the user says /reimagine-it extract, "what does the engine see in this page", "extract the palette", "pull the facts from this page", or wants content analysis for research, migration, or design-system work. CLI: npx reimagine-it extract -i page.html -o signals.json. Part of the reimagine-it Content-Derived Design suite.
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Extract colors, fonts, logos, imagery, and writing style from a live website. Turn measured page details into a structured brand kit, a written guide, and a reusable design system with visual previews.
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Add support for a programming language to codegraph, from connecting its parser and implementing code extraction to writing tests. Evaluate extraction quality and compare agent effort with and without codegraph across three real codebases.
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Read, create, and organize PDF files, including merging documents, splitting pages, extracting tables, and filling forms. It also covers password protection and OCR, which recognizes text in scanned pages for extraction or search.
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