318 件 ・ 関連度順
概要と使いどころ
Pythonic RDKit wrapper with sensible defaults for drug discovery. SMILES parsing, standardization, descriptors, fingerprints, similarity, clustering, diversity selection, scaffold analysis, BRICS/RECAP fragmentation, 3D conformers, and visualization. Returns native rdkit.Chem.Mol. Prefer datamol for standard workflows; use RDKit directly for advanced control.
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Cheminformatics toolkit for molecular analysis and virtual screening: SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints (Morgan/ECFP, MACCS), Tanimoto similarity, SMARTS substructure filtering, Lipinski drug-likeness, reaction enumeration, 2D/3D coordinates. For simpler API use datamol; use RDKit for fine-grained sanitization, custom fingerprints, or SMARTS/reaction control.
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Draft Project Manual specification requirements for EPD submittals and sourced GWP limits. Use for embodied-carbon requirements in project sections; use the EPD parsing, research or comparison skills for product evidence.
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Creates reusable Zod v4 schemas, validates API payloads, forms, and configuration input, transforms and coerces data safely, and handles validation errors with strong type inference for TypeScript applications. Use when designing validation layers, parsing `z.string()`, `z.object()`, or `z.email()` schemas, or implementing runtime type-safe data validation.
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Use Databricks built-in AI Functions (ai_classify, ai_extract, ai_summarize, ai_mask, ai_translate, ai_fix_grammar, ai_gen, ai_analyze_sentiment, ai_similarity, ai_parse_document, ai_prep_search, ai_query, ai_forecast) to add AI capabilities directly to SQL and PySpark pipelines without managing model endpoints. Also covers document parsing and building custom RAG pipelines (parse → prep_search → index → query).
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。
CPU-only operating guidance for the BEHAVIOR-1K BDDL 3.7.0 package: symbolic activity definitions, condition parsing/evaluation, object taxonomy, and generated-data-backed knowledge-base inspection.
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Use this skill any time a .pptx or .potx file is involved in any way — as input, output, or both. This includes: creating slide decks, pitch decks, or presentations; reading, parsing, or extracting text from any .pptx or .potx file (even if the extracted content will be used elsewhere, like in an email or summary); editing, modifying, or updating existing presentations; combining or splitting slide files; working with templates (.potx), layouts, speaker notes, or comments. Trigger whenever the user mentions "deck," "slides," "presentation," or references a .pptx or .potx filename, regardless of what they plan to do with the content afterward. If a .pptx or .potx file needs to be opened, created, or touched, use this skill.
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Builds traceability matrices connecting requirements to design documents to source code implementation, tracking the complete development lifecycle. Use when you need to verify implementation completeness, ensure all requirements are implemented in code, generate compliance documentation, audit requirement coverage, identify orphaned code, or create traceability reports for stakeholders. Supports parsing requirements from Markdown, Word, and PDFs; extracting design from architecture docs and API specs; and scanning source code for implementations. Outputs to Markdown tables, Excel/CSV, and HTML visualizations.
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Collect, normalize, and structure execution traces from instrumented programs (strace, ltrace) into JSON format for downstream analysis. Use when working with system call traces, library call traces, or execution logs that need to be analyzed for debugging, test case reproduction, or verification. Supports parsing strace/ltrace output, filtering noise, extracting debug information, and preparing traces for bug analysis or reproduction workflows.
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 this skill any time a .pptx file is involved in any way — as input, output, or both. This includes: creating slide decks, pitch decks, or presentations; reading, parsing, or extracting text from any .pptx file (even if the extracted content will be used elsewhere, like in an email or summary); editing, modifying, or updating existing presentations; combining or splitting slide files; working with templates, layouts, speaker notes, or comments. Trigger whenever the user mentions "deck," "slides," "presentation," or references a .pptx filename, regardless of what they plan to do with the content afterward. If a .pptx file needs to be opened, created, or touched, use this skill.
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。
Cloudflare Workers observability with logging, Analytics Engine, Tail Workers, metrics, and alerting. Use for monitoring, debugging, tracing, or encountering log parsing, metric aggregation, alert configuration errors.
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Builds flexible API filtering and sorting systems with query parameter parsing, validation, and security. Use when implementing search endpoints, building data grids, or creating dynamic query APIs.
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Schedules meetings with internal and external stakeholders by parsing natural language requests, resolving time zones, checking calendar availability, proposing optimal slots, and managing invitations and RSVPs. Use when the user requests meeting scheduler or provides relevant inputs for this workflow.
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。
Workflow for foundational sequence parsing, conversion, compression handling, and interval-aware file validation.
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Generate and parse Excel spreadsheets with ExcelJS — create workbooks with multiple sheets, styled cells, formulas, charts, images, and conditional formatting. Use when tasks involve exporting application data to .xlsx, building financial reports, parsing uploaded spreadsheets, or creating data import/export pipelines.
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Parse complex documents with IBM's open-source Docling library, which turns PDFs and Office files into structured Markdown or JSON. Use when a user asks to parse a document with tables, extract figures from a document, handle multi-column layouts, convert a complex PDF to structured data, extract content from academic papers, or process documents with mixed layouts. Handles tables, figures, headers, footers, and multi-column text.
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Extract structured data from any document format using unified document processing. Use when a user asks to extract data from a document, parse a PDF, pull structured data from files, convert documents to JSON or CSV, extract fields from invoices or forms, or scrape data from documents.
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Parse and generate CSV files with the csv package — stream large files, handle custom delimiters, transform records, validate data, and generate CSV output from objects. Use when tasks involve data import/export, ETL pipelines, processing uploaded CSV files, or generating downloadable reports.
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。
Build CLI tools with Commander.js. Use when creating command-line applications, parsing arguments, implementing subcommands, or building developer tools with flags and options.
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Automate bookkeeping workflows by parsing bank statements (CSV/OFX/QIF), categorizing transactions using rules or AI, reconciling accounts, generating expense reports, and detecting duplicates. Use when: automating transaction categorization, building reconciliation pipelines, processing bank exports, or generating expense summaries without manual data entry.
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Parse architectural drawings, floor plans, and building code compliance documents using Vision AI. Extracts building type, occupancy, floor areas, room layouts, dimensions, and code parameters. Use when: reading PDF floor plans, analyzing architectural drawings, extracting building data from images or scanned documents.
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。
Stage and commit with flexible intent parsing. Accepts file/folder scope, free-text description, amend, or any combination.
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。
Interpret video content visually by sampling frames into timestamped contact sheets that can be read as images. Use when: user asks what happens in a video; asks to summarize, describe, review, or QA video content or footage; asks about scenes, actions, people, or objects in a video; needs a storyboard-style overview of a clip; asks to find where something occurs in a video. Triggers on 'watch this video', 'what's in this video', 'summarize the video', 'describe the footage', 'contact sheet', 'storyboard', 'review this clip', 'find the scene where', 'scene detection', 'shot boundaries', 'detect cuts', 'dwell points'. For converting, trimming, or transcoding video, use processing-video instead.
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Create video from prompts by overseeing multi-clip AI generation end to end: write a shot list, generate each scene with Gemini Omni Flash (via the Cloudflare AI Gateway), review the results, and assemble them into a finished cut. Use when the user asks to make/generate a video, a short film, an animatic, or a multi-scene clip from a script or idea; when they mention Omni, Veo, text-to-video, or image-to-video; or when acting as the editing/director agent over generated footage. Triggers on 'make a video', 'generate a clip', 'short film', 'video from this script', 'turn this into a video', 'omni', 'veo', 'text to video', 'storyboard to video'. For transcoding/trimming/merging/GIF/subtitles use processing-video; for reading or summarizing existing video content use parsing-video.
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。