「structured」の検索結果

Drafts a customized Customer Agreement starting from the Y Combinator standard form SaaS template. Tailors the agreement through structured intake questions covering fee structure, data handling, ML rights, implementation services, and more. Applies 18 always-on defaults that transform the raw YC form into a professional starting point (renamed to "Customer Agreement", data privacy section added, warranty restructured, consolidated SLA/support exhibit, etc.). Produces a clean .docx and a lawyer-facing memo explaining every change from the YC standard. Use when user says "draft a SaaS agreement", "YC SaaS", "startup SaaS contract", "customer agreement", "SaaS subscription agreement", or "I need a SaaS agreement starting from the YC form". Also trigger when the user is a startup founder discussing SaaS contracting, even if they don't mention YC specifically.

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Research a US employment law topic across federal, state, and city jurisdictions and produce structured research notes with proper source attribution. Use this skill any time the user asks about US employment laws, regulations, or pending legislation - from a single jurisdiction question to a 50-state survey. Triggers include phrases like "research [employment law topic]", "what are the laws on [employment topic]", "state-by-state [employment topic]", "help me understand [topic] across the US", or any prompt that asks for a legal landscape overview before another deliverable. Always run this BEFORE the employment-law-dashboard skill if a dashboard is the eventual output. Output is a structured research note that distinguishes primary sources (statutes, regs, agency guidance) from secondary sources (law firm alerts, tracker orgs).

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Turn an interpretive-ambiguity audit of a legal text — contract, statute, regulation, or judicial opinion — into a polished deliverable. Produces a multi-page website (default), a single-page interactive site, a Microsoft Word document, a PowerPoint deck, or a LaTeX/PDF. Use whenever a user has a stress-test result, ambiguity audit, or "where will this be litigated" scenarios and wants to publish, present, or share them. Triggers on "publish the audit," "make a report from this," "turn this into a website / deck / brief / Word doc," "make a Netlify site," "build slides of these ambiguities," "render the scenarios," "package this stress test," "produce a LaTeX version," and similar. Pairs with the ambiguity-stress-test skill (the underlying analysis) but works on any structured or semi-structured ambiguity analysis. Visual design is opinionated but the user can override colors, fonts, and brand label. Most formats need Python and a writable filesystem; single-file HTML works where the host has neither.

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Your team's shared, AI-ready knowledge base — people, companies, meetings, SOPs, and decisions structured so an agent can answer on your team's behalf. Team-scope sibling to second-brain. Modes — capture, compile (wiki pages + INDEX.md), query (trust-weighted, saved to outputs/), review (verify / deprecate / supersede stale captures), lint, connect, search. Structured raw dirs (people/, companies/, meetings/, sops/, decisions/, customer-language/, sales-objections/). Every capture stamps author, timestamp, and trust status. Optional sync from call transcripts, Slack/email exports, CRM. Vault at ${COMPANY_BRAIN_VAULT:-$HOME/Documents/CompanyBrain}/. Triggers on "/company-brain," "/cb," "capture this into the team brain," "log this meeting," "save this SOP," "compile the company wiki," "query the team brain," "what does the team know about X," "review the company brain," "lint the company brain," "who's the internal expert on X."

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Select and configure a structured discussion protocol matched to the purpose, topic, and group readiness. Use when planning classroom discussions, Socratic seminars, or structured debate.

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Detect, validate, and generate Schema.org structured data. JSON-LD format preferred. Use when user says "schema", "structured data", "rich results", "JSON-LD", or "markup".

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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," or "breadcrumb schema." For broader SEO issues, see seo-audit.

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Cyber threat intelligence and OSINT analysis toolkit. Runs structured investigations and delivers analyst-grade intelligence products with sourced, trust-scored findings. Use for OSINT and CTI cases, digital-footprint and exposure review, domain/subdomain/DNS/certificate recon, web-infrastructure pivoting (favicon hashes, tracker IDs, TLS certs, phishing-kit fingerprinting, campaign clustering), username/email/phone enumeration, breach and infostealer-log triage, image forensics, geolocation, crypto-wallet and IBAN/bank-account tracing, darknet search, M365/Azure and SaaS tenant recon, China/Sinophone recon (ICP filings, PRC corporate registries, Baidu/FOFA/Quake/ZoomEye), vulnerability and ransomware lookup, threat modeling, PII redaction, and structured reporting. Commands include /case, /sweep, /query, /webpivot, /username, /phone, /email-deep, /breach-deep, /icp, /cn-corp, /iban, /stealer-log, /exposure, /threat-model, /report, /brief, /redact, /apikeys.

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Guide users through a structured workflow for co-authoring documentation. Use when user wants to write documentation, proposals, technical specs, decision docs, or similar structured content. This workflow helps users efficiently transfer context, refine content through iteration, and verify the doc works for readers. Trigger when user mentions writing docs, creating proposals, drafting specs, or similar documentation tasks.

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Instrument Spring Boot with Micrometer metrics, distributed tracing, and structured logging. Use when adding Micrometer metrics, distributed tracing, or structured logging to Spring Boot.

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Standards for structured logging and observability in Golang. Use when adding structured logging or tracing to Go services.

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。

Guide users through a structured workflow for co-authoring documentation. Use when user wants to write documentation, proposals, technical specs, decision docs, or similar structured content. This workflow helps users efficiently transfer context, refine content through iteration, and verify the doc works for readers. Trigger when user mentions writing docs, creating proposals, drafting specs, or similar documentation tasks.

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Use this skill when the user requests to generate, create, or write professional research reports including but not limited to market analysis, consumer insights, brand analysis, financial analysis, industry research, competitive intelligence, investment due diligence, or any consulting-grade analytical report. This skill operates in two phases — (1) generating a structured analysis framework with chapter skeleton, data query requirements, and analysis logic, and (2) after data collection by other skills, producing the final consulting-grade report with structured narratives, embedded charts, and strategic insights.

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Performs deep AI-powered analysis and understanding of long-form videos (up to 3 hours, 10GB) using Volcengine LAS large language models. Video analysis, video comprehension, and video summarization — generates comprehensive video summaries, video recaps, chapter breakdowns, event timelines, key moments, and structured content indexing. Supports behavior detection, action detection, video annotation, video tagging, and video content recognition. Enables intelligent video question answering — ask questions about video content and get AI answers. Handles long meeting recordings, lecture videos, webinars, surveillance and security footage, movies, tutorials, and any long video that needs detailed understanding. Async processing with submit-poll workflow. Use this skill when the user wants to analyze or understand long videos (up to 3h/10GB) with LLM-based deep comprehension, summarize video content or generate recaps, extract chapters/key moments/timelines from videos, do video Q&A (ask questions about video content), detect actions/behaviors in videos, review meeting recordings/lectures/webinars, or get structured video descriptions.

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 you need to apply Java concurrency best practices — including thread safety fundamentals, ExecutorService thread pool management, concurrent design patterns like Producer-Consumer, asynchronous programming with CompletableFuture, immutability and safe publication, deadlock avoidance, virtual threads, structured concurrency, scoped values, backpressure, cancellation discipline, and observability for concurrent systems. This should trigger for requests such as Review Java code for concurrency; Review Java code for thread safety; Fix race conditions in Java concurrency code; Choose ExecutorService or virtual threads in Java; Improve synchronization and shared mutable state handling; Apply structured concurrency for related Java subtasks. Part of Plinth Toolkit

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Decide where to enforce structured LM output (constrain at decode time with Outlines vs validate-and-retry with Instructor vs grammar with Guidance) and which failure stance to take (Assert/hard-fail vs Suggest/soft-retry). Use when an LM's output is parsed or typed by downstream code and you must pick one enforcement library plus its failure handling, when malformed output is burning tokens on retries, or when choosing between decode-time vs validation-time constraints for local vs API models.

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。

Guide users through a structured workflow for co-authoring documentation. Use when user wants to write documentation, proposals, technical specs, decision docs, or similar structured content. This workflow helps users efficiently transfer context, refine content through iteration, and verify the doc works for readers. Trigger when user mentions writing docs, creating proposals, drafting specs, or similar documentation 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。

Extract structured data from construction PDFs. Convert specifications, BOMs, schedules, and reports from PDF to Excel/CSV/JSON. Use OCR for scanned documents and pdfplumber for native PDFs.

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Comprehensive guide to Spark Structured Streaming for production workloads. Use when building streaming pipelines, working with Kafka ingestion, implementing Real-Time Mode (RTM), configuring triggers (processingTime, availableNow), handling stateful operations with watermarks, optimizing checkpoints, performing stream-stream or stream-static joins, writing to multiple sinks, or tuning streaming cost and performance.

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Build RAG / unstructured-document evaluation datasets and demo documents (e.g. for Knowledge Assistant) on Databricks: generate synthetic PDFs locally, upload to Unity Catalog volumes, and pair each document with test questions for retrieval evaluation.

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。

Guide users through a structured workflow for co-authoring documentation. Use when user wants to write documentation, proposals, technical specs, decision docs, or similar structured content. This workflow helps users efficiently transfer context, refine content through iteration, and verify the doc works for readers. Trigger when user mentions writing docs, creating proposals, drafting specs, or similar documentation 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。

Build automated PII detection and redaction pipelines using spaCy NER, Microsoft Presidio, and AWS Macie integration. Includes confidence scoring, custom entity type definitions, batch processing workflows, and multi-format document scanning for structured and unstructured data sources.

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Design, audit, or implement application structured logging architecture from repository evidence. Use when a user asks whether or where to add logs, how to choose or migrate a logger, how to standardize events/fields/levels/redaction, how to add HTTP access or panic logs, or why production logs cannot answer an incident question. Do not use merely to tail platform logs or to design a full metrics, tracing, SLO, and incident-management program.

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Transforms natural language requirements (user stories, verbal descriptions, business rules) into formal specifications and constraints. Use when converting informal requirements into structured, testable specifications with explicit constraints. Outputs in multiple formats including BDD-style Given-When-Then, JSON Schema, and structured plain text requirements 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。