「structure」の検索結果

Designing conversational flows for website chatbots and AI agents. Intent recognition architecture, branching logic, fallback handling, escalation to human, conversation analytics. Honest about scripted-bot (rigid trees, fail edge cases), hallucinating-bot (LLM without structure, makes things up), and structured-guided-conversation (LLM-powered with intent architecture and fallback discipline) patterns. Distinguishes chatbot DESIGN (this skill) from chatbot IMPLEMENTATION (engineering and platform work). Triggers on chatbot, conversational AI, AI agent, chat widget, intent design, conversational flow, bot escalation, LLM grounding. Also triggers when a chatbot is hallucinating, when a scripted bot is failing edge cases, or when a chatbot is being scoped for the first time.

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。

Expert TSA cybersecurity compliance advisor for critical infrastructure owners and operators. Use this skill whenever a user asks about TSA Security Directives for pipelines, freight railroads, passenger rail, public transit, or bus operators; the TSA Cyber Risk Management Program (CRMP); Cybersecurity Implementation Plan (CIP); Cybersecurity Operational Implementation Plan (COIP); Cybersecurity Assessment Plan (CAP); incident reporting to CISA; designation of a Cybersecurity Coordinator; Critical Cyber Systems (CCS); OT/IT network segmentation; the TSA November 2024 NPRM; or any directive in the SD Pipeline-2021 series, SD 1580-21-01 (freight rail), or SD 1582-21-01 (public transit/passenger rail). Also trigger for questions like "are we covered by TSA directives?", "what does the TSA require for pipeline cybersecurity?", "how do I build a CIP?", "what must I report to CISA?", or any request involving transportation critical infrastructure cybersecurity compliance.

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。

Collect and persist structured presentation requirements for Codex Slides, including scenario choice, audience, page count, language, format, research mode, visual direction, source-file roles, and clarification checkpoints. Use when a new deck request is ambiguous or scenario-led.

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。

Generate validated AWS architecture diagrams as draw.io XML using official AWS4 icon libraries. Use this skill whenever the user wants to create, generate, or design AWS architecture diagrams, cloud infrastructure diagrams, or system design visuals. Also triggers for requests to visualize existing infrastructure from CloudFormation, CDK, or Terraform code. Supports two modes: analyze an existing codebase to auto-generate diagrams, or brainstorm interactively from scratch. Exports .drawio files with optional PNG/SVG/PDF export via draw.io desktop CLI.

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。

Structured data implementation, validation, and optimization. Covers JSON-LD patterns for 20+ schema types, rich snippet eligibility, AI search visibility, Knowledge Graph optimization, and CMS-specific deployment guides.

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。

Information architecture, URL hierarchy, internal linking strategy, navigation design, and silo structure for websites. Covers architectural audits, new site planning, crawl equity optimization, and topic cluster mapping.

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。

This skill should be used when the user asks to "analyze Terraform modules", "scan IaC for security issues", "review Terraform configurations", "check infrastructure code for misconfigurations", or "audit cloud resources".

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。

Comprehensive guide for working with HashiCorp Terraform Stacks. Use when creating, modifying, or validating Terraform Stack configurations (.tfcomponent.hcl, .tfdeploy.hcl files), working with stack components and deployments from local modules, public registry, or private registry sources, managing multi-region or multi-environment infrastructure, or troubleshooting Terraform Stacks syntax and structure.

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。

Terraform provider acceptance test patterns using terraform-plugin-testing with the Plugin Framework. Covers test structure, TestCase/TestStep fields, ConfigStateChecks with custom statecheck.StateCheck implementations, plan checks, CompareValue for cross-step assertions, config helpers, import testing with ImportStateKind, sweepers, and scenario patterns (basic, update, disappears, validation, regression), and ephemeral resource testing with the echoprovider package. Use when writing, reviewing, or debugging provider acceptance tests, including questions about statecheck, plancheck, TestCheckFunc, CheckDestroy, ExpectError, import state verification, ephemeral resources, or how to structure test files.

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。

Expert AWS solution architecture for startups focusing on serverless, scalable, and cost-effective cloud infrastructure with modern DevOps practices and infrastructure-as-code

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。

Production-grade web scraping with automatic anti-bot bypass, structured JSON parsing for 40+ targets, and geo-targeting. Use when the user needs to scrape web pages, extract product data, get search results, or collect structured data from supported e-commerce and search platforms without worrying about getting blocked and when geo targeting is required.

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。

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.

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。

Team structure, gearing analysis, continuity planning, and competing demand management for legal matters. Use when setting up a matter team, reviewing whether work is being done at the right grade, planning cover for an unavailable team member, or managing a resource conflict across matters. Trigger on: 'who should be on this matter', 'team structure', 'gearing', 'right people at the right level', 'partner is doing associate work', 'cover for maternity leave', 'who has capacity', 'team member leaving', 'over-allocated', 'staffing conflict', 'pinch point', 'capacity problem', 'how do we resource this', 'team too senior', 'team too junior'.

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。

Stakeholder mapping, communication plan design, reporting hierarchy, and mid-matter comms updates. Use when setting up a new matter and needing to identify who needs what information, designing the communication rhythm, building reporting structures for multi-jurisdiction programmes, or updating the comms plan when the stakeholder landscape changes. Trigger on: 'stakeholder map', 'who needs to be kept informed', 'communication plan', 'reporting structure', 'who do we report to', 'how often do we meet', 'client wants more updates', 'new contact on the client side', 'build the comms plan', 'reporting hierarchy', 'HQ vs regions', 'who is the decision maker', 'comms rhythm', 'meeting cadence', 'status call schedule'.

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。

Screens a dismissal — proposed or already done — against the unfair dismissal framework for England & Wales, and shows where it is exposed. Structures the qualifying-service question, the automatically-unfair categories (no qualifying period), and a Burchell / Polkey / band-of-reasonable-responses analysis for a solicitor to verify — it does not determine fairness. Surfaces specific procedural risks. Use when the user says 'is this unfair', 'screen this dismissal', 'ordinary unfair dismissal', 'automatic unfair dismissal', or wants a structured fairness review before or after dismissal.

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。

Expert TSA cybersecurity compliance advisor for critical infrastructure owners and operators. Use this skill whenever a user asks about TSA Security Directives for pipelines, freight railroads, passenger rail, public transit, or bus operators; the TSA Cyber Risk Management Program (CRMP); Cybersecurity Implementation Plan (CIP); Cybersecurity Operational Implementation Plan (COIP); Cybersecurity Assessment Plan (CAP); incident reporting to CISA; designation of a Cybersecurity Coordinator; Critical Cyber Systems (CCS); OT/IT network segmentation; the TSA November 2024 NPRM; or any directive in the SD Pipeline-2021 series, SD 1580-21-01 (freight rail), or SD 1582-21-01 (public transit/passenger rail). Also trigger for questions like "are we covered by TSA directives?", "what does the TSA require for pipeline cybersecurity?", "how do I build a CIP?", "what must I report to CISA?", or any request involving transportation critical infrastructure cybersecurity compliance.

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。

Matter scoping across the full pre-execution arc — organise client data into a structured brief, capture the agreed baseline, or reconstruct scope mid-flight. Use when making sense of client information before a proposal, scoping a new matter, running a kickoff, defining scope, mapping stakeholders, or inheriting a matter mid-flight. Trigger on: 'make sense of this', 'structure this for the proposal', 'scope this matter', 'new matter', 'kickoff', 'what are we doing', 'who are the stakeholders', 'what does success look like', 'matter setup', 'intake', 'I've inherited this matter', 'organise this client data'.

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。

Panel structure design, firm selection criteria, right-sourcing analysis, and coverage gap assessment for in-house legal teams. Build panel frameworks from scratch or formalise existing informal arrangements. Define weighted selection criteria for firm evaluation. Run right-sourcing analysis (ABC tiering) to identify work sent to expensive providers that doesn't require that level of resource. Assess existing panels for practice area, geographic, and capability gaps. Trigger on: 'design our panel', 'how many firms do we need', 'panel structure', 'firm selection criteria', 'which firms should we use', 'right-sourcing', 'are we sending work to the right firms', 'panel gaps', 'too many firms', 'consolidate our panel', 'we need a firm for', 'preferred provider', 'panel tiers', 'do we need BigLaw for this', 'convergence', 'D&I panel targets', 'how do we choose firms'.

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。

A jurisdiction-grounding reference file for AI assistants working on Gibraltar legal, regulatory, and compliance research. Corrects systematic errors AI models make when applying English law to Gibraltar — a distinct jurisdiction under the Gibraltar Constitution Order 2006, with its own court hierarchy, civil procedure rules, employment law, and regulatory framework. Covers the source hierarchy, court structure, civil procedure divergences (14-day deemed service, post-Jackson reforms not adopted), employment law specifics (uncapped discrimination awards, 5-employee redundancy threshold), FSC financial services regulation, AML/KYC compliance sources, Gibraltar Companies Act 2014 with UBO public register, and a structured directory of official public record sources. Includes research profiles for lawyers, compliance professionals, and due diligence practitioners. Not legal advice.

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。

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).

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。

An end-to-end method for drafting and finalising a complex, multi-pillar regulated fintech agreement — from intake to signature. Authored from a senior fintech lawyer's manual: a licensed payment-services provider engaging a counterparty across agent cash-in/cash-out, QR payments, wallet e-payments, and a marketplace, each with its own regulatory profile. Runs five phases and fourteen steps: regulatory mapping (activity-to-licence matrix, grey-zone classification gates), architecture (framework-plus-sub-agreement structure, ring-fenced marketplace), the regulatory–commercial balance (what flexes vs what cannot), core drafting (authority, float mechanics, hard-coded regulator caps, liability — all tracking control), execution-blocker triage, and a pre-signature check closing open blockers as conditions precedent. It refuses to invent licence-specific values or draft a representation as true without executed evidence. Use it to structure, draft, negotiate, or review any regulated payments contract.

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。

AFA design, scope-to-fee matching, negotiation preparation, health check, and scope dispute assessment for in-house legal ops teams managing outside counsel arrangements. Design fee structures (fixed, capped, collar, blended, phased, success). Assess whether scope supports a proposed AFA. Prepare commercial positions for fee negotiations. Review existing AFAs against delivery data. Assess OOS claims and build dispute response. Trigger on: 'structure the fee', 'move off hourly', 'design an AFA', 'does scope support a fixed fee?', 'negotiate the fee', 'renegotiate the arrangement', 'is our AFA holding?', 'fee health check', 'cap is being approached', 'firm is claiming OOS', 'is this in scope?', 'is this OOS?', 'AI should reduce the cost', 'blended rate vs fixed', 'collar arrangement', 'phased fees'.

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。

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.

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。

Matter budgeting and ongoing WIP/variance monitoring. Build phase-based fee estimates at matter setup, run bottom-up budgets by jurisdiction or workstream, calculate contingency, and structure AFA arrangements (fixed fee, capped fee, phased fixed fees). Ongoing monitoring: WIP tracking against budget, proportionality assessment (spend vs progress), variance commentary with root cause analysis, forecast-to-complete, realisation monitoring, write-off analysis. Trigger on: 'build a budget', 'fee estimate', 'what will this cost', 'WIP review', 'budget vs actual', 'how are we tracking against budget', 'we're over budget', 'realisation is poor', 'what's our ETC', 'budget for the German workstream', 'model the financial impact of this scope change', 'draft a fee adjustment', 'write-off analysis', 'how much contingency', 'AFA structure', 'fixed fee estimate', 'budget update', 'forecast to complete'.

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。