「structured」の検索結果

Validate database schema, migrations, indexes, and query behavior with structured pass/fail reporting.

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Run frontend functional, accessibility, visual, and performance test planning with structured reports.

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Review a GitHub pull request and provide structured feedback with severity ratings

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Schema.org structured data audit and generation optimized for AI discoverability — detect, validate, and generate JSON-LD markup

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SOP for structured documentation lifecycle. Trigger when: - Designing, writing, reviewing, or auditing large-scale documentation structures. - Navigating phases: Audit, Plan, Draft, Review, Verify. - Prompt contains: /doc, doc workflow, audit, draft, review, verify, documentation debt.

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Structured Domain Modeling Architecture (SDMA) principles. Trigger when: - Designing domain models, building ologs (ontological graphs), verifying protocol equivalence, selecting schemas, or measuring architectural entropy. - Modeling structures using Category Theory, Coalgebra, or Information Theory. - Prompt contains keywords: sdma, olog, Category Theory, Coalgebra, Information Theory, entropy, schema, pullback, functor, monad.

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Capture a POC's signed-off behaviour into its spec ledger (`<poc>/spec.json`) — the structured, walkable behaviour contract graduation freezes as authoritative. Run at the moment a behaviour gets signed off ("ok, this works"), while it's fresh — the same trigger as the HANDOFF.md update, but capturing the *behaviour as a testable entry* rather than the *decision as prose*. Use when a POC behaviour is approved, or asked to "capture this spec", "write the spec entry", "log this to the ledger".

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Activates a structured decorator framework that modifies how Claude reasons, responds, and formats output. Trigger this skill whenever a prompt contains one or more markers prefixed with +++, such as +++Reasoning, +++StepByStep, +++Tone(style=formal), or any other +++ marker, regardless of what follows in the prompt. Also trigger when the user asks to activate, list, or clear decorators, references the prompt decorator framework, or uses phrases like "use decorator", "apply +++", or "what decorators are available". This skill must be active for any +++ marker to take effect.

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Full SEO audit for rtk-ldp landing pages — JSON-LD schemas, OG tags, meta, robots, titles. Outputs a structured pass/fail checklist with score.

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Get structured data from catalogued providers instead of scraping pages. Firecrawl Alexandria is a library of official APIs, licensed publishers, and Firecrawl indexes (financial series, company data, government contracts and law, podcasts, listings, research). Use this skill when the user needs records, listings, prices, filings, transcripts, or a dataset, says "is there an API for", "find a data source for", "pull the records", "get the dataset", or when assembling the answer by scraping would take many pages that one provider call returns directly. Discover with search, inspect with find_tools, execute with scrape.

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Efficiently extract and convert the contents of any local file—such as PDF, DOCX, DOC, ODT, RTF, XLSX, XLS, or HTML—into clean, well-formatted markdown saved to disk. Use this skill whenever the user requests to parse, read, or extract information from a file on their computer, including phrases like “parse this PDF”, “convert this document”, “read this file”, “extract text from”, or when a local file path (not a URL) is provided. This skill offers advanced options like generating AI-powered summaries and answering questions based on the file's content. Prefer this tool over `scrape` when handling local files to deliver precise, structured outputs for downstream tasks.

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Use for complex coding requests that need a structured implementation plan with dependencies, checkpoints, and verification criteria.

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Use when implementation is complete, all tests pass, and you need to decide how to integrate the work — guides completion of development work by presenting structured options for merge, PR, or cleanup

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Use before build, fix, debug, review, or docs implementation; converts prompts into structured intent JSON with goal, constraints, and routing.

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Use Exasol Cloud Storage Extension workflows for moving structured object-storage files through extension UDFs. Covers setup prerequisites, the fixed `CLOUD_STORAGE_EXTENSION.IMPORT_PATH` and `CLOUD_STORAGE_EXTENSION.EXPORT_PATH` entrypoints used with `FROM SCRIPT` and `INTO SCRIPT`, Parquet, Avro, ORC, and Delta files on S3, Google Cloud Storage, Azure Blob Storage, Azure Data Lake, HDFS, and Alluxio, connection-object credential patterns, and routing away from native IMPORT or EXPORT when the extension path is the right fit.

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Create structured GitHub issues from text, screenshots, or lists, with acceptance criteria and preserved reporter context. Use for filing bugs/features, batch creation, or template cleanup. Don't use for resolving, triaging, or deep issue analysis.

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Extract text, tables, and structured data from documents using Azure Document Intelligence (@azure-rest/ai-document-intelligence). Use when processing invoices, receipts, IDs, forms, or building custom document models.

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Azure AI Document Intelligence SDK for .NET. Extract text, tables, and structured data from documents using prebuilt and custom models. Use for invoice processing, receipt extraction, ID document analysis, and custom document models. Triggers: "Document Intelligence", "DocumentIntelligenceClient"...

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Actorization converts existing software into reusable serverless applications compatible with the Apify platform. Actors are programs packaged as Docker images that accept well-defined JSON input, perform an action, and optionally produce structured JSON output.

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Design spec with 98 rules for building CLI tools that AI agents can safely use. Covers structured JSON output, error handling, input contracts, safety guardrails, exit codes, and agent self-description.

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Convert human-written CLAUDE.md into AI-native structured-label format. Battle-tested across 4 models. Same rules, fewer tokens, higher compliance.

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Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.

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Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports.

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Fetch any X/Twitter post as clean LLM-friendly JSON. Converts x.com, twitter.com, or adhx.com links into structured data with full article content, author info, and engagement metrics. No scraping or browser 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。