「documents」の検索結果

Azure AI Search SDK for Python. Use for vector search, hybrid search, semantic ranking, indexing, and skillsets.

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Azure Cosmos DB JavaScript/TypeScript SDK (@azure/cosmos) for data plane operations. Use for CRUD operations on documents, queries, bulk operations, and container management.

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Azure AI Content Understanding SDK for Python. Use for multimodal content extraction from documents, images, audio, and video.

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Extract text, tables, and structured data from documents using prebuilt and custom 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.

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Azure AI Document Translation SDK for batch translation of documents with format preservation. Use for translating Word, PDF, Excel, PowerPoint, and other document formats at scale.

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Implements AWS Nitro Enclave-based confidential computing environments with cryptographic attestation, KMS policy integration using PCR-based condition keys, and secure vsock communication channels. The practitioner builds enclave images, configures attestation-aware KMS policies, validates attestation documents against the AWS Nitro PKI root of trust, and establishes isolated computation pipelines for processing sensitive data such as PII, cryptographic keys, and healthcare records. Activate...

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Extract text from PDFs/scans (pymupdf, marker-pdf).

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Search the Unique AI Platform knowledge base using the unique-cli search command, with automatic per-turn citation tracking so cited facts render as clickable reference chips and `<sup>N</sup>` footnotes on the Unique platform. Use whenever the user asks to find, search, or query documents or content on Unique, including filtering by folder or metadata. Also covers `unique-cli read <cont_id>` for reading the full indexed text of a document when its content ID is already known. NOTE: This search uses combined vector + full-text indexing. Excel (.xlsx/.xls), CSV (.csv), and image files are NOT full-text indexed, so they will not appear in search results. To locate these file types, use the unique-cli-file-management skill instead (browse folders with `unique-cli ls` to find them by name).

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Use for the infrastructure implementation phase after the backend follows the model — turning documents/infra-design decisions into infra/ (local container stack, production IaC), wiring cradle.json infra.up / infra.containers, adding IaC validate and image build steps to CI, and, once E2E passes, applying production once in a sandbox and recording it in the deployment table. Not for designing infrastructure (infra-design).

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Use to create, update or verify the Lean 4 executable specification under lean/ from the exploration documents (event-timeline, hotspots, ubiquitous-language, ux-review, model-review) — initial generation, reflecting a session's results, fixing lake build, adding a command or a screen, checking a proposed feature against the model. Formalization questions go to model-review.md as MQ, never to the expert directly.

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Use for the infrastructure design phase between domain exploration and Lean formalization — deriving non-functional requirements from the Lean model, deciding environments (test/local/prod), identity provider, the relational database's product, version and configuration (the storage kind is fixed to relational), ID representation, and the list of things NOT to build. Produces documents/infra-design with INFRA-D/A/Q entries.

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Use to start a new product on Cradle or to add the Cradle skeleton to an existing repository — writes cradle.json, the project-facts instruction (.apm/instructions/project.instructions.md), documents/{ddd,ai-notes,infra-design,codebase} templates, CI, and a compiling Lean executable-spec scaffold with a minimal example domain, mockup and CLI.

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Use when adding or changing REST endpoints or the OpenAPI contract — deriving endpoints from the Lean commands and views, writing documents/codebase/openapi.yaml first, then regenerating the backend controller interfaces and the frontend client.

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Lesson-plan generation and compliance-check desk (Busabase App-in-Skill) for a dean of studies or training-program lead. Use when the user invokes $kelly-lesson or /kelly-lesson, mentions lesson plan, 备课, 教案, teaching plan compliance, curriculum template, dean of studies, teacher training, wants lesson plans drafted from curriculum materials and the school template, teacher drafts imported and checked against quality standards, a review queue for approving plans, or a library of approved plans exported as documents.

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Build and operate a WeChat editorial content library in Busabase. Use when a team wants reusable source material, linked article drafts, an editorial kanban board, a publishing calendar, or a source-check and review queue. Keeps source documents and article scheduling in two linked Bases; does not publish to WeChat or approve an agent's own proposals.

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Legal casebase App-in-Skill (Busabase-backed) intake and anonymization QA desk for law-firm judgment and award documents. Use when the user invokes $kelly-legal-casebase-ingest, mentions internal case database, 智能案例库, 裁判文书入库, 脱敏, 结构化, 分类标注, 案例审核, 质量验收, or wants a Busabase-backed desk where reviewers approve, revise, or block agent-prepared case records before they become searchable knowledge assets.

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Busabase-backed App-in-Skill idea desk that works like a business consultant — it interrogates a product or business idea until it is sharp, then carries it through professional, visual BRD, MRD, and PRD documents. Use when the user invokes $kelly-ideas or /kelly-ideas, wants to capture or shape a product/business idea, asks for a BRD/MRD/PRD, says they have a product idea but cannot describe it clearly, or wants an idea shaped into a spec that $kelly-app-skill-creator can build into a real app.

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Produces and audits externally published marketing copy where factual claims must survive scrutiny — landing pages, product pages, launch emails, one-pagers, search/social metadata. Every statistic, competitor comparison, capability description, and proof point is traced to a fetched source or the project's own documents, labeled, and adversarially verified before it ships. Use when writing or rewriting high-stakes marketing copy; when copy must pass legal, compliance, brand, or claims review; when the audience is skeptical technical or executive buyers; when the company has little or no customer proof yet (pre-launch, zero customers); or when auditing existing copy for unsupported claims, invented capabilities, unverified statistics, or AI-slop language.

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Drive the AgentWiki knowledge platform via its CLI (`@aiagentwiki/cli`, command `agentwiki`) and MCP server (`https://api.agentwiki.cc/mcp`). Use this skill whenever the user mentions AgentWiki, agentwiki.cc, app.agentwiki.cc, the `agentwiki` CLI, the `@aiagentwiki/cli` npm package, the AgentWiki MCP server, or asks to create/list/search/share/publish documents, manage folders or tags, upload files or images, deploy static sites, traverse a knowledge graph, or query their AgentWiki workspace from the terminal or as an AI agent. Triggers include phrases like "agentwiki login", "aw_… API key", "create a doc on agentwiki", "search my wiki", "publish to agentwiki", "agentwiki MCP", or any task that would otherwise require opening app.agentwiki.cc manually.

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Structure any communication artifact — documents, reports, plans, issue comments, PR descriptions, emails, presentations, slides, charts, and dashboards — using Jean-luc Doumont's "Trees, Maps, and Theorems" method. Use whenever the goal is to get a message across to a busy audience, when asked to improve clarity or structure of writing or slides, or when a deliverable will be judged by whether readers act on it, not just read it.

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Deploys canary files (honeytokens) across file systems to detect ransomware encryption activity in real time. Uses strategically placed decoy documents monitored via file integrity monitoring or OS-level watchdogs to trigger alerts when ransomware modifies or encrypts them. Activates for requests involving ransomware canary deployment, honeyfile setup, deception-based ransomware detection, or file integrity monitoring for encryption.

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Designs and documents structured incident response playbooks that define step-by-step procedures for specific incident types aligned with NIST SP 800-61r3 and SANS PICERL frameworks. Covers playbook structure, decision trees, escalation criteria, RACI matrices, and integration with SOAR platforms. Activates for requests involving IR playbook creation, incident response procedure documentation, response runbook development, or SOAR playbook design.

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Perform static analysis of malicious PDF documents using peepdf, pdfid, and pdf-parser to extract embedded JavaScript, shellcode, and suspicious objects.

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