topics/document_intelligence/SKILL.md
# Document Intelligence Skill ## Purpose Convert unstructured documents into LLM-ready Markdown using MarkItDown, with deterministic defaults and governance-compliant wrappers. References: - `core/GLOBAL_RULES.md` - `core/LOGGING_STANDARD.md` - `core/TESTING_STANDARD.md` - `core/STRUCTURED_OUTPUT_STANDARD.md` ## When to use - You need a standard document-to-Markdown conversion path. - You need deterministic conversion that does not require an LLM by default. - You need optional visual/LLM int
npx skillsauth add azadehtavassoli/agent-skills-toolkit topics/document_intelligenceInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Convert unstructured documents into LLM-ready Markdown using MarkItDown, with deterministic defaults and governance-compliant wrappers.
References:
core/GLOBAL_RULES.mdcore/LOGGING_STANDARD.mdcore/TESTING_STANDARD.mdcore/STRUCTURED_OUTPUT_STANDARD.mdio.BytesIO + convert_stream).pydantic.BaseModel.content_bytes.llm_client.convert_stream.development
# Demo Pipeline — SKILL ## What this skill is for Use this skill to implement a long-term, maintainable demo pipeline for product demos. The aim is to create a reusable machine for demos, not a one-off screen recording. ## Canonical design A good demo pipeline has these responsibilities: ### 1. Scenario definition A scenario defines: - objective - audience - core message - preconditions - interaction steps - hero moment - output profiles Use: - `brief.md` for human-readable story and intent
development
# UI TOPIC SKILL ## Purpose Define deterministic UI implementation patterns with accessibility, component decomposition, and reproducible behavior. ## Framework Adapters - `topics/ui/frameworks/streamlit/` - `topics/ui/frameworks/gradio/` ## Hard MUST Rules - Component contracts must be explicit. - Accessibility checks are mandatory. - UI state transitions must be predictable and testable. - Framework-specific UI logic MUST remain inside adapter folders.
testing
# RAG TOPIC SKILL ## Purpose Define deterministic retrieval-augmented generation pipelines with grounded outputs and measurable evaluation. ## Hard MUST Rules 1. Retrieval, ranking, and generation stages MUST be explicit and logged. 2. Groundedness and citation integrity MUST be validated in tests. 3. Evaluation outputs MUST be structured and reproducible. 4. No real LLM/provider calls in automated tests. 5. Structured output models are mandatory.
tools
# MCP TOPIC SKILL ## Purpose Implement Model Context Protocol (MCP) servers and clients with the official MCP Python SDK (`mcp`) using deterministic, transport-explicit patterns. ## When to Use - Building MCP servers over `stdio` for local process integration - Building MCP servers over Streamable HTTP for remote/networked access - Building MCP clients that connect to local (`stdio`) or online (`streamable-http`) servers - Defining reusable MCP templates and transport-specific implementation p