Linkup Build Workflow
openbooklet.com/s/linkup-build-workflowopenbooklet.com/s/[email protected]GET /api/v1/skills/linkup-build-workflowAdapted from Linkup's MIT source pack (2026-09-15 snapshot). Read the [2026-10-29 compatibility corrections](sources-and-compatibility.md) before copying API examples. Examples require an approved connection/budget; do not run paid calls for validation.
Adaption AI SDK for synthetic data augmentation and dataset adaptation. Use when building data pipelines with the Adaption Python SDK, uploading datasets (local files, Hugging Face, Kaggle), running augmentation/adaptation jobs, configuring brand controls (hallucination mitigation, safety categories, length), recipe specifications (reasoning traces, deduplication, preference pairs, prompt rephrase), evaluating dataset quality, downloading results, or any workflow involving `pip install adaption`, `from adaption import Adaption`, Adaptive Data, or the adaptionlabs.ai API. Also trigger when the user mentions synthetic data generation for fine-tuning, dataset augmentation pipelines, DPO preference pair generation, or grounding-based hallucination reduction on training data.
Build typed extraction with the unofficial VianneyMI/clairic Python SDK for Cloudflare's Clairic model. Use for Clairic, AsyncClairic, Pydantic answer models, annotated field specs, null/confidence handling, estimates, or forecasts. Also trigger for Workers AI Clairic REST/bindings, SDK-versus-service differences, and offline MockTransport tests.
Build GenAI and agentic apps with genai-tk (tclatos/genai-tk), a YAML-driven IoC toolkit over LangChain, DeepAgents, DeerFlow, and Prefect. Use this skill whenever the user mentions genai-tk, genai_tk, `cli init`, `cli agents run`, `LangchainAgent`, `create_harness`, `get_llm`, `global_config()`, `model_id@provider`, `agents:`/`agent_defaults:` profiles, `RetrieverFactory`, `cli workflow`, or BAML extraction. Also trigger for app_conf.yaml, GENAITK_PROFILE, CodeAct, OpenSandbox, decision models, or "the toolkit".
Adapted from Linkup's MIT source pack (2026-09-15 snapshot). Read the [2026-10-29 compatibility corrections](sources-and-compatibility.md) before copying API examples. Examples require an approved connection/budget; do not run paid calls for validation.
Use Linkup Extract for structured rows from one listing page. Trigger for linkup-extract, /v1/extract, listing-page extraction, row schemas, and Linkup closed-beta access. Also trigger for directory, catalog, or event-list extraction and choosing Extract versus Search or Fetch.
Use Linkup Fetch to read one known URL as Markdown. Trigger for linkup-fetch, known-page retrieval, renderJs, or reading a discovered Linkup source. Also trigger for choosing Fetch versus Search scraping and handling incomplete, blocked, or JavaScript-rendered pages.
Use Linkup Research for long-running multi-source investigations. Trigger for linkup-research, /v1/research, research job IDs, polling, and source-backed reports. Also trigger for bounded research submission, terminal job states, partial results, and avoiding duplicate paid research jobs.
Use Linkup Search for web discovery and source-backed answers. Trigger for linkup-search, search depth, outputType, retrieval-plan queries, or Linkup web lookups. Also trigger for search-then-fetch chains, structured web evidence, and choosing Flash versus standard or deep search.
Use Linkup Workflow to turn a business goal into a bounded multi-step web-evidence workflow. Trigger for linkup-workflow, Linkup workflow recipes, enrichment, monitoring, and search-then-fetch plans. Also trigger for routing Search, Fetch, Research, and Extract with evidence, budgets, and stop conditions.
Build production AI agents with Mastra and TypeScript. Use this skill whenever the user mentions Mastra, @mastra/core, createTool, createWorkflow, Agent, Memory, MCPClient, or Mastra evals, wants tool-calling agents, durable workflows, RAG, human approval, or agent deployment. Also trigger for agent architecture, guardrails, memory scoping, and observability.
Build AI agents with Hugging Face's SmolAgents framework. Use when creating code-executing agents, tool-calling agents, multi-agent systems, agentic RAG, text-to-SQL pipelines, web browsing agents, or any multi-step AI workflows. Covers CodeAgent, ToolCallingAgent, custom tools, MCP integration, memory management, secure code execution (E2B, Docker, Blaxel), and model configuration (HF Inference, LiteLLM, Transformers, Ollama).
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