Agent skill · product strategy · datadrivenconstruction
multi-agent-estimation
Build multi-agent AI systems for construction estimation. Use CrewAI/LangGraph to orchestrate specialized agents: QTO agent, pricing agent, validation agent. Automate complex estimation workflows.
Why this skill is useful
Adds executable scripts and workflows for orchestrating multi-agent systems in construction estimation that are not available in public documentation.
What it needs
Requires ifcopenshell, python3 installed locally. Requires openai, qdrant account access. About 3k tokens when loaded. Last updated 2026-02-14. 265 stars on the source repository.
What this skill does
Multi-Agent Estimation System Overview In 2026, AI agents are moving from single-task assistants to orchestrated multi-agent systems. This skill enables building a crew of specialized AI agents that work together to automate construction estimation. "Thanks to LLM nodes, you can simply ask ChatGPT, Claude, or any advanced AI assistant to generate n8n automation pipelines — whether for extracting tables from PDFs, validating parameters, or producing custom QTO tables — and get ready-to-run workflows in seconds." — Artem Boiko Architecture Quick Start with CrewAI Define Tasks Run the Crew n8n Integration Why Multi-Agent in 2026? Single Agent Multi-Agent -------------- ------------- One prompt, one task Specialized experts collaborate Context limits Distributed memory Single point of failure Redundancy and validation Hard to debug Clear responsibility Generic output Domain-specific quality Requirements Resources CrewAI: https://www.crewai.com LangGraph: https://langchain-ai.github.io/langgraph/ n8n AI Agents: https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.agent/
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills datadrivenconstruction/multi-agent-estimation