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WILLOSCAR/research-units-pipeline-skills

A file-first research execution system that uses AI models as semantic judgment providers within structured pipeline workflows.

461 stars Python LLMOps · EvalAgents
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This project provides a multi-layer harness for converting open-ended research goals into protocolized execution with durable artifacts and evaluable evidence surfaces. It structures research as units with declared inputs/outputs, acceptance criteria, and guardrails. The system combines a workflow protocol layer, execution ledger, evidence loop for run validation, and a capability surface with reusable AI-driven skills. It is designed to make AI-assisted research auditable, resumable, and improvable through visible contracts and structured intermediate artifacts.

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