agent-research-aggregator

Aggregating scattered agent logs for paper writing involves scanning common cache directories to discover experiment artifacts, then using LLM-assisted batch extraction to format them into structured markdown input files like idea.md and experimental_log.md.

Preparing experimental logs for academic paper inputs requires deterministic discovery and project-aware extraction to filter multiple projects into a single coherent research thread, producing structured PaperOrchestra-ready files.

Yes, this log aggregation workflow is specifically designed for scenarios where workspace inputs like idea.md and experimental_log.md are missing, automatically generating them from your existing scattered experimentation histories.

Data quality auditing during experiment extraction works by generating deterministic discovery manifests and validating extracted JSON structures, producing an audit report that highlights data quality gaps and conflicts.

Handling multiple projects in agent cache directories involves a project-aware extraction workflow that forces selection when multiple projects are detected, then re-filters the logs to isolate a single coherent research thread.