os-experiment-log

To persist and query agent OS experiment logs, this Skill automates writing one dated file per run to a context/experiment-log/ directory and updates an index.md. This creates a structured, searchable history of qualitative and numeric results from verifier, tester, planner, and orchestrator activities.

Indexing experiment outcomes for traceability is handled by maintaining an index.md file aggregating dated run logs. This provides a searchable overview of past experiments, allowing you to surface results, comparisons, and gap analyses using keyword and date-range queries.

Yes, you need Python to automate tracking of OS experiment outcomes. The workflow requires a Python-based script named experiment_log.py to write dated run files and maintain the index.md within the context/experiment-log/ directory.

Yes, you can search past experiment logs by keyword and date range. The index.md provides a searchable overview of past experiments, enabling you to quickly surface specific qualitative and numeric results, comparisons, and gap analyses across verifier and planner runs.

Centralized logging for agentic-os experiment runs works by creating one date-stamped file per run inside a dedicated context/experiment-log/ directory. It captures qualitative and numeric results across activities, updating an index for quick queries and traceability.