I agree, only looking at the token burn is not enough

in this case it was 10 tasks and all harnesses could complete the tasks successfully, of course now the question is: will this hold for more and more complex tasks but I had to start somewhere :)

Checksum: Compute a file’s SHA-256 checksum and save the exact digest to an output file.

Log correlation: Correlate nested service logs to identify and summarize a request’s complete execution path.

CSV report: Parse quoted CSV data and aggregate paid orders and exact decimal totals by region.

JSONL join: Join related JSON Lines datasets and produce a correctly grouped and ordered report.

Archive repair: Find the correct version of a corrupted file in a tar archive and restore it.

SQLite migration: Safely migrate a SQLite database schema and verify the resulting data and constraints.

Python bug fix: Repair an interval-merging implementation so it passes visible and hidden edge-case tests.

Python CLI: Implement a robust command-line program that reads JSONL and reports validated statistics.

Multi-file feature: Add an atomic feature across a small Python package, CLI, and associated tests.

Pipeline repair: Fix a Make, shell, and Python reporting pipeline so it handles general input and passes verification.

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