> in my book anything that is (I'm sure well intentioned) and injected to help the agent — but doesn't help it — is a waste of tokens

How do you tell, though? I guess what I'm asking is: the data you presented shows some crazy differences, but the token burn alone doesn't tell us enough. What was the output of the task like? Did the harnesses that burned fewer tokens give you as good a result as the ones that burned more?

I guess it's subjective, of course, but nearly everything about LLM use is...

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.