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Measuring Codex workflow usage

bin/codex_usage.py is a read-only, standard-library report over rollout JSONL files the user names explicitly. It parses each selected line but extracts and outputs metadata only; prompts, responses, tool arguments, and secrets are never reported.

python bin/codex_usage.py parent.jsonl --child child-a.jsonl --child child-b.jsonl
python bin/codex_usage.py --json parent.jsonl

The report uses the last cumulative usage snapshot per session, never sums snapshots, and de-duplicates repeated files and session IDs. Cached input is included in input; reasoning is included in output, so those subset counters are reported separately rather than added again. All observed model and effort values are listed. Missing counters make the total unknown/incomplete, not zero. Coordination-call counts describe tool calls, not their cause or value.

Explicit children are summed only when every selected session has counters. Full-history forks may repeat inherited parent events; the current schema does not always expose a reliable baseline, so child aggregation carries an over-count warning rather than claiming an exact task total. The report never converts tokens to billing or subscription cost.

Activate the workspace policy

After this Brain change merges, update the workspace-root AGENTS.md through the tracked installer:

bash PyAutoBrain/bin/install.sh --write-workspace-policy
bash PyAutoBrain/bin/install.sh --check-workspace-policy

The writer migrates the known legacy delegation text, preserves the root file and any symlink to it, and fails closed on malformed markers. Do not run it before MODEL_DELEGATION.md exists on the installed branch because the generated root policy links to that reference.

Comparison protocol

After 3–5 comparable completed tasks, record for each task:

  • selected parent and child rollout paths and whether aggregation is reliable;
  • observed models and reasoning-effort values;
  • input, cached-input, output, and reasoning-output counters;
  • duration and coordination-call count;
  • outcome quality and independent-review verdict;
  • number of correction loops or user interventions.

Compare medians only across similar task types and report unknown sessions. Treat the current Astra Medium configuration as the baseline; do not change local model configuration merely to manufacture a comparison. Do not claim savings until both usage and outcome/correction evidence support it.

Static context budgets

Use context_efficiency.md for the September follow-up, Mind’s token-load checker and post-merge root activation. Static byte/line reductions are not measured task-token savings.