Extract per-model token usage from resultLine.modelUsage (including
cache tokens and exact API cost), flow CostEntry[] through EvalCollector,
aggregate in finalize(). Extend CostEntry with cache_read_input_tokens,
cache_creation_input_tokens, cost_usd. computeCosts() prefers exact
cost_usd over MODEL_PRICING when available (~4x more accurate with
prompt caching).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
EvalCollector accumulates test results during eval runs, writes JSON to
~/.gstack-dev/evals/{version}-{branch}-{tier}-{timestamp}.json, prints
a summary table, and automatically compares against the previous run.
- EvalCollector class with addTest() / finalize() / summary table
- findPreviousRun() prefers same branch, falls back to any branch
- compareEvalResults() matches tests by name, detects improved/regressed
- extractToolSummary() counts tool types from transcript events
- formatComparison() renders delta table with per-test + aggregate diffs
- Wire into skill-e2e.test.ts (recordE2E helper) and skill-llm-eval.test.ts
- 19 unit tests for collector + comparison functions
- schema_version: 1 for forward compatibility
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>