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Garry TanandClaude Opus 4.7
243e013ea0
Three canonical 225-row receipts (3 variants × 25 fixtures × 3 seeds per model). Each receipt header binds (model, prompt_template_hash, fixtures_hash, harness_sha, ts) so the published SKILL.md numbers are reproducible. Training corpus (n=20, lenient): baseline | Opus 81.7% | Sonnet 86.7% | Haiku 73.3% | 25KB functional-areas | Opus 98.3% | Sonnet 100% | Haiku 88.3% | 13KB resolver-of-resolvers | Opus 63.3% | Sonnet 41.7% | Haiku 65.0% | 10KB functional-areas beats baseline by +13 to +17pp across all three models at 48% the size. resolver-of-resolvers' Sonnet collapse (41.7%) is the SKILL.md "compression without dispatcher clause is broken" claim, observed. Held-out (n=5, lenient) saturates at 100% across most cells (Sonnet × resolver-of-resolvers is 73.3% — the same failure mode visible on a smaller sample). ~$3 API spend across all three runs. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>