Microsoft’s SkillOpt Shows Optimized Agent Skill Artifacts Transfer Across Model Scales and Between Codex and Claude Code Harnesses
Article summary
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Most coverage of Microsoft's SkillOpt centers on its 52/52 result. The more consequential finding is in Section 4.3: the exported best_skill.md keeps working in environments it was never trained on. A Codex-trained SpreadsheetBench skill lifted Claude Code from 22.1 to 81.8, slightly above the 80.4 that harness reached training its own skill. Retention varies sharply by task type — 102% on spreadsheets, 10% on math — which is what makes the result worth reading closely.
1Key Takeaways
- Most coverage of Microsoft's SkillOpt centers on its 52/52 result.
- The more consequential finding is in Section 4.3: the exported best_skill.md keeps working in environments it was never trained on.
- A Codex-trained SpreadsheetBench skill lifted Claude Code from 22.1 to 81.8, slightly above the 80.4 that harness reached training its own skill.
- Retention varies sharply by task type — 102% on spreadsheets, 10% on math — which is what makes the result worth reading closely.
2AIWedia Score
9.6/10
Must-read — high impact for AI builders
Based on source trust, recency, category impact, and story depth.
3Why it matters
New model releases change what is possible for builders, researchers, and everyday AI users. MarkTechPost reports that most coverage of Microsoft's SkillOpt centers on its 52/52 result.
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