SkillOpt：https://github.com/microsoft/SkillOpt is a text-space optimizer developed by a team of researchers from Microsoft, Shanghai Jiao Tong University, Tongji University, and Fudan University.
SkillOpt trains a single natural-language skill document while the target model stays frozen. An optimizer model reads scored rollouts and proposes bounded add/delete/replace edits. A held-out selection split accepts an edit only when the score strictly improves. The exported artifact is one file, best_skill.md .
The transfer tables report three columns. Baseline is the target’s no-skill score. Direct is SkillOpt trained in-domain on that exact target. Transferred applies a skill trained elsewhere, with no further optimization.
The useful comparison is not transferred versus direct. It is how much of the in-domain gain survives the move.
Skills were trained on GPT-5.4 and deployed on smaller variants.
Two rows deserve attention. SpreadsheetBench on GPT-5.4-mini keeps 82% of the in-domain gain. That is close to free reuse. The LiveMath row on GPT-5.4-nano is stranger: the transferred skill scores 28.8 against an in-domain SkillOpt result of 27.2. The paper reads this as evidence that some learned procedures are target-model agnostic.
The GPT-5.4-nano SpreadsheetBench row is the weak one at 16%. Retention is not uniform, and the paper does not claim it is. Its stated bound is narrower: no row falls below the target’s no-skill baseline.
Note the scope. All four rows stay inside one GPT family. Cross-family transfer, such as GPT to Qwen：https://qwen.ai/blog?id=qwen3.5, is not tested.
This is the section that matters most for deployment. All rows use GPT-5.5.
The first row is the headline. A skill optimized inside Codex：https://openai.com/index/introducing-codex/ lifted Claude Code：https://www.anthropic.com/claude-code from 22.1 to 81.8. That slightly exceeds the 80.4 Claude Code reached by training its own skill from scratch.
The two harnesses expose different tool and file APIs and different command surfaces. A skill that survives that shift is not encoding command recipes. The research paper：https://arxiv.org/pdf/2605.23904 attributes SpreadsheetBench’s portability to workbook-level procedures: structure-first inspection, formula-aware verification, and static-value materialization. Those hold regardless of which CLI runs the Python.
LiveMath tells the opposite story. Codex → Claude Code retains only 10% of the in-domain gain. The asymmetry is worth sitting with. Procedural skills — how to inspect, verify, and format — appear to be the portable class. Reasoning-heavy skills appear more tied to their training environment.
There is no Direct column here. No in-domain SkillOpt run on Omni-MATH is reported, so the comparison is against no-skill only. Gains are positive across all three model scales but small. The research paper’s reading is that the skill retained reusable mathematical procedure after both the test instances and the answer-format conventions changed.
The mechanism is stated plainly in the research paper. All three execution modes: direct chat, Codex, Claude Code – consume the same best_skill.md file format. That shared contract is what makes the cross-harness experiment possible in the first place.
The Codex harness renders the current skill to a per-task SKILL.md alongside task files, then reads back a compact execution trace. The Claude Code harness mirrors the same workspace contract through the claude CLI. Neither harness gets a bespoke skill format.
The artifact’s shape supports portability too. Final skills run 379 to 1,995 tokens across the six benchmarks, with a median near 920. They are assembled from 1 to 4 accepted edits. The paper’s Figure 4 samples one learned rule per benchmark, and all are procedural rather than instance-specific. The SpreadsheetBench rule, verbatim: inspect workbook structure and formulas, then write evaluated static values across the full requested target range instead of relying on Excel recalculation.
Training cost is paid once, offline, and measured. The research paper reports 0.6M to 46.4M training tokens per absolute test point, depending on benchmark. SpreadsheetBench sits at 0.6M per point; DocVQA at 46.4M. The optimizer model runs only during training and adds zero inference-time calls at deployment.
If a skill trained in one harness holds up in another, that one-time cost spreads across environments. The Codex → Claude Code SpreadsheetBench result is the existence proof. It also implies you can optimize where tooling is cheapest and deploy where the product lives.
The audit angle is separate and underrated. The deployed artifact is a text file a domain practitioner can read in minutes. Every change to it is traceable: each step records an edit_apply_report.json with per-edit accept and skip status. Portability plus inspectability is a different operational posture than shipping fine-tuned weights.
Resources: Paper：https://arxiv.org/abs/2605.23904, GitHub：https://github.com/microsoft/SkillOpt, Project page：https://microsoft.github.io/SkillOpt/, Docs：https://github.com/microsoft/SkillOpt/blob/main/docs/index.md, PyPI：https://pypi.org/project/skillopt/ and Demo video：https://youtu.be/JUBMDTCiM0M
Baselines referenced: GEPA：https://arxiv.org/abs/2507.19457, TextGrad：https://arxiv.org/abs/2406.07496, EvoSkill：https://arxiv.org/abs/2603.02766 and Trace2Skill：https://arxiv.org/abs/2603.25158
Benchmarks referenced: SearchQA：https://arxiv.org/abs/1704.05179, SpreadsheetBench：https://arxiv.org/abs/2406.14991, DocVQA：https://arxiv.org/abs/2007.00398, LiveMathematicianBench：https://arxiv.org/abs/2604.01754 and ALFWorld：https://openreview.net/forum?id=0IOX0YcCdTn
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