Systematic methodology for optimizing agent skills using deep-learning-style approach.

Based on arXiv:2605.23904 by Microsoft, Shanghai Jiao Tong University, Tongji University, and Fudan University.

SkillOpt treats skill documents as trainable parameters and applies bounded, validated, iterative updates - similar to gradient descent in deep learning, but operating in text space.

✅ Use when:

• Skill has inconsistent performance
• You have execution feedback (success/failure data)
• Manual edits aren't improving results
• Need to prevent regression during updates

❌ Skip when:

• No feedback data available
• Skill is already performing well
• One-off tasks

1. Collect feedback: Run 20-40 tasks with current skill, record outcomes
2. Separate: Split into successes and failures
3. Analyze: Feed failures → analyst_error.md, successes → analyst_success.md
4. Merge: Use merge prompts to combine proposals
5. Apply: Limit to 3-5 edits per iteration
6. Validate: Test on held-out cases before accepting
7. Iterate: Repeat until performance stabilizes

skillopt-methodology-skill/
├── SKILL.md                          # Main skill definition
├── README.md                         # This file
└── references/
    ├── methodology.md                # Complete methodology guide
    ├── design-principles.md          # Core design principles
    └── prompts/
        ├── analyst_error.md          # Failure analysis prompt
        ├── analyst_success.md        # Success analysis prompt
        ├── merge_failure.md          # Merge failure proposals
        ├── merge_success.md          # Merge success proposals
        ├── merge_final.md            # Final merge (failure-priority)
        ├── ranking.md                # Rank and select top edits
        ├── slow_update.md            # Epoch-wise slow update
        └── meta_skill.md             # Optimizer memory

• 52 evaluation cells (6 benchmarks × 7 models × 3 harnesses)
• Best or tied-best on ALL 52 cells
• GPT-5.5 improvements: +23.5 (chat), +24.8 (Codex), +19.1 (Claude Code)
• vs. strongest baseline: +5.4 points average

MIT

@article{skillopt2026,
  title={SkillOpt: Controllable Text-Space Optimization for Agent Skills},
  author={Microsoft Research and Shanghai Jiao Tong University and Tongji University and Fudan University},
  journal={arXiv preprint arXiv:2605.23904},
  year={2026}
}