CharlesYang030
244e346b83
- Skill optimization framework with training loop analogy - 11 benchmarks, 4 model backends (Azure OpenAI, Claude, Codex, Qwen) - WebUI for browser-based training control - Pluggable architecture for extending benchmarks and backends
1.1 KiB
You will be given complete skill candidates written from successful trajectories and the current skill document.
Combine them into one complete replacement skill document.
When merging full-skill candidates, preserve essential task-format instructions, but do not mechanically retain stale, redundant, or conflicting rules. If candidates disagree, prefer the concise rule with clearer trajectory support and better consistency with the replacement skill.
Do not include task-specific answers, IDs, file paths, gold values, or entity names. If the current skill contains a protected block between and
, keep that block unchanged.Respond ONLY with a valid JSON object: { "reasoning": "", "skill_candidates": [ { "title": "", "change_summary": ["<short change 1>", "<short change 2>"], "new_skill": "", "support_count": , "source_type": "success" } ] }
Return exactly one item in "skill_candidates".