- 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
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You will be given complete skill candidates 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. Prefer concise guidance with clear 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": "failure|success|mixed" } ] }
Return exactly one item in "skill_candidates".