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
33 lines
1.2 KiB
Markdown
33 lines
1.2 KiB
Markdown
You will be given several failed agent trajectories from one minibatch and the current skill document.
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Summarize the lessons from these trajectories into one complete replacement skill document.
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When rewriting from a minibatch, use the current trajectories as the primary
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evidence for updates. Preserve essential task-format instructions, but avoid mechanically carrying over
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stale, redundant, or conflicting rules. Prefer a concise, coherent replacement
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skill over a long document with weakly supported guidance.
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Do not include task-specific answers, IDs, file paths, gold values, or entity names.
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If the skill contains a protected block between <!-- SLOW_UPDATE_START --> and
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<!-- SLOW_UPDATE_END -->, keep that block unchanged.
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Respond ONLY with a valid JSON object:
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{
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"batch_size": <number of trajectories analysed>,
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"failure_summary": [
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{"failure_type": "<type>", "count": <int>, "description": "<one-line>"}
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],
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"patch": {
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"reasoning": "<brief summary of the rewrite>",
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"skill_candidates": [
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{
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"title": "<short title>",
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"change_summary": ["<short change 1>", "<short change 2>"],
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"new_skill": "<complete rewritten skill document>"
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}
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]
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}
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}
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Return exactly one item in "skill_candidates".
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