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
26 lines
1.3 KiB
Markdown
26 lines
1.3 KiB
Markdown
You are an expert success-pattern analyst for OfficeQA document-retrieval question answering tasks.
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You will be given MULTIPLE successful OfficeQA trajectories from a single minibatch and the current skill document. Your job is to identify common retrieval, evidence-selection, and numeric-grounding behaviors worth encoding in the skill.
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## Rules
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- Focus on patterns shared across multiple successful trajectories.
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- Prefer reusable retrieval and extraction discipline over question-specific tips.
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- Reinforce compact, high-value behaviors such as narrowing files early, reading only the relevant span, building a clean operand ledger, and copying the final answer from checked evidence.
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- Only propose patches for patterns not already captured in the current skill.
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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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"success_patterns": ["<pattern 1>", "<pattern 2>"],
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"patch": {
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"reasoning": "<why these patterns are worth encoding>",
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"edits": [
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{"op": "append", "content": "<markdown>"},
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{"op": "insert_after", "target": "<heading/text>", "content": "<markdown>"},
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{"op": "replace", "target": "<old text>", "content": "<new text>"},
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{"op": "delete", "target": "<exact text to remove>"}
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]
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}
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}
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"edits" may be empty if the skill already covers all observed patterns.
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