798e89605d
TeamLeaderRunner を4モジュールに分割（execution, aggregation, common, streaming）し、 パート完了時にキュー残数が refill_threshold 以下になると追加タスクを動的に生成する worker pool 型の実行モデルを実装。ParallelLogger に LineTimeSliceBuffer を追加し ストリーミング出力を改善。deep-research ピースに team_leader 設定を追加。
32 lines
1.4 KiB
Markdown
32 lines
1.4 KiB
Markdown
Decompose the research plan (or additional research instructions) into independent subtasks and execute the investigation in parallel.
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**What to do:**
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1. Analyze research items from the plan and decompose them into independently executable subtasks
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2. Include clear research scope and expected deliverables in each subtask's instruction
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3. Include the following data saving rules and report structure in each subtask's instruction
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**Subtask decomposition guidelines:**
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- Prioritize topic independence (group interdependent items into the same subtask)
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- Avoid spreading high-priority items (P1) across too many subtasks
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- Balance workload evenly across subtasks
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**Rules to include in each subtask's instruction:**
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Data saving rules:
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- Write data per research item to `{report_dir}/data-{topic-name}.md`
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- Topic names in lowercase English with hyphens (e.g., `data-market-size.md`)
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- Include source URLs, retrieval dates, and raw data
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External data downloads:
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- Actively download and utilize CSV, Excel, JSON, and other data files from public institutions and trusted sources
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- Always verify source reliability before downloading
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- Save downloaded files to `{report_dir}/`
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- Never download from suspicious domains or download executable files
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Report structure (per subtask):
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- Results and details per research item
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- Summary of key findings
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- Caveats and risks
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- Items unable to research and reasons
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- Recommendations/conclusions
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