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。
1.4 KiB
1.4 KiB
Decompose the research plan (or additional research instructions) into independent subtasks and execute the investigation in parallel.
What to do:
- Analyze research items from the plan and decompose them into independently executable subtasks
- Include clear research scope and expected deliverables in each subtask's instruction
- Include the following data saving rules and report structure in each subtask's instruction
Subtask decomposition guidelines:
- Prioritize topic independence (group interdependent items into the same subtask)
- Avoid spreading high-priority items (P1) across too many subtasks
- Balance workload evenly across subtasks
Rules to include in each subtask's instruction:
Data saving rules:
- Write data per research item to
{report_dir}/data-{topic-name}.md - Topic names in lowercase English with hyphens (e.g.,
data-market-size.md) - Include source URLs, retrieval dates, and raw data
External data downloads:
- Actively download and utilize CSV, Excel, JSON, and other data files from public institutions and trusted sources
- Always verify source reliability before downloading
- Save downloaded files to
{report_dir}/ - Never download from suspicious domains or download executable files
Report structure (per subtask):
- Results and details per research item
- Summary of key findings
- Caveats and risks
- Items unable to research and reasons
- Recommendations/conclusions