Research Deep

Weizhena/Deep-Research-skills/skills/research-codex-zh/research-deep

作者 Weizhena6ce38f60e3f8b22502c29873f96503a4e0c5addb無授權條款收錄於 2026年10月9日更新於 2026年10月9日

Read research outline, launch independent agent for each item for deep research. Disable task output.

AI 產生的概覽

依 outline.yaml 項目清單分批執行深度研究代理,為每個項目產生結構化 JSON 並驗證。

功能
讀取 outline.yaml 以取得研究項目與執行設定,接著分批啟動背景網路搜尋代理逐項研究。每個代理會依照 fields.yaml 的欄位定義,將結構化 JSON 寫入指定的輸出目錄,並標記不確定的值。此技能會略過已完成的項目以支援續跑,最後輸出完成數量、失敗或不確定項目以及輸出目錄的摘要報告。
適用情境
適用於已有列出多個項目的研究大綱,並希望逐項調查後寫入結構化 JSON 的情況。適合項目較多、需要分批執行、續跑與逐項輸出檔案的研究工作。
執行需求
僅為指示,未附指令碼。需要工作目錄中有 outline.yaml、用於欄位定義的 fields.yaml、網路搜尋代理,以及位於 ~/.codex/skills/research/validate_json.py 的驗證指令碼。需要網路搜尋存取權,且每批之間需使用者核准。

Research Deep - Deep Research

Trigger

/research-deep

Workflow

Step 1: Auto-locate Outline

Find */outline.yaml file in current working directory, read items list, execution config (including items_per_agent).

Step 2: Resume Check

  • Check completed JSON files in output_dir
  • Skip completed items

Step 3: Batch Execution

  • Batch by batch_size (need user approval before next batch)
  • Each agent handles items_per_agent items
  • Launch web-search-agent (background parallel, disable task output)

Parameter Retrieval:

  • {topic}: topic field from outline.yaml
  • {item_name}: item's name field
  • {item_related_info}: item's complete yaml content (name + category + description etc.)
  • {output_dir}: execution.output_dir from outline.yaml (default: ./results)
  • {fields_path}: absolute path to {topic}/fields.yaml
  • {output_path}: absolute path to {output_dir}/{item_name_slug}.json (slugify item_name: replace spaces with _, remove special chars)

Hard Constraint: The following prompt must be strictly reproduced, only replacing variables in {xxx}, do not modify structure or wording.

Prompt Template:

python
prompt = f"""## TaskResearch {item_related_info}, output structured JSON to {output_path}
## Field DefinitionsRead {fields_path} to get all field definitions
## Output Requirements1. Output JSON according to fields defined in fields.yaml2. Mark uncertain field values with [uncertain]3. Add uncertain array at the end of JSON, listing all uncertain field names4. All field values must be in English
## Output Path{output_path}
## ValidationAfter completing JSON output, run validation script to ensure complete field coverage:python ~/.codex/skills/research/validate_json.py -f {fields_path} -j {output_path}Task is complete only after validation passes."""

One-shot Example (assuming researching GitHub Copilot):

## TaskResearch name: GitHub Copilotcategory: International Productdescription: Developed by Microsoft/GitHub, first mainstream AI coding assistant, ~40% market share, output structured JSON to {project_dir}/results/GitHub_Copilot.json
## Field DefinitionsRead {project_dir}/fields.yaml to get all field definitions
## Output Requirements1. Output JSON according to fields defined in fields.yaml2. Mark uncertain field values with [uncertain]3. Add uncertain array at the end of JSON, listing all uncertain field names4. All field values must be in English
## Output Path{project_dir}/results/GitHub_Copilot.json
## ValidationAfter completing JSON output, run validation script to ensure complete field coverage:python ~/.codex/skills/research/validate_json.py -f {project_dir}/fields.yaml -j {project_dir}/results/GitHub_Copilot.jsonTask is complete only after validation passes.

Step 4: Wait and Monitor

  • Wait for current batch to complete
  • Launch next batch
  • Display progress

Step 5: Summary Report

After all complete, output:

  • Completion count
  • Failed/uncertain marked items
  • Output directory

Agent Config

  • Background execution: Yes
  • Task Output: Disabled (agent has explicit output file when complete)
  • Resume support: Yes

來源與署名

來源:Weizhena/Deep-Research-skills位於skills/research-codex-zh/research-deep提交6ce38f6

授權條款: 無授權條款

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