Research Deep

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

作者 Weizhena6ce38f60e3f8b22502c29873f96503a4e0c5addb无许可证收录于 2026年10月9日更新于 2026年10月9日

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

AI 生成的概览

根据 outline.yaml 条目列表分批运行深度研究代理,为每个条目生成结构化 JSON 并校验。

功能
读取 outline.yaml 获取研究条目与执行配置,然后分批启动后台网络搜索代理逐项研究。每个代理按照 fields.yaml 中的字段定义,将结构化 JSON 写入指定输出目录,并标记不确定的取值。该技能会跳过已完成条目以支持续跑,最后输出完成数量、失败或不确定条目以及输出目录的汇总报告。
适用场景
适用于已有列出多个条目的研究大纲,并希望逐项调研后写入结构化 JSON 的场景。适合条目较多、需要分批执行、续跑和逐项输出文件的研究任务。
运行要求
仅为指令,不附带脚本。需要工作目录中存在 outline.yaml、用于字段定义的 fields.yaml、网络搜索代理,以及位于 ~/.codex/skills/research/validate_json.py 的校验脚本。需要网络搜索访问权限,且每批之间需用户批准。

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

来源与署名

来源:Weizhena/Deep-Research-skills位于skills/research-codex-en/research-deep提交6ce38f6

许可证: 无许可证

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