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:

prompt = f"""## Task Research {item_related_info}, output structured JSON to {output_path}

Field Definitions

Read {fields_path} to get all field definitions

Output Requirements

1. Output JSON according to fields defined in fields.yaml
2. Mark uncertain field values with [uncertain]
3. Add uncertain array at the end of JSON, listing all uncertain field names
4. All field values must be in English

Output Path

{output_path}

Validation

After completing JSON output, run validation script to ensure complete field coverage: python /home/weizhena/.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):

Task

Research name: GitHub Copilot category: International Product description: Developed by Microsoft/GitHub, first mainstream AI coding assistant, ~40% market share, output structured JSON to /home/weizhena/AIcoding/aicoding-history/results/GitHub_Copilot.json

Field Definitions

Read /home/weizhena/AIcoding/aicoding-history/fields.yaml to get all field definitions

Output Requirements

1. Output JSON according to fields defined in fields.yaml
2. Mark uncertain field values with [uncertain]
3. Add uncertain array at the end of JSON, listing all uncertain field names
4. All field values must be in English

Output Path

/home/weizhena/AIcoding/aicoding-history/results/GitHub_Copilot.json

Validation

After completing JSON output, run validation script to ensure complete field coverage: python /home/weizhena/.codex/skills/research/validate_json.py -f /home/weizhena/AIcoding/aicoding-history/fields.yaml -j /home/weizhena/AIcoding/aicoding-history/results/GitHub_Copilot.json Task 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