research-deep

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

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 ~/.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 {project_dir}/results/GitHub_Copilot.json

## Field Definitions
Read {project_dir}/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
{project_dir}/results/GitHub_Copilot.json

## Validation
After 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.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