Research Deep - Deep Research

SkillProductivity

Lets your agent run deep research by spawning a separate agent for each item in a research outline.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Research Deep - Deep Research skill

About this capability

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

What this skill tells your AI

The instructions your AI receives, as published by weizhena/deep-research-skills in skills/research-codex-en/research-deep/SKILL.md and read by ahel’s review.

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

Signals

GitHub stars
2k
Forks
171
Last commit
Aug 2026
Advanced
Catalog kind
skill
Gateway key
research-deep-weizhena
Source
github.com/weizhena/deep-research-skills