- report/SKILL.md: add AskUserQuestion to allowed-tools - validate_json.py: remove unused imports (List, Any) - validate_json.py: sort output lists for deterministic order - validate_json.py: limit recursion to category level only - validate_json.py: add list-of-dict handling - EN validate_json.py: remove Chinese from CATEGORY_MAPPING - EN deep/SKILL.md: change output language from Chinese to English - deep/SKILL.md: add slug handling for filenames - report/SKILL.md: fix uncertain_fields -> uncertain naming - README: add PyYAML dependency note 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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description, allowed-tools
| description | allowed-tools |
|---|---|
| Read research outline, launch independent agent for each item for deep research. Disable task output. | Bash, Read, Write, Glob, WebSearch, Task |
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 ~/.claude/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 ~/.claude/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