Deep Research Deep Skill for Claude
Read research outline, launch independent agent for each item for deep research. Disable task output.
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/lornshrimp/Lorn.NovelWriteSkills --skill deep-research-deepThe instruction itself
9 sections, as written by the author<!-- ===== Layer 1: 永久缓存 ===== -->
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
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How to use it
Take lornshrimp/deep-research-deep from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.