Research deep

Skill lornshrimp/Lorn.NovelWriteSkills/CommonSkills/通用-深度研究/research-en/research-deep

一个面向长篇网文 / 小说创作工作流的 AI 写作资产库。它不是“随手堆提示词”的仓库，而是一套围绕题材设计 → 大纲搭建 → 章节创作 → 审阅润色 → 多平台改写 → 质量门禁 → 分发落盘搭起来的可复用写作系统。

Install

npx -y skills add lornshrimp/Lorn.NovelWriteSkills --skill research-deep

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.

What its author says it does

Copied from the file, not written here

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

SKILL.md

3.5 KB, as published. Nobody here has run it

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