Deep Research Preliminary Skill for Cursor

Conduct preliminary research on a topic and generate research outline. For academic research, benchmark research, technology selection, etc.

3k tokens

context cost

the whole folder, loaded on every use

files

ships runnable scripts

copies elsewhere

how many repositories repackaged it

148

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/lornshrimp/Lorn.NovelWriteSkills --skill deep-research-preliminary

The instruction itself

10 sections, as written by the author

Research Skill - Preliminary Research

Trigger

/research <topic>

Workflow

Step 1: Generate Initial Framework from Model Knowledge

Based on topic, use model's existing knowledge to generate:

Main research objects/items list in this domain
Suggested research field framework

Output {step1_output}, use AskUserQuestion to confirm:

Need to add/remove items?
Does field framework meet requirements?

Step 2: Web Search Supplement

Use AskUserQuestion to ask for time range (e.g., last 6 months, since 2024, unlimited).

Parameter Retrieval:

{topic}: User input research topic
{YYYY-MM-DD}: Current date
{step1_output}: Complete output from Step 1
{time_range}: User specified time range

Hard Constraint: The following prompt must be strictly reproduced, only replacing variables in {xxx}, do not modify structure or wording.

Launch 1 web-search-agent (background), Prompt Template:

prompt = f"""## Task
Research topic: {topic}
Current date: {YYYY-MM-DD}

Based on the following initial framework, supplement latest items and recommended research fields.

## Existing Framework
{step1_output}

## Goals
1. Verify if existing items are missing important objects
2. Supplement items based on missing objects
3. Continue searching for {topic} related items within {time_range} and supplement
4. Supplement new fields

## Output Requirements
Return structured results directly (do not write files):

### Supplementary Items
- item_name: Brief explanation (why it should be added)
...

### Recommended Supplementary Fields
- field_name: Field description (why this dimension is needed)
...

### Sources
- [Source1](url1)
- [Source2](url2)
"""

One-shot Example (assuming researching AI Coding History):

## Task
Research topic: AI Coding History
Current date: 2025-12-30

Based on the following initial framework, supplement latest items and recommended research fields.

## Existing Framework
### Items List
1. GitHub Copilot: Developed by Microsoft/GitHub, first mainstream AI coding assistant
2. Cursor: AI-first IDE, based on VSCode
...

### Field Framework
- Basic Info: name, release_date, company
- Technical Features: underlying_model, context_window
...

## Goals
1. Verify if existing items are missing important objects
2. Supplement items based on missing objects
3. Continue searching for AI Coding History related items within since 2024 and supplement
4. Supplement new fields

## Output Requirements
Return structured results directly (do not write files):

### Supplementary Items
- item_name: Brief explanation (why it should be added)
...

### Recommended Supplementary Fields
- field_name: Field description (why this dimension is needed)
...

### Sources
- [Source1](url1)
- [Source2](url2)

Step 3: Ask User for Existing Fields

Use AskUserQuestion to ask if user has existing field definition file, if so read and merge.

Step 4: Generate Outline (Separate Files)

Merge {step1_output}, {step2_output} and user's existing fields, generate two files:

outline.yaml (items + config):

topic: Research topic
items: Research objects list
execution:
batch_size: Number of parallel agents (confirm with AskUserQuestion)
items_per_agent: Items per agent (confirm with AskUserQuestion)
output_dir: Results output directory (default: ./results)

fields.yaml (field definitions):

Field categories and definitions
Each field's name, description, detail_level
detail_level hierarchy: brief -> moderate -> detailed
uncertain: Uncertain fields list (reserved field, auto-filled in deep phase)

Step 5: Output and Confirm

Create directory: ./{topic_slug}/
Save: outline.yaml and fields.yaml
Show to user for confirmation

Output Path

{current_working_directory}/{topic_slug}/
  ├── outline.yaml    # items list + execution config
  └── fields.yaml     # field definitions

Follow-up Commands

/research-add-items - Supplement items
/research-add-fields - Supplement fields
/research-deep - Start deep research

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How to use it

Copy the folder

Take lornshrimp/deep-research-preliminary from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

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.