Szpitale-graph/skills/research/SKILL.md

allowed-tools, description

allowed-tools	description
Read, Write, Glob, WebSearch, Task, AskUserQuestion	Conduct preliminary research on a topic and generate a research outline. Use for academic research, benchmark research, technology selection, etc.

Research Skill - Preliminary Research

Trigger

/research <topic>

Workflow

Step 1: Generate Initial Framework from Model Knowledge

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

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

Output {step1_output}.

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
• 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