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 request_user_input to confirm:
- Need to add/remove items?
- Does field framework meet requirements?
Step 2: Web Search Supplement
Use request_user_input 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 request_user_input 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 request_user_input)
- items_per_agent: Items per agent (confirm with request_user_input)
- 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