Skill v1.0.0
LLM-judged scan90/100user-invocable: true allowed-tools: Read, Write, Glob, WebSearch, Task, AskUserQuestion description: Conduct preliminary research on a topic and generate research outline. 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 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"""## TaskResearch topic: {topic}Current date: {YYYY-MM-DD}Based on the following initial framework, supplement latest items and recommended research fields.## Existing Framework{step1_output}## Goals1. Verify if existing items are missing important objects2. Supplement items based on missing objects3. Continue searching for {topic} related items within {time_range} and supplement4. Supplement new fields## Output RequirementsReturn 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):
## TaskResearch topic: AI Coding HistoryCurrent date: 2025-12-30Based on the following initial framework, supplement latest items and recommended research fields.## Existing Framework### Items List1. GitHub Copilot: Developed by Microsoft/GitHub, first mainstream AI coding assistant2. Cursor: AI-first IDE, based on VSCode...### Field Framework- Basic Info: name, release_date, company- Technical Features: underlying_model, context_window...## Goals1. Verify if existing items are missing important objects2. Supplement items based on missing objects3. Continue searching for AI Coding History related items within since 2024 and supplement4. Supplement new fields## Output RequirementsReturn 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.yamlandfields.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