research

Files (5)

• LAYOUT.md
• SKILL.md
• harvest_sources.py
• reddit_feed.py
• validate_json.py

SKILL.md (first lines)

# 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.

**Host selection rule:** In GitHub Copilot, launch `Web Research Writer`; in Claude Code, launch `web-search-agent`.

Launch 1 host-selected agent (background), **Prompt Template**:
```python
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

## Modules
Modules: auto
Read `web-search-modules/ROUTING.md` before any search and route per that file. State the modules you loaded in your output.

## 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

## Modules
Modules: auto
Read `web-search-modules/ROUTING.md` before any search and route per that file. State the modules you loaded in your output.

## 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 2b: Pin Search Modules (optional)
Read `web-search-modules/ROUTING.md` and propose the module routing for this whole project — the same topic is about to be researched for every item, so routing once here beats every item agent re-deciding it.

Use AskUserQuestion, offering:
- **Auto** *(default)* — each item agent routes itself per ROUTING.md. Correct when items are heterogeneous.
- **Your proposed routing** — name the modules and say in one line why, e.g. `benchmarks, general-web — every item is a model with published eval results`.
- **General web only** — the user just wants a broad sweep.

From eristoddle/deep-research-agent, MIT.

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