inquiry-prompts

Files (3)

• README.md
• SKILL.md
• generate_prompts.py · scripts/

SKILL.md (first lines)

# Skill: Inquiry Prompts Generator

You are a specialized agent for generating research prompts from INQ (Inquiry) work items. Your goal is to parse QUESTION.md files and distribute questions across multiple research agents for Phase 1 independent exploration.

## Capabilities

1. **QUESTION.md Parsing**
   - Parse numbered lists (`1. Question here`)
   - Parse headed sections (`## Section\nQuestion text`)
   - Parse bullet points (`- Question here` or `* Question here`)
   - Handle mixed formats in a single file
   - Extract top-level question and sub-questions

2. **Question Distribution**
   - Round-robin: Distribute sequentially across agents
   - Balanced: Evenly distribute by count
   - Grouped: Keep related questions together by heading

3. **Prompt Generation**
   - Load context from `inquiry_report.json`
   - Include constraints and scope
   - Generate role-specific prompts
   - Ensure independence instructions

## Workflow

### 1. Identify the Inquiry

Determine the INQ directory to process. Options:
- User provides path: `/inquiry-prompts inquiries/INQ-001-example/`
- Current directory contains `inquiry_report.json`
- Search for active inquiries in `inquiries/`

### 2. Parse Input Files

Read and validate:
```bash
uv run python scripts/generate_prompts.py <inquiry_path>
```

The script:
1. Loads `inquiry_report.json` for context
2. Parses `QUESTION.md` to extract questions
3. Returns structured question data

### 3. Select Distribution Algorithm

Choose based on question structure:
- **round-robin** (default): Simple sequential distribution
- **balanced**: When questions have similar complexity
- **grouped**: When questions have clear topic headings

### 4. Generate Prompts

Run with options:
```bash
# JSON output (structured)
uv run python scripts/generate_prompts.py <inquiry_path> --algorithm round-robin

# File output (writes to research/)
uv run python scripts/generate_prompts.py <inquiry_path> --output files --algorithm balanced
```

### 5. Return Results

**Structured Output (JSON)**:
```json
{
  "inquiry_id": "INQ-001",
  "title": "Inquiry Title",
  "total_agents": 3,
  "algorithm": "round-robin",
  "prompts": [
    {
      "agent_number": 1,
      "output_file": "research/agent-1.md",
      "questions": ["Question 1", "Question 4"],
      "prompt": "Full prompt text..."
    }
  ]
}
```

**File Output**:
Creates `research/agent-N.md` files with full prompts.

## Prompt Template

Each generated prompt follows this structure:

```markdown
# Research Agent {N} - Independent Research Report

## Assignment

You are Research Agent {N} of {total} working on **{inquiry_title}**.

### Core Question
{main_question}

### Your Assigned Sub-Questions
{assigned_questions}

## Context

{context_from_inquiry_report}

## Constraints

The following constraints MUST be satisfied by any proposed solution:
{constraints_list}

## Scope

{scope_if_defined}

## Instructions

1. Research your assigned questions independently
2. DO NOT consult or coordinate with other research agents

From brendanbecker/featmgmt, MIT.

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