show-draft

name	show-draft
description	Show what /r2r:draft produces - a sample ADR draft that structures research before it begins.

Show Draft Example

When triggered, display this sample draft output:

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/r2r:draft Output Example

Here's what /r2r:draft "AI-powered code review for PR automation" produces:

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RFC DRAFT: AI-Powered Code Review for PR Automation

Status: WIP Author: JD Fiscus Created: 2025-01-20

Problem

Manual code reviews are a bottleneck. Senior engineers spend 4-6 hours/week reviewing PRs. We want to explore whether AI can handle first-pass reviews to catch common issues before human review.

Research Questions

• Which AI models perform best on code review tasks? (GPT-4, Claude, CodeLlama)
• What's the false positive rate for AI-flagged issues?
• How do developers perceive AI review comments?
• What's the cost per PR at scale (1000+ PRs/month)?
• Can we integrate with existing GitHub Actions workflow?

Options to Evaluate

1. OpenAI GPT-4: Highest capability, highest cost, API-based
2. Anthropic Claude: Strong reasoning, good at explanations, API-based
3. Self-hosted CodeLlama: Lower cost at scale, requires infrastructure
4. GitHub Copilot for PRs: Native integration, limited customization

Solution

[To be filled after research]

Rationale

[To be filled after research]

Scope

In Scope

• Automated first-pass review on new PRs
• Common issue detection (security, performance, style)
• Integration with existing CI/CD

Out of Scope

• Replacing human reviewers entirely
• Architectural review
• Business logic validation

Success Criteria

• Reduce senior engineer review time by 30%
• < 10% false positive rate on flagged issues
• Developer satisfaction score > 7/10
• Cost < $0.50 per PR reviewed

Stakeholders

Stakeholder	Interest	Engagement
Engineering	Time savings	Decides
Security	Vulnerability detection	Consulted
Finance	Cost implications	Informed

Risks

• Developers may ignore AI comments (alert fatigue)
• False positives erode trust
• Cost could exceed manual review at scale
• Security/privacy concerns with code leaving org

Next Steps

• Research model options (1 week)
• Build POC with top 2 models (2 weeks)
• Run pilot on internal repos (2 weeks)
• Run /r2r:assess on findings
• Make go/no-go decision

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This creates structure BEFORE research begins. After completing the research plan, run /r2r:assess to evaluate findings and continue through the pipeline.

The full /r2r:draft command:

1. Asks clarifying questions about your research topic
2. Lets you pick a template style (Google/Uber/Sourcegraph/Experiment)
3. Generates a structured draft with research plan
4. Saves to .r2r/00-draft-adr.md