Developer Implements AI-Ready Feedback Loop for Feature Shipping

A developer on r/ClaudeAI shared a workflow that integrates user feedback directly into an AI-assisted development pipeline. The system captures frustration moments and converts them into production-ready features through structured automation.

How the Feedback System Works

The developer built a feedback button into their family assistant app that, when tapped, files a GitHub issue containing:

• The user's screen at the time of feedback
• Last 30 interactions
• Device information
• Performance data
• A full snapshot of what the user was doing when frustrated

The Triage Skill

Built as a Claude Code slash command, this skill triggers when issues are tagged as "triage-ready." It performs several automated steps:

• Fetches the issue from GitHub
• Parses investigation diagnosis (which can use API credits for automatic issue investigation)
• Extracts device context from the issue body
• Estimates complexity
• Identifies which files are probably involved
• Writes acceptance criteria with specific verification steps
• Generates a structured roadmap entry

The roadmap is a markdown file that Claude reads at the start of every session to determine available work. Tasks have statuses, dependencies, and complexity scores. Nothing moves to "ready" unless explicitly tagged and triaged first.

Real-World Implementation Examples

The developer shared two specific shipping examples:

Example 1: Constraint System Fix

While at the mall, the developer encountered a bug in the meal planning constraint system where "no chili this week" wasn't properly filtering. After filing feedback and tagging it triage-ready, they opened Claude Code on their phone and pointed it at the triaged task. Claude had all necessary context:

• What screen the developer was on
• What was tapped
• What went wrong
• A structured task specifying exactly what to build and how to verify completion

The AI built a three-tier constraint model with inline feedback, constraint chips, and telemetry. The developer only needed to review diffs and approve tool calls.

Example 2: Cookbook Scanner Feature

At home, the developer wanted to upload cookbook page photos directly into their meal library for grocery list generation. After filing and tagging the feedback, they instructed Claude to plan the architecture and review its own plan for gaps. The AI identified ten gaps including:

• No duplicate detection
• No allergen checks (critical due to child's serious allergies)
• No way to edit AI-extracted content before saving

The AI initially wrote "ingredient editing is a v2 feature" in its plan, which the developer immediately corrected. The final implementation included:

• Vision AI cookbook scanner
• Editable preview
• Allergen checks
• Correction telemetry
• 1500 lines of code across 11 files

Key Workflow Insight

The developer emphasizes that the value isn't just that Claude wrote code, but that they never had to sit at a computer to explain what was wrong or dig through files to provide context. The feedback system captures everything at the moment of frustration, the triage skill converts it into structured tasks, and Claude has everything needed to proceed immediately.

The developer recommends that if you're building with Claude and maintaining a Trello board of fixes, you should build the capture directly into your product and structure it so the AI can consume it directly.