# Reviewer Feedback Packet

This generated packet gives external reviewers a short path to try the project and submit public evidence.

## Purpose

Give reviewers a short, public, metric-aware path for trying the project and submitting evidence that can later upgrade resume outcomes without inflating the current baseline.

## Reviewer Tasks

| Task | Audience | Minutes | Path | Counts Toward | Submission |
| --- | --- | ---: | --- | --- | --- |
| quick_demo_review | classmate_or_student_developer | 8 | [https://sunnnn2005.github.io/data-quality-agent/](https://sunnnn2005.github.io/data-quality-agent/) | `external_feedback_items` | [Submit](https://github.com/sunnnn2005/data-quality-agent/issues/new?template=demo_feedback.md) |
| local_repo_review | student_developer_or_open_source_reviewer | 15 | [https://github.com/sunnnn2005/data-quality-agent](https://github.com/sunnnn2005/data-quality-agent) | `confirmed_external_users` | [Submit](https://github.com/sunnnn2005/data-quality-agent/issues/new?template=demo_feedback.md) |
| business_case_review | mentor_recruiter_or_data_practitioner | 12 | [https://github.com/sunnnn2005/data-quality-agent/blob/main/docs/business-case-intake.md](https://github.com/sunnnn2005/data-quality-agent/blob/main/docs/business-case-intake.md) | `business_case_feedback_items` | [Submit](https://github.com/sunnnn2005/data-quality-agent/issues/new?template=business_case_review.md) |
| ai_engineer_review | ai_engineer_or_ml_platform_reviewer | 12 | [https://github.com/sunnnn2005/data-quality-agent/blob/main/docs/ai-engineer-review-intake.md](https://github.com/sunnnn2005/data-quality-agent/blob/main/docs/ai-engineer-review-intake.md) | `ai_engineer_review_items` | [Submit](https://github.com/sunnnn2005/data-quality-agent/issues/new?template=ai_engineer_review.md) |

## Evidence Questions

1. Which path did you try: public demo, local repo, API docs, or business-case review?
2. Could you reproduce or understand the support-ticket data-quality failure?
3. What is the strongest AI-agent signal in the project?
4. What was confusing, missing, or not credible enough for an internship reviewer?
5. Would you classify your note as feedback, confirmed run, reproducible issue, feature request, or business-case review?
6. Would you count this as AI Engineer project feedback after inspecting implementation paths?

## Metric Conversion Paths

| Metric | Required Label | Upgrade Threshold |
| --- | --- | ---: |
| external_feedback_items | `feedback` | 3 |
| confirmed_external_users | `confirmed-user` | 1 |
| reproducible_feedback_items | `reproducible` | 1 |
| business_case_feedback_items | `business-case` | 1 |
| ai_engineer_review_items | `ai-engineer-review` | 1 |

## Current Public Counts

| Metric | Current value |
| --- | ---: |
| External Feedback Items | 0 |
| Confirmed External Users | 0 |
| Reproducible Feedback Items | 0 |
| Business Case Feedback Items | 0 |
| Ai Engineer Review Items | 0 |

## Linked Upgrade Rules

- `first_confirmed_external_run`
- `pilot_feedback_signal`
- `reproducible_bug_signal`
- `business_case_signal`
- `github_interest_signal`

## Resume-Safe Summary

Published a CI-verified reviewer feedback packet with 4 task paths, 6 evidence questions, 5 metric conversion paths, and zero current feedback/adoption counts.

## Not Claimed

- external users
- customer feedback
- enterprise production usage
- business impact avoided
- revenue saved
- GitHub stars beyond the current public count