This page gives reviewers one public path to try Data Quality Agent, submit evidence, and help convert currently blocked outcome claims into verified resume signals.
Give external reviewers one public page for trying the project, submitting countable evidence, and seeing which outcome claims are still blocked.
Send one message from the reviewer outreach execution pack to a real non-owner reviewer, then update the status board from not_sent to sent.
Done when: One outreach slot has status sent and no resume outcome is claimed yet.
Evidence pathAsk the reviewer to submit a public issue with path tried, command or URL evidence, observed result, main feedback, no-private-data checkbox, and permission to count.
Done when: External reviewer evidence gate accepts one non-owner issue.
Evidence pathAsk an AI/ML systems reviewer to inspect the tool-calling loop, PostgreSQL adapter, guardrails, trace evidence, and AI Engineer readiness document.
Done when: One public ai-engineer-review issue passes the evidence gate.
Evidence pathAsk a data/ops reviewer for an anonymized data-quality scenario and map it to the agent findings, business impact, fields involved, and useful next action.
Done when: One business-case public issue passes the evidence gate without sensitive data.
Evidence pathShare the public demo, review page, and README with relevant student builders or data engineers; do not buy, trade, or request fake stars.
Done when: docs/adoption-metrics.json and the public GitHub stargazers page show at least 1 star.
Evidence pathCountable evidence must be public, non-owner, permissioned, and free of raw private data.
Counts only after a non-owner public issue grants permission and passes the evidence gate.
Counts only when the reviewer ran or opened a runnable path and submitted observed-result evidence.
Counts only when reproduction steps are specific enough for the maintainer to retry.
Counts only when the business case is anonymized, permissioned, and contains no raw production data.
Counts only when an external reviewer inspects implementation evidence and grants permission.
Counts only from GitHub public star data; never asks for fake engagement.