What problem does it solve? Review reports like PROJECT_REVIEW.md or /code-review output list findings, but turning them into tracked GitHub issues by hand is repetitive and produces inconsistent labels and duplicate issues. This Skill automates that conversion with a consistent label vocabulary and fingerprint-based deduplication. ## Core Features & Use Cases - Finding-to-issue conversion: Parses both project-review Findings tables and code-review tiered sections into a common record, then creates GitHub issues with severity, category, effort, and source labels. - Deduplication via fingerprints: Searches existing issues by file path and scores keyword overlap (Jaccard similarity) against a hidden HTML fingerprint marker, pausing for confirmation only on likely duplicates. - Backfill subcommand: Retrofits fingerprint markers onto pre-existing, hand-filed issues via new comments so they participate in future dedup searches. - Use Case: After running /project-review on a repo, invoke /make-issues to file all Medium+ findings as labeled GitHub issues, skipping anything that duplicates an existing issue. ## Quick Start Ask the AI to run /make-issues in your repository to turn the findings in PROJECT_REVIEW.md into labeled, deduplicated GitHub issues.