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Fetch and rank open GitHub issues by community engagement, present the top 3 candidates, and plan implementation for the selected issue.
Passed 11 of 18 test cases with the skill, and 7 of 18 without it.
and 4 more in the full report
7 fixed7 passed either way7 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 7 test cases the skill fixed. Same model, same prompt, with and without the skill.
When invoking turboplan for issue #15, what text should be passed as the task description?
| Checked | Without skill | With skill |
|---|---|---|
| Passes the complete issue body text as the task description to turboplan | Missed | Passed |
The instructions your model reads when the skill loads.
Rank open GitHub issues by engagement and plan the selected issue.
Run gh issue list to fetch open issues with engagement data:
gh issue list --state open --json number,title,url,reactionGroups,comments,labels,createdAt --limit 50
Calculate an engagement score for each issue:
reactionGroups (thumbs up, heart, hooray, etc.). Weight thumbs-up (THUMBS_UP) reactions 2x since they signal explicit demand.(weighted reactions) + commentsSort issues by engagement score descending.
Present the top 3 issues in a numbered list. For each issue, show:
If fewer than 3 open issues exist, present all of them.
If no open issues exist, inform the user and stop.
Ask the user to pick one of the presented issues (or request to see more).
If the user asks to see more, present the next 3 issues from the ranked list.
test cases passed, vs 7 / 18 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.