Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add tobihagemann/turbo --skill pick-next-issuegit clone --depth 1 https://github.com/tobihagemann/turboWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/tobihagemann/turbo/pick-next-issue)<a href="https://agentmods.dev/skills/tobihagemann/turbo/pick-next-issue"><img src="https://agentmods.dev/badge/skills/tobihagemann/turbo/pick-next-issue/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/tobihagemann/turbo/pick-next-issue"><img src="https://agentmods.dev/badge/skills/tobihagemann/turbo/pick-next-issue.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00077 | $0.00588 |
| Opus 5 | $0.00039 | $0.00294 |
| Sonnet 5 | $0.00015 | $0.00118 |
| Haiku 4.5 | $0.00008 | $0.00059 |
Grade A, and why
pick-next-issue scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pick Next Issue
Rank open GitHub issues by engagement and plan the selected issue.
Step 1: Fetch and Rank Issues
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:
- Reactions score: Sum all reaction counts from
reactionGroups(thumbs up, heart, hooray, etc.). Weight thumbs-up (THUMBS_UP) reactions 2x since they signal explicit demand. - Comments score: Count of comments on the issue.
- Engagement score:
(weighted reactions) + comments
Sort issues by engagement score descending.
Step 2: Present Top 3
Present the top 3 issues in a numbered list. For each issue, show:
- Title with issue number and link
- Labels (if any)
- Engagement: reaction breakdown and comment count
- Created: date
- First paragraph of the issue body (truncate if long)
If fewer than 3 open issues exist, present all of them.
If no open issues exist, inform the user and stop.
Step 3: User Picks an Issue
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.
Step 4: Read the Full Issue
Fetch the complete issue details for the selected issue:
gh issue view <number> --json number,title,body,url,labels,comments,reactionGroups,assignees,milestone
Read the full issue body and comments to understand the requirements and any discussion context.
Step 5: Run /turboplan Skill
Run the /turboplan skill with the issue body as the task description. Tell turboplan that the plan must include a final implementation step: "Close issue #N or reference it in the PR with Closes #N."
Rules
- Requires
ghCLI authenticated with access to the current repo - If
ghfails (not in a repo, not authenticated), inform the user and stop - Never modify issues. This skill is read-only until the implementation is committed.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 65 lines · 77 tokens per session scan A 070dfa09b39c
pick-next-issue is a skill published in the GitHub repository tobihagemann/turbo (402 stars, last pushed 3d ago), licensed MIT. It adds 77 tokens to every session and 588 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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