Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add rtorcato/repo-tooling/plugin install repo-toolingWrote 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/rtorcato/repo-tooling/ai-issue-loop)<a href="https://agentmods.dev/skills/rtorcato/repo-tooling/ai-issue-loop"><img src="https://agentmods.dev/badge/skills/rtorcato/repo-tooling/ai-issue-loop.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00173 | $0.25173 |
| Opus 5 | $0.00086 | $0.12587 |
| Sonnet 5 | $0.00035 | $0.05035 |
| Haiku 4.5 | $0.00017 | $0.02517 |
Grade B, and why
ai-issue-loop scanned grade B with 1 finding 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 5d 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.
Reads agent configuration directoriesmediumAgent snooping
.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.
grep -qxF '.claude/ai-loop-status' .gitignore || echo '.claude/ai-loop-status' >> .gitignore How it starts
The opening of the file, as written. The whole thing — 1,751 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ai-issue-loop
One tick of an unattended pipeline: ai-ready issue → worktree → PR → two
agent reviews → assigned to you to merge → worktree removed on the next tick.
Only Dependabot PRs merge themselves, plus — on a repo whose release environment
requires reviewers — a fully-passed issue PR. See Pass 1. Whenever the loop
declines to merge, it says why in a comment on the PR.
All state lives in GitHub labels. A tick is a stateless, idempotent pass over that state, so a missed tick, a crash, or a restart costs nothing. Never keep pipeline state in the conversation.
The one constraint that shapes everything
Every agent here authenticates as the user's own gh — no PATs, no bot accounts.
GitHub refuses gh pr review --approve on your own PR, so a real GitHub
approval is impossible. Approval is therefore a label, and the repo's required
status checks stay the real merge gate.
Never run gh pr review --approve. Never set required_pull_request_reviews on
the protected branch — it would deadlock every PR.
If you ever switch to real approvals — a second GitHub account reviewing as
someone else, so --approve works and the ai-ok-* labels become unnecessary —
first check what else writes your branch protection. Any repo-settings tool that
treats required_pull_request_reviews as drift will PUT it back to null on its
next run, because required review deadlocks solo Dependabot auto-merge. Your
approval rule vanishes, merges hand themselves back to the labels, and nothing in
that tool's output ties the change to this pipeline. @rtorcato/repo-tooling,
which ships this skill, is one such tool — its repo-settings standard asserts
required_pull_request_reviews: null, so change that standard before you rely on
real approvals.
The same constraint makes everything an agent posts look hand-written by the
owner. So every comment any agent leaves — review, blocked, gave-up, declined —
opens with a 🤖 *Automated …* italic header line naming which agent wrote it,
then a blank line. Name the agent and stop there: a detailed security review
under a human's avatar misrepresents who reviewed the code, but why it wears
that avatar is read once and then reread on every comment forever.
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.
- 5d ago First seen · 1,751 lines · 173 tokens per session scan B 96ace1a438cd
ai-issue-loop is a skill published in the GitHub repository rtorcato/repo-tooling (2 stars, last pushed 5d ago), licensed MIT. It adds 173 tokens to every session and 25,173 once invoked, about $0.0009 per session on Opus 5. A static security scan graded it B with 1 finding (reads agent configuration directories). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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