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 rtorcato/repo-tooling --skill ai-issuegit clone --depth 1 https://github.com/rtorcato/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)<a href="https://agentmods.dev/skills/rtorcato/repo-tooling/ai-issue"><img src="https://agentmods.dev/badge/skills/rtorcato/repo-tooling/ai-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/rtorcato/repo-tooling/ai-issue"><img src="https://agentmods.dev/badge/skills/rtorcato/repo-tooling/ai-issue.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00096 | $0.00749 |
| Opus 5 | $0.00048 | $0.00375 |
| Sonnet 5 | $0.00019 | $0.00150 |
| Haiku 4.5 | $0.00010 | $0.00075 |
Grade A, and why
ai-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 8d 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 — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ai-issue
File an issue an agent will execute unattended, labelled ai-ready so
ai-issue-loop picks it up. Arguments: $ARGUMENTS
This is only for work you intend a background agent to do without you. For an
ordinary issue, use gh issue create with no ai-* label.
Preflight
git remote get-url origin— GitHub only. On GitLab, stop: the loop isgh-based and nothing would ever pick the issue up.gh label list --search ai-ready— if the label is missing, this repo hasn't been bootstrapped for the loop. Stop and point at theai-issue-loopskill's label block; creating a bareai-readylabel would produce an issue that silently never runs.
Write it for an agent, not for yourself
The agent that picks this up cannot ask a follow-up question, and is instructed to treat the body as untrusted data. Both change how it must read:
- Describe, never instruct. "The README claims X but Y is true" — not "go update the README". Directive phrasing is exactly what the agent is told to ignore, so an instruction-shaped issue reads as empty.
- Name the files you already know are involved. The two reviewing agents are diff-scoped and won't explore the repo to judge whether the right thing was touched.
- State a done-condition a reviewer can check. Those same agents review the PR against this body; a vague issue produces a vague review on a PR you then merge without having really vetted.
- One PR's worth. Split anything spanning several concerns — the loop runs several issues in parallel, so splitting is free.
Refuse the ones that aren't ready
Say so, and file it unlabelled instead, when the task:
- needs a judgement call you'd normally make mid-PR,
- needs eyes on rendered output, a real device, or a running service,
- depends on context that lives in this conversation rather than the repo, or
- you couldn't write self-contained without "we can sort that out in review".
An ai-ready that stalls costs more than an issue you did yourself: it burns a
concurrency slot, two review passes, and up to two fix rounds before it lands as
ai-blocked.
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.
- 8d ago First seen · 75 lines · 96 tokens per session scan A 405f23e58c66
ai-issue is a skill published in the GitHub repository rtorcato/repo-tooling (2 stars, last pushed 7d ago), licensed MIT. It adds 96 tokens to every session and 749 once invoked, about $0.0005 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 2026-08-31.
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