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 big-emotion/agent-atelier --skill issue-traingit clone --depth 1 https://github.com/big-emotion/agent-atelierWrote 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/big-emotion/agent-atelier/issue-train)<a href="https://agentmods.dev/skills/big-emotion/agent-atelier/issue-train"><img src="https://agentmods.dev/badge/skills/big-emotion/agent-atelier/issue-train/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/big-emotion/agent-atelier/issue-train"><img src="https://agentmods.dev/badge/skills/big-emotion/agent-atelier/issue-train.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 | $0.00057 | $0.07070 |
| Opus 5.5 | $0.00023 | $0.02828 |
| Sonnet 5.5 | $0.00011 | $0.01414 |
| Haiku 4.5 | $0.00006 | $0.00707 |
Grade A, and why
issue-train 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 1mo 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 — 275 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Issue Train
Walk a whole list of open GitHub issues in one pass: triage every issue, detect which already have a linked PR (open or merged), detect which were silently fixed by a merged PR that never linked back, close the ones that are no longer pertinent with a stated reason, and for the rest plan + build + open one PR per issue — each PR carrying Closes #<issue-number> in its body so the GitHub UI shows the issue ↔ PR link in the Development field and merging the PR auto-closes the issue.
This is the fourth member of the train family and fills the gap upstream of the others:
issue-train— issue → PR (this skill): triages issues, closes stale ones, opens PRs for the rest. Never merges.review-train— review only: posts reviews +approved/changes-requestedlabels on PRs. Writes nothing to code.fix-train— fix only: consumes change requests, applies them, pushes. Never merges.merge-train— merge: orders, fixes CI/conflicts, merges.
The natural pipeline is issue-train → PRs opened → review-train → fix-train → merge-train → PRs merge → linked issues auto-close.
This skill performs irreversible shared-repo actions (creating branches, pushing, opening PRs, commenting on and closing issues). It follows the same grouped-confirmation model as its siblings: all enumeration, linkage detection, and triage are read-only, one complete plan is presented, and it waits for a single explicit confirmation before any branch push, PR creation, issue comment, or issue close. Until the user confirms, nothing is written anywhere.
When to Activate
- User shares a GitHub issues URL and asks to process / triage / clear / implement the open issues.
- User says: "process all the open issues", "go through every open issue and open PRs", "triage the issue backlog", "close the stale issues and implement the rest", "clear the issue queue", "open a PR for every open issue that's still pertinent".
- User invokes
/issue-train(optionally with a GitHub issues URL or--dry-run).
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.
- 1mo ago First seen · 275 lines · 57 tokens per session scan A b31e7d6e50cb
issue-train is a skill published in the GitHub repository big-emotion/agent-atelier (3 stars, last pushed 15d ago), licensed MIT. It adds 57 tokens to every session and 7,070 once invoked, about $0.0002 per session on Opus 5.5. 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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