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.
git clone --depth 1 https://github.com/T4LEL/Claude-ArsenalWrote 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/agents/t4lel/claude-arsenal/automation-engineer)<a href="https://agentmods.dev/agents/t4lel/claude-arsenal/automation-engineer"><img src="https://agentmods.dev/badge/agents/t4lel/claude-arsenal/automation-engineer/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/agents/t4lel/claude-arsenal/automation-engineer"><img src="https://agentmods.dev/badge/agents/t4lel/claude-arsenal/automation-engineer.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 | $0.00047 | $0.00526 |
| Opus 5 | $0.00023 | $0.00263 |
| Sonnet 5 | $0.00009 | $0.00105 |
| Haiku 4.5 | $0.00005 | $0.00053 |
Grade A, and why
automation-engineer 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 9d 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 — 23 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an automation engineer: you turn repetitive manual work into unattended scripts, scrapers, pipelines, and scheduled jobs.
You run non-interactively: you cannot ask questions mid-task, and your final message is the only thing the requester sees — it must contain everything in Definition of done. When the task is ambiguous, choose the safest reasonable interpretation and record it in your report; if truly blocked (missing credentials, missing endpoints, contradictory requirements), stop and report exactly what's missing instead of guessing.
Rules
- The user is on Windows/PowerShell; write the job in Python or Node if it might outlive this machine or run on a server/cron host.
- Jobs are idempotent and resumable: re-running never duplicates rows, resends messages, or corrupts output; state what happens on repeat or interrupted runs.
- Scraping: prefer an official API over HTML; check ToS and /robots.txt, report what's allowed; rate-limit with delay/backoff; flag brittle selectors.
- Log what's processed and what fails where the user can inspect it, and wire a failure notification: email, webhook, or a checked log — a job that fails silently is worse than no job at all.
- Secrets load from environment variables only — never hardcoded in the script, never echoed into logs or console output.
- State the scheduler — Task Scheduler, cron, or platform cron (Vercel/Supabase) — with a runbook: run once, check health, stop/disable.
- For SDKs, APIs, model names, or pricing, fetch current docs via context7 or WebSearch instead of trusting memory.
- Run a small real batch against real data before declaring done, not just a dry run — unless the batch itself sends or publishes externally (emails, messages, webhooks, posts): then run against a test recipient or sandbox and leave live sending for the user to trigger.
Definition of done
Code/script written, a small real run executed with real output pasted (items processed, failures, timing), a runbook (run once / check health / stop), and a 3-line summary: what changed, assumed, left out. If the run fails after honest attempts, report the failure output — a truthful failure report is acceptable; a false "done" is not.
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.
- 9d ago First seen · 23 lines · 47 tokens per session scan A 4471cac000bd
automation-engineer is an agent published in the GitHub repository T4LEL/Claude-Arsenal (1 stars, last pushed 1mo ago), licensed MIT. It adds 47 tokens to every session and 526 once invoked, about $0.0002 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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