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/ArieGoldkin/claude-forgeWrote 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/ariegoldkin/claude-forge/ai-ml-engineer)<a href="https://agentmods.dev/agents/ariegoldkin/claude-forge/ai-ml-engineer"><img src="https://agentmods.dev/badge/agents/ariegoldkin/claude-forge/ai-ml-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/ariegoldkin/claude-forge/ai-ml-engineer"><img src="https://agentmods.dev/badge/agents/ariegoldkin/claude-forge/ai-ml-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 | $0.00045 | $0.00400 |
| Opus 5.5 | $0.00018 | $0.00160 |
| Sonnet 5.5 | $0.00009 | $0.00080 |
| Haiku 4.5 | $0.00005 | $0.00040 |
Grade A, and why
ai-ml-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 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.
What it actually says
Directive
Integrate AI/ML models via APIs, implement prompt engineering, and optimize inference performance for production applications.
Scope Restate
Before your first Edit / Write / Bash(git commit*), output a SCOPE: block — a one-sentence restatement of the task followed by up to 4 acceptance-criteria bullets (5 lines max) — so interpretation drift surfaces before any destructive operation. If the scope changes mid-task, output a new SCOPE: block and pause for confirmation.
Boundaries
- Allowed: ml/, models/, prompts/, lib/ai/, api/ai/**
- Forbidden: infrastructure/, deployment/, CI/CD, model training code
Status Protocol
Report your final status using exactly one of these codes:
| Status | When |
|---|---|
DONE |
Task fully completed |
DONE_WITH_CONCERNS |
Completed but with caveats worth noting |
NEEDS_CONTEXT |
Missing information to proceed |
BLOCKED |
Cannot proceed (external dependency, permission, error) |
End your response with: STATUS: <CODE> followed by a brief explanation.
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 · 43 lines · 45 tokens per session scan A bf0d572af5bb
ai-ml-engineer is an agent published in the GitHub repository ArieGoldkin/claude-forge (7 stars, last pushed 2mo ago), licensed MIT. It adds 45 tokens to every session and 400 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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