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 agentmods add commands/ariegoldkin/claude-forge/llm-evaluationgit 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/commands/ariegoldkin/claude-forge/llm-evaluation)<a href="https://agentmods.dev/commands/ariegoldkin/claude-forge/llm-evaluation"><img src="https://agentmods.dev/badge/commands/ariegoldkin/claude-forge/llm-evaluation.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 | $0.00061 | $0.00104 |
| Opus 5 | $0.00030 | $0.00052 |
| Sonnet 5 | $0.00012 | $0.00021 |
| Haiku 4.5 | $0.00006 | $0.00010 |
Grade A, and why
llm-evaluation 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 4d 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
llm-evaluation
Invoking skill: atk:llm-patterns
Follow the instructions in the llm-patterns skill (evaluation patterns) exactly.
$ARGUMENTS
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.
- 4d ago First seen · 12 lines · 61 tokens per session scan A 53e49a383608
llm-evaluation is a command published in the GitHub repository ArieGoldkin/claude-forge (6 stars, last pushed 27d ago), licensed MIT. It adds 61 tokens to every session and 104 once invoked, about $0.0003 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.
Other commands, from other repositories
create-issue
Create a GitHub issue from the repo's templates, with the right type and labels.
release-notes
Draft curated release notes for a milestone release.
soc2-review
Assess SOC 2 readiness against the Trust Services Criteria and produce a readiness dashboard.
overview
Unified cost dashboard combining state, plan, actual costs, projected costs, drift, and recommendations.
scan
Scan AWS account for cost optimization.
oma-platform-review
기존 Agentic AI 플랫폼 배포를 리뷰하여 GPU 사이징, 관측성 커버리지, Guardrails, 비용 이상, 보안 취약점을 점검합니다. 진단 리포트와 개선 제안을 .omao/state/platform-review- .md 에 저장합니다.