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 MarieLynneBlock/arcanum-artifex --skill imbalanced-classificationgit clone --depth 1 https://github.com/MarieLynneBlock/arcanum-artifexWrote 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/marielynneblock/arcanum-artifex/imbalanced-classification)<a href="https://agentmods.dev/skills/marielynneblock/arcanum-artifex/imbalanced-classification"><img src="https://agentmods.dev/badge/skills/marielynneblock/arcanum-artifex/imbalanced-classification/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/marielynneblock/arcanum-artifex/imbalanced-classification"><img src="https://agentmods.dev/badge/skills/marielynneblock/arcanum-artifex/imbalanced-classification.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 | $0.00029 | $0.00266 |
| Opus 5 | $0.00015 | $0.00133 |
| Sonnet 5 | $0.00006 | $0.00053 |
| Haiku 4.5 | $0.00003 | $0.00027 |
Grade A, and why
imbalanced-classification 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 11d 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.
The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
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.
- 11d ago First seen · 46 lines · 29 tokens per session scan A 886bdcca68f6
imbalanced-classification is a skill published in the GitHub repository MarieLynneBlock/arcanum-artifex (4 stars, last pushed 5d ago), with no licence file. It adds 29 tokens to every session and 266 once invoked, about $0.0001 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 skills, from other repositories
chain-llm-pattern
Build multi-step LLM reasoning chains in n8n using Groq, OpenAI, or Claude for structured data extraction, categorization, scoring, and analysis. Use this skill whenever the user wants to chain multiple LLM calls together in an n8n workflow — phrases like "extract entities then categorize", "multi-step LLM prompt"…
agent-neural-network
Agent skill for neural-network - invoke with $agent-neural-network.
agent-safla-neural
Agent skill for safla-neural - invoke with $agent-safla-neural.
mem0-integration
Mem0 memory layer integration for AI agents. Implement persistent, semantic memory for long-term context retention and personalization.
chroma-integration
Chroma local vector database setup and operations for development and production.
few-shot-example-gen
Few-shot example generation and optimization for improved LLM performance.