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 CAMARA-CHILENA-INTELIGENCIA-ARTIFICIAL/cchia-skills --skill benchmark-artificialanalysisgit clone --depth 1 https://github.com/CAMARA-CHILENA-INTELIGENCIA-ARTIFICIAL/cchia-skillsWrote 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/camara-chilena-inteligencia-artificial/cchia-skills/benchmark-artificialanalysis)<a href="https://agentmods.dev/skills/camara-chilena-inteligencia-artificial/cchia-skills/benchmark-artificialanalysis"><img src="https://agentmods.dev/badge/skills/camara-chilena-inteligencia-artificial/cchia-skills/benchmark-artificialanalysis/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/camara-chilena-inteligencia-artificial/cchia-skills/benchmark-artificialanalysis"><img src="https://agentmods.dev/badge/skills/camara-chilena-inteligencia-artificial/cchia-skills/benchmark-artificialanalysis.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 | $0.00117 | $0.01524 |
| Opus 5 | $0.00059 | $0.00762 |
| Sonnet 5 | $0.00023 | $0.00305 |
| Haiku 4.5 | $0.00012 | $0.00152 |
Grade A, and why
benchmark-artificialanalysis 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 12d 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 — 145 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Artificial Analysis Benchmark Skill
This skill provides tools to query, search, and compare LLM models using independent benchmark data from Artificial Analysis. It covers 500+ models across 15 benchmarks, with pricing and speed metrics.
Data attribution: Artificial Analysis.
When to use this skill
- User asks "which model should I use for X?"
- User wants to compare models by price, speed, or quality
- User asks about specific benchmarks (IFBench, MMLU-Pro, GPQA, etc.)
- User needs a model for a specific use case (entity extraction, coding, reasoning, etc.)
- User asks about model pricing or throughput
- User mentions Artificial Analysis or their benchmarks
Setup
The skill needs an API key from Artificial Analysis stored as ARTIFICIAL_ANALYSIS_API_KEY
environment variable. The API is free with a 1,000 requests/day limit.
Step 1: Fetch fresh model data
Always start by fetching current data. The API returns all models in one call.
python <skill-path>/scripts/fetch_models.py --output /tmp/aa_models.json
If the file already exists and was created recently (same session), skip this step.
Step 2: Understand what the user needs
Read references/benchmark_catalog.json to understand which benchmarks and metrics
are relevant to the user's question. The catalog contains:
- 15 benchmarks organized into 5 categories (composite indexes, knowledge & reasoning, coding, math, instruction following & tool use)
- Plain-language descriptions of each benchmark so you can explain them to the user
- Tags for matching natural-language queries to the right benchmarks
- Performance metrics (pricing, speed, latency) with descriptions
Use the tags and descriptions to map the user's request to concrete benchmark keys.
Benchmark quick reference
| Category | Benchmarks | When to use |
|---|---|---|
| Composite Indexes | Intelligence Index, Coding Index, Math Index | Overall rankings, "best model" questions |
| Knowledge & Reasoning | MMLU-Pro, GPQA, HLE, LCR | Factual accuracy, scientific reasoning, document analysis |
| Coding | LiveCodeBench, SciCode, TerminalBench | Programming tasks, software engineering |
| Math | MATH-500, AIME 2024, AIME 2025 | Mathematical reasoning, calculations |
| Instruction Following & Tool Use | IFBench, tau2-Bench | Structured output, entity extraction, function calling, agent workflows |
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago First seen · 145 lines · 117 tokens per session scan A 612f3cf0d15e
benchmark-artificialanalysis is a skill published in the GitHub repository CAMARA-CHILENA-INTELIGENCIA-ARTIFICIAL/cchia-skills (5 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 117 tokens to every session and 1,524 once invoked, about $0.0006 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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