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 AgnesAI-Labs/skills --skill agnes-ai-modelsgit clone --depth 1 https://github.com/AgnesAI-Labs/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/agnesai-labs/skills/agnes-ai-models)<a href="https://agentmods.dev/skills/agnesai-labs/skills/agnes-ai-models"><img src="https://agentmods.dev/badge/skills/agnesai-labs/skills/agnes-ai-models/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/agnesai-labs/skills/agnes-ai-models"><img src="https://agentmods.dev/badge/skills/agnesai-labs/skills/agnes-ai-models.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 | $0.00133 | $0.01516 |
| Opus 5.5 | $0.00053 | $0.00606 |
| Sonnet 5.5 | $0.00027 | $0.00303 |
| Haiku 4.5 | $0.00013 | $0.00152 |
Grade A, and why
agnes-ai-models scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
description: Agnes AI model integration skill for OpenAI-compatible text, image, video, and agent workflows. Use when Codex needs to configure Agnes AI API access, select or document Agnes models including agnes-2.5-flas 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 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.
- 1mo ago Changed · +23 lines · +10 tokens per session d666a1631c6e
- 1mo ago First seen · 116 lines · 123 tokens per session scan A d4c69d0566cc
agnes-ai-models is a skill published in the GitHub repository AgnesAI-Labs/skills (68 stars, last pushed 1mo ago), with no licence file. It adds 133 tokens to every session and 1,516 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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data-quality-audit
Audit training data and tokenizer pipelines for quality issues that silently degrade LLM training. Use when: preparing a dataset for LLM pretraining or fine-tuning.
graphify
Converts this codebase into a queryable knowledge graph so AI sessions query graph.json (71.5x fewer tokens) instead of reading raw source files. Integrates with Claude Code and Codex via repo hooks that auto-refresh on session start and after turns.
checkpoint-strategy
Design and validate checkpointing strategies for LLM training runs. Use when: starting a new training run (set checkpointing policy upfront).