AReaL is an infrastructure system for training large language models with reinforcement learning, connecting model training to applications built around AI agents. Researchers and developers use it to train reasoning and agentic models through asynchronous workflows, and the catalogue add-ons support working with AReaL.
Borrowing it
Nothing to install: this file belongs to areal-project/AReaL. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/areal-project/AReaL/main/.agents/skills/add-archon-model/SKILL.mdgit clone --depth 1 https://github.com/areal-project/AReaLWrote 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/areal-project/areal/add-archon-model)<a href="https://agentmods.dev/skills/areal-project/areal/add-archon-model"><img src="https://agentmods.dev/badge/skills/areal-project/areal/add-archon-model/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/areal-project/areal/add-archon-model"><img src="https://agentmods.dev/badge/skills/areal-project/areal/add-archon-model.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 3 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Prompt Injection · line 464 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
- medium Agent Snooping · line 469 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Agent Snooping · line 470 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00038 | $0.04859 |
| Opus 5 | $0.00019 | $0.02429 |
| Sonnet 5 | $0.00008 | $0.00972 |
| Haiku 4.5 | $0.00004 | $0.00486 |
Grade A, and why
add-archon-model 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 10d 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 — 510 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Add Archon Model
Add support for a new HuggingFace model architecture in the Archon training engine.
When to Use
This skill is triggered when:
- User asks "how do I add a model to Archon?"
- User wants to support a new model family (e.g., Llama, Mistral, DeepSeek) in ArchonEngine
- User mentions adding a new
ModelSpecor model type for Archon
Prerequisites
Before starting, ensure:
- The target model is available on HuggingFace (has
config.jsonwithmodel_type) - You know the HuggingFace model ID (e.g.,
meta-llama/Llama-3-8B) - The model uses a standard transformer architecture (decoder-only)
Step-by-Step Guide
Step 1: Analyze the Target Model Architecture
Read the HuggingFace model's source code to extract key architecture information.
Action: Fetch and analyze the model's HuggingFace configuration and modeling files.
-
Read the model's
config.json(viaAutoConfig.from_pretrained) to identify:model_typestring (this is the key used for registry lookup)- All architecture hyperparameters (hidden_size, num_layers, etc.)
- Any model-specific fields (e.g.,
qk_norm,attention_bias, MoE fields)
-
Read the HuggingFace
modeling_*.pysource to identify:- Attention variant: Does it have Q/K norm? Attention bias? Sliding window? Multi-latent attention?
- FFN variant: SwiGLU (gate_proj + up_proj + down_proj)? GeGLU? Standard MLP?
- MoE support: Does it have MoE layers? What router type? Shared experts?
- RoPE variant: Standard RoPE? YaRN? NTK-aware scaling? What is the inv_freq formula?
- Normalization: RMSNorm or LayerNorm? Pre-norm or post-norm? Elementwise affine?
- Weight tying: Does
tie_word_embeddingsappear in config? - State dict key names: What are the HF weight key naming conventions?
-
Summarize findings in a checklist like:
Target model: <name>
HF model_type: "<model_type>" (and variants like "<model_type>_moe" if applicable)
Attention: [standard GQA / with QK norm / with bias / sliding window / ...]
FFN: [SwiGLU / GeGLU / standard MLP / ...]
MoE: [no / yes - num_experts, top_k, shared_experts]
RoPE: [standard / YaRN / NTK-aware / ...]
Norm: [RMSNorm / LayerNorm] with [pre-norm / post-norm]
Weight tying: [yes / no]
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.
- 10d ago First seen · 510 lines · 38 tokens per session scan A 24b53b005b0a
add-archon-model is a skill published in the GitHub repository areal-project/AReaL (5,739 stars, last pushed today), licensed Apache-2.0. It adds 38 tokens to every session and 4,859 once invoked, about $0.0002 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 2026-08-30.
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