UniRL: Skill for Claude Code

.claude/skills/development/add-model-bundle/SKILL.md

add-model-bundle is a skill for Claude Code from Tencent-Hunyuan/UniRL. It costs 62 tokens per session (4,068 once invoked), scanned A, original, no licence file.

A set of instructions for adding or updating support for model packages in UniRL, including diffusion and autoregressive models. Diffusion models generate data by gradually removing noise, while autoregressive models generate one part at a time.

In plain words
What is it for?
Implementing model pipelines, configuration data classes, bundles, stages, conditioning for text, images, video, or audio, LoRA targets, and FSDP wrapping hints in UniRL.
Why use it?
Adding a model package requires several connected pieces, such as configuration, data conditions, training stages, and distributed-training setup.

Skill for Claude Code

Written for Claude Code: installed under .claude/.

This is Tencent-Hunyuan/UniRL's own configuration. It tells Claude Code how to work on UniRL itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything UniRL configures →

Reuse

Borrowing it

Nothing to install: this file belongs to Tencent-Hunyuan/UniRL. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/Tencent-Hunyuan/UniRL/main/.claude/skills/development/add-model-bundle/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/Tencent-Hunyuan/UniRL

Made for: Claude Code.

Wrote 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.

agentmods badge for add-model-bundle

README.md
[![agentmods](https://agentmods.dev/badge/skills/tencent-hunyuan/unirl/add-model-bundle/github.svg)](https://agentmods.dev/skills/tencent-hunyuan/unirl/add-model-bundle)
Your own site
<a href="https://agentmods.dev/skills/tencent-hunyuan/unirl/add-model-bundle"><img src="https://agentmods.dev/badge/skills/tencent-hunyuan/unirl/add-model-bundle/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.

agentmods 80×15 button for add-model-bundle

Your own site · 80×15
<a href="https://agentmods.dev/skills/tencent-hunyuan/unirl/add-model-bundle"><img src="https://agentmods.dev/badge/skills/tencent-hunyuan/unirl/add-model-bundle.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 62 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,068 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin unknown No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00062 $0.04068
Opus 5 $0.00031 $0.02034
Sonnet 5 $0.00012 $0.00814
Haiku 4.5 $0.00006 $0.00407

Measured 2d ago against content hash 15a97efdcb96, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

add-model-bundle 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 2d 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.

.claude/skills/development/add-model-bundle/SKILL.md · 174 lines

The source is not reproduced here

A licence we could not identify

The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.

Read it on GitHub

Changes

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.

  1. 2d ago Changed · +2 lines 15a97efdcb96
  2. 11d ago First seen · 172 lines · 62 tokens per session scan A 8a8d2d39299e

Subscribe to this mod's changes

add-model-bundle is a skill published in the GitHub repository Tencent-Hunyuan/UniRL (942 stars, last pushed today), with no licence file. It adds 62 tokens to every session and 4,068 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 2026-08-30.

Related

Other skills, from other repositories

miles-rl-training

Provides guidance for enterprise-grade RL training using miles, a production-ready fork of slime. Use when training large MoE models with FP8/INT4, needing train-inference alignment, or requiring speculative RL for maximum throughput.

davila7/claude-code-templates · 51 tokens

slime-rl-training

Provides guidance for LLM post-training with RL using slime, a Megatron+SGLang framework. Use when training GLM models, implementing custom data generation workflows, or needing tight Megatron-LM integration for RL scaling.

davila7/claude-code-templates · 52 tokens

slime-rl-training

Provides guidance for LLM post-training with RL using slime, a Megatron+SGLang framework. Use when training GLM models, implementing custom data generation workflows, or needing tight Megatron-LM integration for RL scaling.

OpenLAIR/dr-claw · 52 tokens

miles-rl-training

Provides guidance for enterprise-grade RL training using miles, a production-ready fork of slime. Use when training large MoE models with FP8/INT4, needing train-inference alignment, or requiring speculative RL for maximum throughput.

OpenLAIR/dr-claw · 51 tokens

slime-rl-training

Provides guidance for LLM post-training with RL using slime, a Megatron+SGLang framework. Use when training GLM models, implementing custom data generation workflows, or needing tight Megatron-LM integration for RL scaling.

synthetic-sciences/openscience · 52 tokens

miles-rl-training

Provides guidance for enterprise-grade RL training using miles, a production-ready fork of slime. Use when training large MoE models with FP8/INT4, needing train-inference alignment, or requiring speculative RL for maximum throughput.

synthetic-sciences/openscience · 51 tokens