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 agentmods add skills/liortesta/clawdagent/slimenpx skills add liortesta/ClawdAgent --skill slimegit clone --depth 1 https://github.com/liortesta/ClawdAgentWrote 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/liortesta/clawdagent/slime)<a href="https://agentmods.dev/skills/liortesta/clawdagent/slime"><img src="https://agentmods.dev/badge/skills/liortesta/clawdagent/slime.svg" alt="Measured on agentmods" 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 | $0.00052 | $0.02969 |
| Opus 5 | $0.00026 | $0.01484 |
| Sonnet 5 | $0.00010 | $0.00594 |
| Haiku 4.5 | $0.00005 | $0.00297 |
Grade A, and why
slime-rl-training 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 yesterday.
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.
This is a copy
94% identical to slime — 10 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 465 lines — stays where its author put it; the contents beside it link to each section on GitHub.
slime: LLM Post-Training Framework for RL Scaling
slime is an LLM post-training framework from Tsinghua's THUDM team, powering GLM-4.5, GLM-4.6, and GLM-4.7. It connects Megatron-LM for training with SGLang for high-throughput rollout generation.
When to Use slime
Choose slime when you need:
- Megatron-LM native training with SGLang inference
- Custom data generation workflows with flexible data buffers
- Training GLM, Qwen3, DeepSeek V3, or Llama 3 models
- Research-grade framework with production backing (Z.ai)
Consider alternatives when:
- You need enterprise-grade stability features → use miles
- You want flexible backend swapping → use verl
- You need PyTorch-native abstractions → use torchforge
Key Features
- Training: Megatron-LM with full parallelism support (TP, PP, DP, SP)
- Rollout: SGLang-based high-throughput generation with router
- Data Buffer: Flexible prompt management and sample storage
- Models: GLM-4.x, Qwen3, DeepSeek V3/R1, Llama 3
Architecture Overview
┌─────────────────────────────────────────────────────────┐
│ Data Buffer │
│ - Prompt initialization and management │
│ - Custom data generation and filtering │
│ - Rollout sample storage │
└─────────────┬───────────────────────────┬───────────────┘
│ │
┌─────────────▼───────────┐ ┌─────────────▼───────────────┐
│ Training (Megatron-LM) │ │ Rollout (SGLang + Router) │
│ - Actor model training │ │ - Response generation │
│ - Critic (optional) │ │ - Reward/verifier output │
│ - Weight sync to rollout│ │ - Multi-turn support │
└─────────────────────────┘ └─────────────────────────────┘
Installation
# Recommended: Docker
docker pull slimerl/slime:latest
docker run --rm --gpus all --ipc=host --shm-size=16g \
-it slimerl/slime:latest /bin/bash
# Inside container
cd /root/slime && pip install -e . --no-deps
What ships with it
2 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.
- yesterday First seen · 465 lines · 52 tokens per session scan A 117c8fd2d57d
slime-rl-training is a skill published in the GitHub repository liortesta/ClawdAgent (11 stars, last pushed 8d ago), licensed Apache-2.0. It adds 52 tokens to every session and 2,969 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to slime, differing in 10 lines, and is treated as a copy.
Other skills, from other repositories
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.
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.
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.
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.
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.
model_finetuning
Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.