Claude Code Templates is a command-line tool and catalogue for configuring Anthropic’s Claude Code with agents, commands, settings, hooks, integrations, skills, and project templates. Developers use it to browse and install reusable components for their coding workflows. The catalogue includes many of these Claude Code components.
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 davila7/claude-code-templates --skill post-training-milesgit clone --depth 1 https://github.com/davila7/claude-code-templatesWrote 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/davila7/claude-code-templates/post-training-miles)<a href="https://agentmods.dev/skills/davila7/claude-code-templates/post-training-miles"><img src="https://agentmods.dev/badge/skills/davila7/claude-code-templates/post-training-miles.svg" alt="Measured on agentmods" 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 Privilege Escalation · line 50 Potential security issue detected. Manual review is recommended.Fix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
- medium MCP Rug Pull · line 49 Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.Fix: Pin the image: image:tag or image@sha256:abc123
- medium MCP Rug Pull · line 50 Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.Fix: Pin the image: image:tag or image@sha256:abc123
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 | $0.00051 | $0.02424 |
| Opus 5 | $0.00026 | $0.01212 |
| Sonnet 5 | $0.00010 | $0.00485 |
| Haiku 4.5 | $0.00005 | $0.00242 |
Grade A, and why
miles-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 4d 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.
Copies of this mod
4 near-identical copies found in the catalogue:
- miles-rl-training — 100% identical, 0 lines differ
- miles-rl-training — 100% identical, 0 lines differ
- miles-rl-training — 100% identical, 0 lines differ
- miles-rl-training — 86% identical, 14 lines differ
How it starts
The opening of the file, as written. The whole thing — 316 lines — stays where its author put it; the contents beside it link to each section on GitHub.
miles: Enterprise-Grade RL for Large-Scale Model Training
miles is a high-performance, enterprise-ready RL framework optimized for large-scale model post-training. Built as a production fork of slime, it addresses critical challenges in MoE training stability, low-precision training, and train-inference alignment.
When to Use miles
Choose miles when you need:
- Training 1TB+ MoE models (DeepSeek V3, Qwen3-MoE)
- FP8 or INT4 quantization-aware training
- Bit-wise identical train-inference alignment
- Speculative RL for maximum throughput
- Production stability with enterprise support
Consider alternatives when:
- You want the research-grade original → use slime
- You need flexible backend swapping → use verl
- You want PyTorch-native abstractions → use torchforge
Key Features
Low-Precision Training
- Unified FP8: End-to-end FP8 for both inference and training
- INT4 QAT: 1TB models on single-machine VRAM (H200)
- Rollout Routing Replay (R3): Bit-wise expert alignment for MoE
Performance Optimizations
- Speculative RL: 25%+ rollout speedup with online SFT draft models
- Zero-Copy Weight Sync: CUDA IPC zero-copy mapping
- Partial Rollout: Recycle half-finished trajectories
Train-Inference Alignment
- TIS/MIS: Truncated/Masked Importance Sampling for off-policy correction
- Kernel-level optimization: FlashAttention-3, DeepGEMM integration
Installation
# Recommended: Docker
docker pull radixark/miles:latest
docker run --rm --gpus all --ipc=host --shm-size=16g \
-it radixark/miles:latest /bin/bash
# From source
git clone https://github.com/radixark/miles.git
cd miles
pip install -r requirements.txt
pip install -e .
Quick Start
miles inherits slime's configuration system. Basic training:
python train.py \
--advantage-estimator grpo \
--model-name qwen3-30b-a3b \
--hf-checkpoint /path/to/qwen3-30b-a3b-hf \
--rollout-batch-size 512 \
--n-samples-per-prompt 8
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.
- 4d ago First seen · 316 lines · 51 tokens per session scan A cbf610faa37b
miles-rl-training is a skill published in the GitHub repository davila7/claude-code-templates (30,559 stars, last pushed today), licensed MIT. It adds 51 tokens to every session and 2,424 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 2026-09-03.
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.
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.
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.
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.
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.
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.