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
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/davila7/claude-code-templatesnpx agentmods add skills/davila7/claude-code-templates/post-training-openrlhfWrote 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-openrlhf)<a href="https://agentmods.dev/skills/davila7/claude-code-templates/post-training-openrlhf"><img src="https://agentmods.dev/badge/skills/davila7/claude-code-templates/post-training-openrlhf/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/davila7/claude-code-templates/post-training-openrlhf"><img src="https://agentmods.dev/badge/skills/davila7/claude-code-templates/post-training-openrlhf.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 Privilege Escalation · line 20 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 20 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 Privilege Escalation · line 24 Commands invoke sudo or root privileges. Verify this elevated access is necessary and justified.Fix: Avoid sudo/root unless strictly required. Prefer least-privilege patterns. If elevation is needed, document the justification and scope.
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 | $0.00072 | $0.02537 |
| Opus 5.5 | $0.00029 | $0.01015 |
| Sonnet 5.5 | $0.00014 | $0.00507 |
| Haiku 4.5 | $0.00007 | $0.00254 |
Grade B, and why
openrlhf-training scanned grade B 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.
Asks for rootmediumPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
sudo pip uninstall xgboost transformer_engine flash_attn pynvml -y Copies of this mod
3 near-identical copies found in the catalogue:
- openrlhf-training — 100% identical, 0 lines differ
- openrlhf-training — 100% identical, 0 lines differ
- openrlhf-training — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 250 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OpenRLHF - High-Performance RLHF Training
Quick start
OpenRLHF is a Ray-based RLHF framework optimized for distributed training with vLLM inference acceleration.
Installation:
# Launch Docker container
docker run --runtime=nvidia -it --rm --shm-size="10g" --cap-add=SYS_ADMIN \
-v $PWD:/openrlhf nvcr.io/nvidia/pytorch:25.02-py3 bash
# Uninstall conflicts
sudo pip uninstall xgboost transformer_engine flash_attn pynvml -y
# Install OpenRLHF with vLLM
pip install openrlhf[vllm]
PPO Training (Hybrid Engine):
ray start --head --node-ip-address 0.0.0.0 --num-gpus 8
ray job submit --address="http://127.0.0.1:8265" \
--runtime-env-json='{"working_dir": "/openrlhf"}' \
-- python3 -m openrlhf.cli.train_ppo_ray \
--ref_num_nodes 1 --ref_num_gpus_per_node 8 \
--reward_num_nodes 1 --reward_num_gpus_per_node 8 \
--critic_num_nodes 1 --critic_num_gpus_per_node 8 \
--actor_num_nodes 1 --actor_num_gpus_per_node 8 \
--vllm_num_engines 4 --vllm_tensor_parallel_size 2 \
--colocate_all_models \
--vllm_gpu_memory_utilization 0.5 \
--pretrain OpenRLHF/Llama-3-8b-sft-mixture \
--reward_pretrain OpenRLHF/Llama-3-8b-rm-700k \
--save_path ./output/llama3-8b-rlhf \
--micro_train_batch_size 8 --train_batch_size 128 \
--micro_rollout_batch_size 16 --rollout_batch_size 1024 \
--max_epochs 1 --prompt_max_len 1024 --generate_max_len 1024 \
--zero_stage 3 --bf16 \
--actor_learning_rate 5e-7 --critic_learning_rate 9e-6 \
--init_kl_coef 0.01 --normalize_reward \
--gradient_checkpointing --packing_samples \
--vllm_enable_sleep --deepspeed_enable_sleep
GRPO Training (Group Normalized Policy Optimization):
# Same command as PPO, but add:
--advantage_estimator group_norm
Common workflows
Workflow 1: Full RLHF pipeline (SFT → Reward Model → PPO)
Step 1: Train reward model (DPO):
deepspeed --module openrlhf.cli.train_rm \
--save_path ./output/llama3-8b-rm \
--save_steps -1 --logging_steps 1 \
--eval_steps -1 --train_batch_size 256 \
--micro_train_batch_size 1 --pretrain meta-llama/Meta-Llama-3-8B \
--bf16 --max_epochs 1 --max_len 8192 \
--zero_stage 3 --learning_rate 9e-6 \
--dataset OpenRLHF/preference_dataset_mixture2_and_safe_pku \
--apply_chat_template --chosen_key chosen \
--rejected_key rejected --flash_attn --gradient_checkpointing
What ships with it
4 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 First seen · 250 lines · 72 tokens per session scan B eb9da6a1a81c
openrlhf-training is a skill published in the GitHub repository davila7/claude-code-templates (32,856 stars, last pushed today), licensed MIT. It adds 72 tokens to every session and 2,537 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it B with 1 finding (asks for root). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
openrlhf-training
High-performance RLHF framework with Ray+vLLM acceleration. Use for PPO, GRPO, RLOO, DPO training of large models (7B-70B+). Built on Ray, vLLM, ZeRO-3. 2× faster than DeepSpeedChat with distributed architecture and GPU resource sharing.
openrlhf-training
High-performance RLHF framework with Ray+vLLM acceleration. Use for PPO, GRPO, RLOO, DPO training of large models (7B-70B+). Built on Ray, vLLM, ZeRO-3. 2× faster than DeepSpeedChat with distributed architecture and GPU resource sharing.
openrlhf-training
High-performance RLHF framework with Ray+vLLM acceleration. Use for PPO, GRPO, RLOO, DPO training of large models (7B-70B+). Built on Ray, vLLM, ZeRO-3. 2× faster than DeepSpeedChat with distributed architecture and GPU resource sharing.
openrlhf-training
High-performance RLHF framework with Ray+vLLM acceleration. Use for PPO, GRPO, RLOO, DPO training of large models (7B-70B+). Built on Ray, vLLM, ZeRO-3. 2× faster than DeepSpeedChat with distributed architecture and GPU resource sharing.
fine-tuning-with-trl
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.
grpo-rl-training
Expert guidance for GRPO/RL fine-tuning with TRL for reasoning and task-specific model training.