grpo-rl-training

grpo-rl-training is a skill for Claude Code, Codex from raphaelmansuy/edgecrab. It costs 26 tokens per session (4,291 once invoked), scanned A, original, no licence file.

Expert guidance for training language models with GRPO, a reinforcement-learning method that improves performance on reasoning and task-specific work.

In plain words
What is it for?
Use it to fine-tune models with Hugging Face TRL for reasoning tasks or custom model behavior.
Why use it?
It helps you choose and apply the right fine-tuning steps when ordinary training is not enough for a particular task.

Skill for Claude CodeCodex

Which agent this was written for is unclear — body not stored (licence); the path alone says nothing.

Good fit Use it to fine-tune models with Hugging Face TRL for reasoning tasks or custom model behavior.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/raphaelmansuy/edgecrab/grpo-rl-training
Install

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.

Any agent
npx skills add raphaelmansuy/edgecrab --skill grpo-rl-training
Clone the repo
git clone --depth 1 https://github.com/raphaelmansuy/edgecrab

Made for: Claude Code, Codex.

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 grpo-rl-training

README.md
[![agentmods](https://agentmods.dev/badge/skills/raphaelmansuy/edgecrab/grpo-rl-training/github.svg)](https://agentmods.dev/skills/raphaelmansuy/edgecrab/grpo-rl-training)
Your own site
<a href="https://agentmods.dev/skills/raphaelmansuy/edgecrab/grpo-rl-training"><img src="https://agentmods.dev/badge/skills/raphaelmansuy/edgecrab/grpo-rl-training/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 grpo-rl-training

Your own site · 80×15
<a href="https://agentmods.dev/skills/raphaelmansuy/edgecrab/grpo-rl-training"><img src="https://agentmods.dev/badge/skills/raphaelmansuy/edgecrab/grpo-rl-training.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 26 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,291 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.
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 $0.00026 $0.04291
Opus 5 $0.00013 $0.02145
Sonnet 5 $0.00005 $0.00858
Haiku 4.5 $0.00003 $0.00429

Measured 6d ago against content hash e819621bfda3, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

grpo-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 6d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (templates/basic_grpo_training.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/mlops/training/grpo-rl-training/SKILL.md · 576 lines

The source is not reproduced here

No licence file

A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.

Read it on GitHub

Files

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.

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. 6d ago First seen · 576 lines · 26 tokens per session scan A e819621bfda3

Subscribe to this mod's changes

grpo-rl-training is a skill published in the GitHub repository raphaelmansuy/edgecrab (85 stars, last pushed 1mo ago), with no licence file. It adds 26 tokens to every session and 4,291 once invoked, about $0.0001 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.

Related

Other skills, from other repositories

building-agents

Use when building or restructuring an LLM agent — provider adapter, tool calling, structured output, RAG, agent loop, eval gate, cost routing, tracing, MCP server — model-agnostic across OpenAI/Anthropic/Gemini/OSS so a model swap is a config change. NOT vector-store SQL alone (that is postgresdb) or service…

ericrisco/rsc-harness · 85 tokens

agent-eval

Use when measuring whether an LLM or agent system actually got better and gating merges on it: golden sets, fixing an inflated LLM-as-judge, scoring RAG (faithfulness, contextual recall) or agent trajectories (tool correctness, completion), or picking an eval framework. NOT building the agent loop, tools or RAG…

ericrisco/rsc-harness · 79 tokens

prompt-engineering

Use when one prompt must give the same right answer across reruns, models, and pasted-in hostile input: forcing a fixed schema, picking the few-shot set, ordering the prompt blocks, or the inline cases you run while tuning. NOT the agent loop, tools, or retrieval (that is building-agents), NOT a standing CI eval…

ericrisco/rsc-harness · 83 tokens

mcp-tool-developer

Build Model Context Protocol (MCP) servers and tools from scratch. Full-stack MCP development with TypeScript/Python, testing, deployment, and registry publishing.

LiHongwei-cn/lihongwei-cn · 38 tokens

ai-engineering-toolkit

6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.

LiHongwei-cn/lihongwei-cn · 47 tokens

tool-prompt-optimization

Optimize the description prompts an AI agent reads to learn its built-in tools (the .md files under prompts/tools/). Two halves: (1) measure how much of a prompt is already inferable from the tool's JSON parameter schema + name, to prune redundancy with evidence; (2) house authoring rules for what belongs in a tool…

can1357/oh-my-pi · 0 tokens