Benchmark Cli Agents On Autonomous Llm Post Training With Posttrainbench

Name: Benchmark Cli Agents On Autonomous Llm Post Training With Posttrainbench
Author: agentskillexchange

ASecurity

Run Claude Code, Codex CLI, Gemini CLI, or OpenCode through bounded H100 post-training tasks and compare how well each agent improves a base LLM.

36 stars
0 votes
0 copies
3 views
Added September 2, 2026

ai-agentspythongobashgitapisecuritydocumentation

Works with

claude code
cli
api

Security analysis

A100/100

Scanned September 2, 2026

npx -y skills add agentskillexchange/skills --skill benchmark-cli-agents-on-autonomous-llm-post-training-with-posttrainbench --agent claude-code

Installs into .claude/skills of the current project.

Are you the author of Benchmark Cli Agents On Autonomous Llm Post Training With Posttrainbench?

Add the live security badge to your README. It updates with every re-scan.

Security grade badge for Benchmark Cli Agents On Autonomous Llm Post Training With Posttrainbench

[![Security: A — Skills Directory](https://www.skillsdirectory.com/api/skills/agentskillexchange-benchmark-cli-agents-on-autonomous-llm-post-traini/badge)](https://www.skillsdirectory.com/skills/agentskillexchange-benchmark-cli-agents-on-autonomous-llm-post-traini)

More formats (shields.io, HTML) on the badges page. Keep it an A: scan every change in CI with Pro.

Download with Pro

SKILL.md

---
name: "Benchmark CLI agents on autonomous LLM post-training with PostTrainBench"
slug: "benchmark-cli-agents-on-autonomous-llm-post-training-with-posttrainbench"
description: "Run Claude Code, Codex CLI, Gemini CLI, or OpenCode through bounded H100 post-training tasks and compare how well each agent improves a base LLM."
github_stars: 543
verification: "security_reviewed"
source: "https://github.com/aisa-group/PostTrainBench"
author: "AISA Group"
publisher_type: "open_source_project"
category: "Developer Tools"
framework: "Multi-Framework"
tool_ecosystem:
  github_repo: "aisa-group/PostTrainBench"
  github_stars: 543
---

# Benchmark CLI agents on autonomous LLM post-training with PostTrainBench

Run Claude Code, Codex CLI, Gemini CLI, or OpenCode through bounded H100 post-training tasks and compare how well each agent improves a base LLM.

## Prerequisites

Python, apptainer, fuse-overlayfs, Hugging Face cache, H100 GPU access, currently HTCondor scheduler support, and credentials for the selected CLI agent scaffolds

## Installation

Install or set up from the source-backed instructions:

Clone https://github.com/aisa-group/PostTrainBench, install requirements including apptainer and fuse-overlayfs, build the standard container with bash containers/build_container.sh standard, download the Hugging Face cache with bash containers/download_hf_cache/download_hf_cache.sh, copy example.env to .env, set API keys and paths, then submit jobs with bash src/commit_utils/commit.sh.

- Source: https://github.com/aisa-group/PostTrainBench

## Documentation

- http://posttrainbench.com/

## Source

- [Agent Skill Exchange](https://agentskillexchange.com/skills/benchmark-cli-agents-on-autonomous-llm-post-training-with-posttrainbench/)