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.
Install
npx skills add https://github.com/agentskillexchange/skills/tree/main/skills/benchmark-cli-agents-on-autonomous-llm-post-training-with-posttrainbench
claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install agentskillexchange-skills@llmmart
git clone https://github.com/agentskillexchange/skills.git
The skills CLI installs just this skill, for any of its supported agents. Claude Code installs the whole agentskillexchange/skills collection as a plugin from our marketplace. Git is the plain clone.
Skill manifest
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.
Documentation
Source
Files (skills)
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SKILL.md 1.7 KB
--- 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/)
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