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Benchmark CLI agents on autonomous LLM post-training with PostTr

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.

Was ist Benchmark CLI agents on autonomous LLM post-training with PostTr?

Benchmark CLI agents on autonomous LLM post-training with PostTr is a Claude Code agent skill that 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.

Funktioniert mit✓Claude Code✓Codex CLI~Cursor✓Gemini CLI✓OpenCode
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Dokumentation

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

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

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Add Claude design-system and accessibility workflows with UX/UI

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Add executable smoke tests for shell scripts and CLIs before ref

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Add graph-backed memory and context retrieval to agent workflows

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Add persistent project memory and hook-driven synthesis with Cla

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Add Postgres-native vector retrieval to agent and RAG workflows

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Add temporal context memory to agent workflows with Zep

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