Hugging Face ML Intern reimagined as an OpenAI Codex plugin. Research papers, inspect datasets and models, plan and evaluate AI/RAG systems, run training and evaluation on Hugging Face Jobs, and ship ML artifacts — all inside Codex.
What This Is
This repository replicates the core functionality of huggingface/mlintern-plugin (a Claude Code plugin) for the OpenAI Codex ecosystem. It is a Codex plugin, not a Claude Code plugin, using the Codex Skills and Plugin format.
The original mlintern-plugin wraps the ml-intern CLI binary inside Claude Code with slash commands. This Codex plugin instead uses:
Skills (SKILL.md files) that teach Codex how to use Hugging Face tools
Commands (commands/run.md) for /mlintern:run task execution
Scripts for operations the HF plugin doesn't directly expose (dataset inspection, paper research)
The Hugging Face Codex plugin (hugging-face MCP) for direct API tools (model_search, dataset_search, paper_search, hub_repo_details, hf_doc_search, hf_doc_fetch, hf_jobs)
Plugin Structure
./.agents/plugins/marketplace.json - Repo marketplace for Codex
Codex web browsing/search tools and citation links
hf-jobs
Submit and monitor cloud jobs
_hf_jobs (run, uv, ps, logs, inspect, cancel)
Comparison to Original
Feature
huggingface/mlintern-plugin (Claude Code)
This Codex Plugin
Platform
Claude Code
OpenAI Codex
Format
.claude-plugin manifest + companion script
.codex-plugin manifest + Skills
Interaction
Slash commands (/mlintern:run)
Slash commands + skill invocation
Agent Runtime
Spawns ml-intern CLI binary
Uses Codex's native agent loop + Skills
Paper Research
Built into ml-intern binary
papers.py script shim
Dataset Inspection
Built into ml-intern binary
inspect_dataset.py script shim
Job Submission
Built into ml-intern binary
_hf_jobs via Hugging Face Codex plugin
Sandbox
HF Space sandboxes
Codex local shell + _hf_jobs
Parity Status
This plugin intentionally mirrors the parts of ML Intern that matter most for HF ML work:
Area
Status
Notes
Paper discovery
Done
HF paper search plus the deeper papers.py research flow is available.
Paper reading
Done
Section reading, citations, recommendations, and linked resources are implemented.
Dataset validation
Done
Schema, splits, sample rows, parquet availability, and compatibility notes are covered.
HF docs lookup
Done
Search plus fetch are available for current HF library guidance.
End-to-end ML workflow
Done
The harness pushes research-first, validate-first behavior.
Generic web search
Partial
Best-effort Codex web-search guidance exists, but it is not the exact ML Intern DuckDuckGo wrapper.
For the closest ML Intern feel, use the paper and dataset skills first, then docs, then the harness workflow.
Behavioral Contract
When ML Intern is invoked directly, the plugin should behave like a research harness rather than a generic assistant:
Route through ml-intern -> ml-intern-harness for non-trivial AI/ML/RAG/search/evaluation tasks, even when they are not purely Hugging Face tasks.
Use plan tracking at the beginning, each phase transition, and completion when Codex exposes a planning tool.
Split research into explicit tracks before synthesis, such as platform constraints, technical approaches, and evaluation methodology.
Use hf-paper-search for literature, benchmarks, and evaluation methods.
Use web-search for current platform/API constraints, official docs, policies, pricing, rate limits, SDK behavior, and other non-HF facts.
Cite important architecture and evaluation decisions with papers, official docs, or primary sources.
If the user asks for "plan only", stop after research and do not write implementation code or scaffold files.
If a write, shell, network, or sandbox step fails, fail forward with an inline deliverable when possible.
Codex Compatibility Layer
This plugin cannot inject upstream Python tools into Codex directly, so it should reproduce the behavior of the upstream tools using Codex-native primitives:
Upstream tool
Codex compatibility layer
Required behavior to preserve
plan_tool
update_plan
Full-plan replacement, one in_progress item, updates at start/phase transition/completion
research
delegated sub-agent research when explicitly allowed, otherwise focused sequential research
Separate research context when possible, read-only scope, papers-first workflow, compact evidence-backed summary
Faithful plan_tool semantics to preserve:
Use it for tasks with 3 or more meaningful steps.
Replace the whole visible plan each update.
Keep exactly one item in progress.
Mark completed only after full success.
Faithful research semantics to preserve:
Main context stays focused on synthesis and decisions.
Research uses papers, citation graphs, dataset inspection, docs, GitHub examples, and web search.
Research returns compact findings with concrete references and recipe-level claims.
If separate delegation is unavailable, preserve the same research floor directly rather than skipping it.
Example plan-only trigger:
text
1[@ml-intern](plugin://ml-intern@ml-intern-codex)
2i want to query generic Discord servers in natural language.
3first figure out constraints and challenges, then research how to build and test quality.
4i'm only interested in the plan for now.
Expected behavior: track a plan, use web-search for Discord API constraints, use hf-paper-search for RAG/forum/social QA and evaluation research, synthesize a cited build-and-test plan, and avoid implementation.
Why This Exists
The original huggingface/mlintern-plugin is Claude Code only — it's a companion script that spawns the ml-intern CLI inside Claude Code sessions. There is no equivalent for Codex. The huggingface/skills repo provides general HF skills but not the full ML Intern harness. This plugin bridges the gap.
Contributing
This is an early version. The biggest improvement would be testing the skill instructions in real Codex sessions and tightening the guardrails where the LLM deviates. PRs welcome.
License
Apache-2.0
Generated by ML Intern
This model repository was generated by ML Intern, an agent for machine learning research and development on the Hugging Face Hub.
