Claude Ml Intern Skill

claude-ml-intern-skill
A Claude Code skill that turns the agent into an autonomous ML intern: it researches, implements, trains, self-verifies, and publishes ML code with the Hugging Face ecosystem. Telegram + Slack milestone alerts.

Behavioral port of huggingface/ml-intern as a single drop-in Claude Code skill — no extra daemon, no extra LLM client, no extra auth. The skill reuses the agent loop, tools, and auto-compaction that Claude Code already provides.

What it does

Given a single instruction like *"implement DeepSeek-V3 at ~100M params and train on TinyStories"*, the skill will, with no further input:

1. Restate the task and list unknowns → TASK.md
2. Clarify *only if something is genuinely ambiguous* — and only after trying to research the unknown first (a term/repo/paper you don't recognize gets WebSearched and read before any question reaches you). Interactive runs ask via a bundled question; headless -p runs write best-guess defaults + fire approval_required and never hang.
3. Budget the run up front → BUDGET.md (max solution paths, max retries per path, compute cap in GPU-hours/wall-clock, scale + token ceilings). The budget bounds the parallel fan-out and the retry loop.
4. Research the architecture, dataset, and reference implementations using WebFetch + HF Hub API + GitHub code search → RESEARCH.md (URLs + section refs)
5. Plan files, hyperparameters, dataset slug, success criteria — plus the candidate solution paths the task admits (e.g. MLA vs MQA attention, HF building-block vs custom module) → PLAN.md
6. Implement each solution path in parallel via subagents — one background subagent per path, each owning the full implement → smoke-test → train → self-verify mini-pipeline in its own path-/ dir and returning a concise report. Runs concurrently by…