一项Claude Code技能，将代理转变为自主机器学习实习生：它研究、实施、培训、自我验证并发布机器学习代码，使用Hugging Face生态系统。Telegram + Slack 里程碑提醒。

将huggingface/ml-intern的行为移植作为单一的Claude代码技能——无需额外守护进程、无需额外LLM客户端或额外授权。该技能重用了Claude代码已有的代理循环、工具和自动压缩功能。

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它的作用

给定一个指令，比如*“在~100M参数下实现DeepSeek-V3并训练TinyStories”*，技能会执行，无需额外输入：

1. 重述任务并列出未知→ TASK.md
2. 澄清 只有当某事确实有歧义时——而且只有在尝试先研究未知**之后（你不认识的术语/仓库/论文会在你问问题前被WebSearch和阅读）。互动运行通过捆绑问题提问;无头-p运行写入最佳猜测默认+触发approval_required，且绝不挂机。
3. 预算→前期运行 BUDGET.md（最大解决方案路径、每条路径最大重试次数、GPU小时数/墙时钟计算上限、规模+代币上限）。

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.

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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 default (serializes on CUDA OOM). Single obvious path → single subagent. On agents without subagents (e.g. Codex CLI) the paths run inline and sequentially.
7. Smoke-test (per path): instantiate, print param count, run a forward pass on random tensors before any training
8. Train (per path) with NaN guard, LR schedule, checkpointing, eval, generation sampling at milestones
9. Self-verify (per path) → VERIFY.md with six independent verdicts (generation sanity, loss-vs-baseline, eval-tracks-train, data consumption, stderr scan, param-count drift). A low loss number alone never declares success — the skill exists in part because of a real failure where a model reported train_loss=0.03 / eval=0.02 but generated word salad (causal mask leak via the CSA Compressor).