Ml Autopilot
Arm autonomous mode for hands-off or budgeted ML work. Use when the user asks for unattended training runs, gives a time budget, or wants iteration to continue without supervision.
Install
$ agentstack add skill-boeschj-ml-intern-plugin-ml-autopilot ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-boeschj-ml-intern-plugin-ml-autopilot) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
!"${CLAUDE_PLUGIN_ROOT}/scripts/autopilot_start.py" $ARGUMENTS
Autopilot is armed: a stop guard now blocks this session from going idle while budget remains. The guard reconciles against real job state before every block and disarms itself when the deadline passes.
Rules while autopilot is armed:
NEVER stop working while budget remains. Nobody is watching; they expect the entire budget used productively. Do not ask "should I continue?", there is nobody to answer.
Your workflow is a loop, not a checklist. Once you have a working result, keep iterating:
- Research the approach (delegate to hf-researcher, read docs, find examples).
- Implement (write code, set up training per /hf-jobs).
- Train and evaluate.
- Push the model to the Hub, always.
- Improve: tune hyperparameters, try different data, adjust the recipe, try a different approach.
- Go to 1.
HYPERPARAMETER TUNING: do not tune by hand one value at a time. Write one sweep script that launches a grid and evaluates each run automatically. One well-designed sweep beats ten manual experiments.
Out of ideas: go back to the literature. Crawl citation graphs deeper, read methodology sections of papers you have not read, extract new datasets and tricks, combine recipes across papers, re-read the task and the training logs. There is always a paper you have not read, and it probably has a better dataset.
Budget your time: reserve the final 10 minutes for evaluation and model saving. The task is not done until the required output exists on the Hub and you have evaluated it.
Disarm early by deleting the autopilot state: rerun this skill with 0.01 hours to let the deadline lapse.
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: boeschj
- Source: boeschj/ml-intern-plugin
- License: Apache-2.0
- Homepage: https://github.com/boeschj/ml-intern-plugin#quickstart
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.