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Training Check

wanshuiyin/auto-claude-code-research-in-sleep

Polls WandB metrics during training runs to catch NaN values, loss divergence, and gradient explosions before you waste GPU hours. Starts checking every 10 minutes, then backs off to hourly if everything looks healthy. The clever bit is it only calls out to GPT-5.5 via Codex when metrics are ambiguous, like a flattening loss curve that might be normal or might be trouble. Otherwise it just applies hard rules and stays out of your way. Works alongside the watchdog for process-level checks. Set it up with CronCreate after your first few training steps stabilize and let it run unattended. Better than discovering at 3am that your 16-hour run diverged after step 100.

skills.sh installs
512
GitHub stars
18.9K
last updated
Aug 21, 2026
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Install

$ npx -y skills add wanshuiyin/auto-claude-code-research-in-sleep --skill training-check --agent claude-code

Installs into .claude/skills of the current project.

Files

Training Check

Before each reviewer slot, follow Selectable Reviewers: resolve invocation → .aris/reviewer.json → Claude Code default (Codex MCP) with reviewer_route.py through the canonical helper resolver, preserving this slot's tier, fresh contexts, source/image inputs and deterministic gates. Codex examples/pins below apply only to backend=codex; other routes use their own first-call/reply contract. Save the route and actual returned identity; unavailable selections are REVIEW_UNAVAILABLE. Classify acceptance from actual families/completion/scope. Forward invocation reviewer/model/effort overrides to reviewer-bearing children. Generation workers are unchanged.

Periodically read WandB metrics during training to catch problems early. Do not wait until training finishes to discover it was a waste of GPU time.

SKILL.md on GitHub

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Details

Repository
github.com/wanshuiyin/auto-claude-code-research-in-sleep
Categories
Data Science & ML, Automation & Workflows
First seen
Jun 3, 2026

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