# training-check

Checks WandB metrics during training to spot NaN values, loss divergence, and metric degradation before a run finishes.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 482 installs · ★ 18090
- Author: wanshuiyin
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/HEAD/skills/training-check/SKILL.md
- Updated: 2026-10-24
- HTML: https://affpapa.org/en/nevibecode/skills/training-check

## What it does

The skill reads WandB run history and checks loss trends, evaluation metrics, learning rate, and gradient norm. It looks for NaN, Inf, abrupt spikes, and sustained degradation. When a run is clearly failing, it stops training and records the run URL, key metrics, and the reason.

Ambiguous signals are sent to Codex MCP with `gpt-6-astra` for a STOP, CONTINUE, or WAIT decision. Clear cases do not use that review. The check interval grows from 10 to 60 minutes when results stay healthy, then returns to a short interval after an anomaly.

If WandB cannot be reached, the skill reads a training log over SSH. `watchdog.py` covers process health and GPU activity, while training-check focuses on training quality. If both sources fail, it reports a connectivity problem and retries later, so you need at least one reachable source of evidence.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill training-check -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/training-check/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/training-check/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill training-check -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/training-check/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill training-check -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/training-check/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install wanshuiyin/Auto-claude-code-research-in-sleep training-check
```

2. **Manually: put the files in place**
- File: `.github/skills/training-check/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill training-check -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep --path skills/training-check
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/training-check/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill training-check -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/HEAD/skills/training-check/SKILL.md

## When to use it
- detect NaN and Inf in loss or gradients
- stop a run when loss keeps diverging
- flag evaluation metrics that fall below a baseline
- ask Codex MCP to judge unclear metric trends
- extend the polling interval after healthy checks

## Example prompts
- Run `/training-check entity/project/run_id` and inspect the current loss trend.
- Set up recurring `/training-check entity/project/run_id` checks for this active run.
- Use `/training-check entity/project/run_id` to assess evaluation metrics against the baseline.

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: ee6ef948ca979c6f.

BEGIN THIRD-PARTY FILE ee6ef948ca979c6f
```text
---
name: training-check
description: Periodically check WandB metrics during training to catch problems early (NaN, loss divergence, idle GPUs). Avoids wasting GPU hours on broken runs. Use when training is running and you want automated health checks.
argument-hint: "[wandb-run-path]"
allowed-tools: Bash(*), Read, Grep, Glob, Write, Edit, mcp__codex__codex, mcp__codex__codex-reply, mcp__antigravity__antigravity, mcp__antigravity__antigravity-reply, mcp__grok__grok, mcp__grok__grok-reply, mcp__claude-review__review, mcp__claude-review__review_reply, mcp__claude-review__review_start, mcp__claude-review__review_reply_start, mcp__claude-review__review_status
---

# Training Check

> **Before each reviewer slot**, follow [Selectable Reviewers](../shared-references/reviewer-backends.md):
> 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.

> ⏱ This skill is **correctly** cron-wired (see below): it polls
> machine-checkable training health (NaN / divergence / idle GPU) — the additive
> external-wait shape in
> [`shared-references/external-cadence.md`](../shared-references/external-cadence.md).
> The occasional Codex call for an ambiguous metric is a **one-shot** check per
> tick, not a multi-round verdict loop, so it stays additive — it never grows
> into a wrapped verdict skill.

## Context: $ARGUMENTS

## Constants

- WANDB_ENTITY and WANDB_PROJECT: read from CLAUDE.md or passed as argument (format: `entity/project/run_id`)
- CHECK_INTERVAL: starts at 10 minutes, then gradually increases if consistently healthy: 10 min → 20 min → 30 min → 60 min (cap)
- REVIEWER_MODEL = `gpt-6-astra` — used via Codex MCP for ambiguous cases only

## When to Use

- After training is confirmed running (session alive, loss decreasing for first few steps)
- Set up via CronCreate to fire periodically during training
```
END THIRD-PARTY FILE ee6ef948ca979c6f

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