Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add raja21068/AutoResearch --skill training-checkgit clone --depth 1 https://github.com/raja21068/AutoResearchWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/raja21068/autoresearch/training-check)<a href="https://agentmods.dev/skills/raja21068/autoresearch/training-check"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/training-check/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/raja21068/autoresearch/training-check"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/training-check.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00035 | $0.00986 |
| Opus 5 | $0.00017 | $0.00493 |
| Sonnet 5 | $0.00007 | $0.00197 |
| Haiku 4.5 | $0.00003 | $0.00099 |
Grade A, and why
training-check scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to training-check — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Training Check
You are now in interactive watch / 交互式训练监控模式.
Keep the current session open and report directly in the current terminal. The user is watching this terminal for updates. By default, run a training health check every 30 minutes, output a concise but complete analysis report after each check, state the next check time, then continue monitoring.
This skill checks training quality, not basic process health. Process health checks such as whether a tmux session exists or whether the GPU is idle can be handled by watchdog-style tooling; this skill focuses on whether the run is still worth continuing.
Inputs To Establish First
Before the first check, identify or ask for the minimum monitoring context:
- WandB run path or URL, if available.
- Fallback log path, SSH command, or local command for reading recent training logs.
- Training target, expected baseline, and key metrics that define success.
- How the training was launched, so it can be stopped if needed.
- Project notes path for recording decisions and evidence.
If a source is unavailable, say so clearly and continue with the available source. If both WandB and fallback logs are unreachable, report the connectivity issue, classify the round as WAIT, and check again later. Do not infer that training is bad only because data is unreachable.
Per-Round Check
Every round, read WandB first when configured. If WandB is unreachable, read the fallback logs. Inspect at least:
- Training loss trend over recent checkpoints or steps.
- Eval metrics and whether they improve, flatten, or degrade against baseline.
- NaN or Inf in loss, gradients, activations, or logged metrics.
- Sudden loss spikes, divergence, or repeated failed evaluations.
- Learning rate schedule behavior.
- Gradient norm, if logged.
- Plateau patterns that suggest the run is no longer useful.
Output one report in the current terminal with this structure:
## Training Check - <local timestamp>
- Data source: wandb_ok | log_fallback | unreachable
- Run: <wandb run or training identifier>
- Recent metrics: <loss/eval/lr/grad summary>
- Anomalies: <NaN/Inf/spike/divergence/plateau findings>
- Evidence: <WandB URL, log lines, metric values, or files inspected>
- Decision: CONTINUE | WAIT | STOP
- Reason: <why this decision is justified>
- Next check: <local timestamp, normally 30 minutes later unless ending>
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago First seen · 84 lines · 35 tokens per session scan A e6c225a521aa
training-check is a skill published in the GitHub repository raja21068/AutoResearch (2 stars, last pushed 3mo ago), licensed MIT. It adds 35 tokens to every session and 986 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to training-check, differing in 2 lines, and is treated as a copy.
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