check-failed-run

check-failed-run is a skill for Claude Code from lawwu/agentic-ml-plugin. It costs 81 tokens per session (2,487 once invoked), scanned A, original, no licence file.

A diagnostic tool for failed or unstable machine-learning training runs. It examines logs, saved files, processes, experiment trackers, and Vertex AI pipelines to classify the failure and find its likely cause.

In plain words
What is it for?
Use it to investigate failed training jobs, analyse why a run became unstable, and produce a prioritised recovery plan.
Why use it?
It gathers evidence from the places where training-run details are stored, so debugging does not rely on guesswork.

Skill for Claude Code

Written for Claude Code: argument-hint in frontmatter.

Part of the agentic-ml plugin — 15 skills shipped together

Good fit Use it to investigate failed training jobs, analyse why a run became unstable, and produce a prioritised recovery plan.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/lawwu/agentic-ml-plugin/check-failed-run
Install

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.

Any agent
npx skills add lawwu/agentic-ml-plugin --skill check-failed-run
Clone the repo
git clone --depth 1 https://github.com/lawwu/agentic-ml-plugin

Made for: Claude Code.

Or install agentic-ml, the plugin that ships this one along with the rest of its 15 skills.

Wrote 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.

agentmods badge for check-failed-run

README.md
[![agentmods](https://agentmods.dev/badge/skills/lawwu/agentic-ml-plugin/check-failed-run/github.svg)](https://agentmods.dev/skills/lawwu/agentic-ml-plugin/check-failed-run)
Your own site
<a href="https://agentmods.dev/skills/lawwu/agentic-ml-plugin/check-failed-run"><img src="https://agentmods.dev/badge/skills/lawwu/agentic-ml-plugin/check-failed-run/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.

agentmods 80×15 button for check-failed-run

Your own site · 80×15
<a href="https://agentmods.dev/skills/lawwu/agentic-ml-plugin/check-failed-run"><img src="https://agentmods.dev/badge/skills/lawwu/agentic-ml-plugin/check-failed-run.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 81 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,487 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin unknown No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00081 $0.02487
Opus 5.5 $0.00032 $0.00995
Sonnet 5.5 $0.00016 $0.00497
Haiku 4.5 $0.00008 $0.00249

Measured 1mo ago against content hash 1babbda63807, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

check-failed-run 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 1mo ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/fetch-remote-log.sh), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

plugins/agentic-ml/skills/check-failed-run/SKILL.md · 238 lines

The source is not reproduced here

No licence file

A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.

Read it on GitHub

Files

What ships with it

3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

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.

  1. 1mo ago First seen · 238 lines · 81 tokens per session scan A 1babbda63807

Subscribe to this mod's changes

check-failed-run is a skill published in the GitHub repository lawwu/agentic-ml-plugin (3 stars, last pushed 7mo ago), with no licence file. It adds 81 tokens to every session and 2,487 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Related

Other skills, from other repositories

notebooklm

Install, authenticate, troubleshoot, and operate Gemini Notebook through the notebooklm-py CLI or typed async Python API. Use for notebook and source management, grounded chat and research, and artifact generation or download when the user mentions Gemini Notebook, notebooklm-py, the notebooklm CLI, or its Python API.…

teng-lin/notebooklm-py · 79 tokens

rocketride-debugging-pipelines

Use when a RocketRide pipeline run failed, errored, or produced wrong/empty output, and you need to find the failing node and fix it. Reads run status and execution traces, diagnoses the cause, and routes back to design or configuration. Also use directly when asked to debug a run.

rocketride-org/rocketride-server · 67 tokens

trulens-instrumentation

Instrument LLM apps with TruLens OTEL-based tracing - from setup to debugging and optimization.

truera/trulens · 25 tokens

profile-vllm-performance

Profile vLLM and RT-VLM inference to identify and remove GPU-idle gaps, underfilled batches, transfer stalls, serialized multimodal work, scheduler gaps, or KV pressure. Use this skill when GPU utilization is unexpectedly low or bursty, capacity trails another runtime, TTFT or ITL regresses, or multimodal prefill and…

NVIDIA-AI-Blueprints/video-search-and-summarization · 93 tokens

prompt-debugging

Figure out why a prompt isn't working and fix it — diagnose the actual failure (ambiguity, missing context, wrong format, conflicting instructions) instead of randomly rewording. Use when asked why isn't my prompt working, the AI keeps ignoring my instructions, my prompt gives inconsistent results, or how do I fix…

mohitagw15856/pm-claude-skills · 131 tokens

prompt-optimizer

Diagnose and rewrite an underperforming LLM prompt so it produces reliable, well-structured output. Use when asked to improve a prompt, fix a prompt that gives inconsistent or wrong results, reduce hallucination/refusals, or make output follow a format. Produces a rewritten prompt with a diagnosis of what was failing…

mohitagw15856/pm-claude-skills · 86 tokens