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 openshift-eng/ai-helpers --skill diagnose-job-run-symptomsgit clone --depth 1 https://github.com/openshift-eng/ai-helpersWrote 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/openshift-eng/ai-helpers/diagnose-job-run-symptoms)<a href="https://agentmods.dev/skills/openshift-eng/ai-helpers/diagnose-job-run-symptoms"><img src="https://agentmods.dev/badge/skills/openshift-eng/ai-helpers/diagnose-job-run-symptoms/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/openshift-eng/ai-helpers/diagnose-job-run-symptoms"><img src="https://agentmods.dev/badge/skills/openshift-eng/ai-helpers/diagnose-job-run-symptoms.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.01645 |
| Opus 5 | $0.00017 | $0.00822 |
| Sonnet 5 | $0.00007 | $0.00329 |
| Haiku 4.5 | $0.00003 | $0.00164 |
Grade A, and why
diagnose-job-run-symptoms 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 12d 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.
How it starts
The opening of the file, as written. The whole thing — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Diagnose Job Run Symptoms
Sippy Symptoms are known-failure signatures for OpenShift CI. A symptom is a rule made of a file pattern (a glob over a CI job run's artifact files, e.g. **/build-log.txt) and a matcher (string = substring, regex = regular expression, none = file merely exists, cel = a compound CEL expression over other label names). When a symptom matches a job run's artifacts, Sippy applies one or more Labels — human-readable tags like InfraFailure — to that run. Labels appear in the Sippy UI and Spyglass and help everyone quickly recognize known failure modes without re-debugging them. You do not need any prior Sippy knowledge to use this skill.
This skill takes a Prow job run URL and explains which symptoms matched the run and what each applied label means — including the matched file and text.
When to Use This Skill
Use this skill when:
- A CI job failed and you want to know if it is a known failure mode before debugging it from scratch
- You are about to create a new symptom and want to check what already matched the run (see
manage-symptoms) - You want a plain-language explanation of the labels shown on a run in the Sippy UI or Spyglass
Prerequisites
-
Default mode (already-applied labels): only network access to public GCS (
https://storage.googleapis.com) and the public Sippy API (https://sippy.dptools.openshift.org) — no authentication required. -
Deep mode (
--deep, server-side dry-run rescan): a Bearer token from the DPCR cluster.- Must be logged into the DPCR cluster via
oc login - Cluster API:
https://api.cr.j7t7.p1.openshiftapps.com:6443 - Use the
oc-authskill to obtain the token (see the token-acquisition snippet inreevaluate-job-runs/SKILL.md); preferexport SIPPY_TOKEN=...over--token— argv is visible in process listings
- Must be logged into the DPCR cluster via
-
Python 3: Python 3.6 or later, standard library only.
Implementation Steps
Step 1: Default mode — explain already-applied labels
What ships with it
2 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.
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.
- 12d ago First seen · 110 lines · 35 tokens per session scan A 13199967790c
diagnose-job-run-symptoms is a skill published in the GitHub repository openshift-eng/ai-helpers (116 stars, last pushed today), licensed Apache-2.0. It adds 35 tokens to every session and 1,645 once invoked, about $0.0002 per session on Opus 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 2026-08-30.
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