debug-inference

A troubleshooting guide for InferenceService deployments on OpenShift AI. An InferenceService is a deployment that exposes a machine-learning model so applications can send it requests.

In plain words
What is it for?
Use it to inspect deployment conditions, events, pod logs, GPU scheduling, and inference latency.
Why use it?
It helps identify why a model will not start, responds with errors, is slow, or cannot obtain a GPU.

Skill for Claude CodeCodex

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.

agentmods
npx agentmods add skills/rhecosystemappeng/agentic-plugins/debug-inference
Any agent
npx skills add RHEcosystemAppEng/agentic-plugins --skill debug-inference
Clone the repo
git clone --depth 1 https://github.com/RHEcosystemAppEng/agentic-plugins

Made for: Claude Code, Codex.

Per session 107 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,554 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. Scan, not verified.
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 $0.00107 $0.03554
Opus 5 $0.00053 $0.01777
Sonnet 5 $0.00021 $0.00711
Haiku 4.5 $0.00011 $0.00355

Measured 2d ago against content hash 9c4d8b95fe11, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

debug-inference scanned grade A with 1 finding 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 2d 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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

3. Test endpoint: curl command to the inference URL
rh-ai-engineer/skills/debug-inference/SKILL.md · 361 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

7 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. 2d ago First seen · 361 lines · 107 tokens per session scan A 9c4d8b95fe11

Subscribe to this mod's changes

debug-inference is a skill published in the GitHub repository RHEcosystemAppEng/agentic-plugins (50 stars, last pushed 8d ago), with no licence file. It adds 107 tokens to every session and 3,554 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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