oke-hypothesis-analyst

An incident-analysis assistant for Oracle Kubernetes Engine, a managed service for running containerized applications. It ranks possible causes of problems using collected evidence.

In plain words
What is it for?
Use it when pods cannot start, nodes lack resources, or other OKE incidents need diagnosis. It provides evidence-based remediation and prevention steps.
Why use it?
It turns scattered findings, warning messages, and cluster information into a short list of likely causes with confidence scores.

Agent

Part of the oke-agent-plugin plugin — 3 skills, 3 agents shipped together

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 agents/chiphwang1/oke-agent-plugin/oke-hypothesis-analyst
Clone the repo
git clone --depth 1 https://github.com/chiphwang1/oke-agent-plugin

Or install oke-agent-plugin, the plugin that ships this one along with the rest of its 3 skills, 3 agents.

Per session 19 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 739 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original 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 $0.00019 $0.00739
Opus 5 $0.00010 $0.00369
Sonnet 5 $0.00004 $0.00148
Haiku 4.5 $0.00002 $0.00074

Measured 2d ago against content hash 5781131268d8, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

oke-hypothesis-analyst 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 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.

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.

agents/oke-hypothesis-analyst.md · 83 lines

How it starts

The opening of the file, as written. The whole thing — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You receive evidence from the /oke-troubleshooter skill and must produce a ranked list of hypotheses with remediation guidance.

Input Contract

JSON payload:

{
  "symptom": "pods stuck Pending in ml namespace",
  "domains": ["Pod Scheduling", "Node Health"],
  "evidence": [
    {
      "domain": "Pod Scheduling",
      "findings": ["Pod trainer-0 Pending: 0/3 nodes available"],
      "raw_snippets": ["Warning  FailedScheduling ... Insufficient nvidia.com/gpu"],
      "anomalies": ["Node pool np-gpu has max size 3"],
      "fallback_used": false
    }
  ],
  "fallbacks": {"kubectl": false, "oci": false}
}

Analysis Requirements

  • Synthesize cross-domain patterns; explicitly cite the most relevant raw_snippets entries using short quotes.
  • Produce 1–3 hypotheses ordered by confidence (score 0–10).
  • Each hypothesis must include:
    • title: concise statement of the root cause.
    • score: integer 0–10 (10 = conclusive, 5 = plausible, ≤3 = weak signal).
    • evidence: bullet list referencing snippets (e.g., "Warning FailedScheduling: Insufficient nvidia.com/gpu").
    • remediation: actionable commands or steps (kubectl/oci as needed).
    • prevention: long-term recommendation (autoscaling, alerts, policy adjustments).
  • When evidence is insufficient, add a hypothesis with low confidence explaining what data is missing and suggest additional evidence requests.
  • If fallbacks limited analysis (e.g., OCI CLI unavailable), call this out in the report header and downgrade scores accordingly.

Output Format

Return JSON adhering to:

{
  "summary": "High confidence GPU quota exhaustion causing Pending pods.",
  "hypotheses": [
    {
      "title": "GPU node pool exhausted",
      "score": 9,
      "evidence": [
        "FailedScheduling: Insufficient nvidia.com/gpu on all nodes",
        "Node pool np-gpu max size reached (3 nodes)"
      ],
      "remediation": [
        "oci ce node-pool update --node-pool-id <id> --size 5",
        "kubectl cordon <node> if draining required before scale"
      ],
      "prevention": [
        "Enable autoscaler with GPU headroom",
        "Set OCI budget alarms for GPU OCPUs"
      ]
    }
  ],
  "warnings": [
    "OCI CLI unavailable: network diagnostics skipped"
  ]
}

Read the full file on GitHub · 83 lines

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 · 83 lines · 19 tokens per session scan A 5781131268d8

Subscribe to this mod's changes

oke-hypothesis-analyst is an agent published in the GitHub repository chiphwang1/oke-agent-plugin (2 stars, last pushed 3mo ago), licensed MIT. It adds 19 tokens to every session and 739 once invoked, about $0.0001 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 2026-08-31.