holmesgpt-eval

holmesgpt-eval is a skill for Claude Code from Azure/AKS-Skills. It costs 112 tokens per session (1,828 once invoked), scanned A, original, MIT.

A test runner for HolmesGPT, using fixed test cases to evaluate how the current agent answers questions. HolmesGPT is a system for investigating Kubernetes problems.

In plain words
What is it for?
Running all evaluation cases or selected cases, preparing test fixtures, scoring answers, and producing JSON reports.
Why use it?
It provides a repeatable way to compare the agent's answers with expected results instead of judging them informally.

Skill for Claude Code ✓ vendor

Written for Claude Code: shipped in a Claude Code plugin.

Part of the aks plugin — 8 skills, 1 MCP server shipped together

Good fit Running all evaluation cases or selected cases, preparing test fixtures, scoring answers, and producing JSON reports.

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Install with agentmods
npx agentmods add skills/azure/aks-skills/holmesgpt-eval
About the project

Azure/AKS-Skills is a collection of agent skills for operating Azure Kubernetes Service clusters after they are provisioned. It helps compatible coding agents investigate incidents, control costs, assess AKS Automatic readiness, run GPU and inference workloads, capture packet-level evidence, and design clusters. The catalogue entries provide the skills, MCP integration, and plugin configuration for these AKS operations.

Azure/AKS-Skills · 4 stars · on GitHub

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 Azure/AKS-Skills --skill holmesgpt-eval
Clone the repo
git clone --depth 1 https://github.com/Azure/AKS-Skills

Made for: Claude Code.

Or install aks, the plugin that ships this one along with the rest of its 8 skills, 1 MCP server.

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 holmesgpt-eval

README.md
[![agentmods](https://agentmods.dev/badge/skills/azure/aks-skills/holmesgpt-eval/github.svg)](https://agentmods.dev/skills/azure/aks-skills/holmesgpt-eval)
Your own site
<a href="https://agentmods.dev/skills/azure/aks-skills/holmesgpt-eval"><img src="https://agentmods.dev/badge/skills/azure/aks-skills/holmesgpt-eval/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 holmesgpt-eval

Your own site · 80×15
<a href="https://agentmods.dev/skills/azure/aks-skills/holmesgpt-eval"><img src="https://agentmods.dev/badge/skills/azure/aks-skills/holmesgpt-eval.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 112 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,828 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 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 5.1 $0.00112 $0.01828
Opus 5 $0.00056 $0.00914
Sonnet 5 $0.00022 $0.00366
Haiku 4.5 $0.00011 $0.00183

Measured 10d ago against content hash 6cd8ca3a4a8c, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

holmesgpt-eval 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 10d ago.

The scan reads SKILL.md. This mod also ships 3 executable files (scripts/fetch_fixtures.sh, scripts/gold_blind.sh, scripts/gold_split.py), 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.

evals/holmesgpt-eval/SKILL.md · 116 lines

How it starts

The opening of the file, as written. The whole thing — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.

HolmesGPT Eval — Agent-Native (Self-Answer)

Primary purpose

  • Evaluate the current agent against HolmesGPT's eval fixtures by having the agent answer each user_prompt itself.
  • No HTTP bridge or shell wrapper for answering — the agent's own tools should be used to produce the answer string that gets scored.
  • Supports two modes: bulk run (all or filtered tests) and individual run (single specific test case).

Agent-native flow (what the agent should do)

  1. Check for existing fixtures:
    • If vendor/holmesgpt/tests/llm/fixtures/test_ask_holmes exists, skip fetch and use existing fixtures.
    • Otherwise, call scripts/fetch_fixtures.sh via exec to download/unzip HolmesGPT and return tests/llm/fixtures/test_ask_holmes.
  2. Determine run mode:
    • Bulk run: If no specific test case is specified, iterate through all case directories (optionally filtered by tags).
    • Individual run: If a specific test case is specified (e.g., "01_how_many_pods", "09_crashpod"), run only that single test.
  3. Iterate each case directory containing test_case.yaml:
    • Parse test_case.yaml and determine case controls before executing anything:
      • If tags are specified for the run, check if the test_case.yaml contains a matching tag in its tags field. If not, ignore this case completely (do not record, process, or report it).
      • If evaluation.correctness.expected_score == 0 → SKIP this case entirely (do not run before_test/after_test). Record as skipped with reason.
      • Determine evaluation mode for this case (see Scoring Rules → Mode selection).
    • For non-skipped cases:
      • Run before/after as bash scripts (materialize then execute):
        • Write the before_test block verbatim to .before.sh in the case directory.
        • Write the after_test block verbatim to .after.sh in the case directory.
        • Execute with a non-interactive shell and strict flags from the case directory:
          • before: bash --noprofile --norc -eo pipefail ./.before.sh
          • after: bash --noprofile --norc -eo pipefail ./.after.sh
        • Always set cwd to the case directory so relative paths resolve.
        • Capture stdout, stderr, and exit code for both before and after.
      • Ask the agent itself the user_prompt (inline in the current session) and capture the raw textual reply.
        • The agent MUST answer the prompt directly without creating script files (.py, .sh, etc.) unless explicitly required by the test case.
        • The agent should use available tools to investigate and provide answers in the current session.
        • If the test runs longer than 5 minutes, stop the test immediately and mark it as failed with reason "timeout_exceeded".
      • Score the agent's answer against expected_output.
      • Always run after_test (as above) to clean up, even if before/ask/score failed.
  4. Write results under evals/holmesgpt-eval/results/:
    • results.json — array of case results (prompt, expected, output, pass/fail, skipped, details)
    • report.md — concise summary (pass/fail/skip counts; per-case status, expected list, output, missing elements if any)
    • latest-results.md — points to the latest report

Scoring rules for the agent

  • Mode selection (per-case):

    • Default to loose mode.

    • If the test_case.yaml contains evaluation.correctness.type, use that value to set the mode for this case.

      • Supported values (case-insensitive): strict, loose
      • Unknown/absent values: fall back to loose
    • Example YAML snippet:

      evaluation: correctness: type: strict

  • Modes:

    • strict: pass only if ALL expected elements are sufficiently present in the output
    • loose: pass if the output reasonably matches the expected content overall
  • “Sufficiently present” guidance:

    • Case-insensitive; ignore trivial punctuation/whitespace differences
    • Numeric normalization OK when unambiguous (e.g., 14 vs fourteen)
    • Minor paraphrases allowed if the essence is clear; elements may span multiple sentences
  • Deterministic baseline when unsure:

    • strict = all expected substrings present (case-insensitive)
    • loose = any expected substring present

Read the full file on GitHub · 116 lines

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. 10d ago First seen · 116 lines · 112 tokens per session scan A 6cd8ca3a4a8c

Subscribe to this mod's changes

holmesgpt-eval is a skill published in the GitHub repository Azure/AKS-Skills (4 stars, last pushed today), licensed MIT. It adds 112 tokens to every session and 1,828 once invoked, about $0.0006 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.

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