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.
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 Azure/AKS-Skills --skill holmesgpt-evalgit clone --depth 1 https://github.com/Azure/AKS-SkillsWrote 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/azure/aks-skills/holmesgpt-eval)<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.
<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>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 | $0.00112 | $0.01828 |
| Opus 5 | $0.00056 | $0.00914 |
| Sonnet 5 | $0.00022 | $0.00366 |
| Haiku 4.5 | $0.00011 | $0.00183 |
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.
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 — 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)
- 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.
- 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.
- 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.
- Run before/after as bash scripts (materialize then execute):
- Parse test_case.yaml and determine case controls before executing anything:
- 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
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.
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.
- 10d ago First seen · 116 lines · 112 tokens per session scan A 6cd8ca3a4a8c
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.
Other skills, from other repositories
author-test
Generate a test given sample. Parameters: C# SDK repository root; Package name: one of Azure.AI.Projects, Azure.AI.Projects.Agents or Azure.AI.Extensions.OpenAI; the sample to use as a starting point for the test.
azure-cosmos-db-py
Build Azure Cosmos DB NoSQL services with Python/FastAPI following production-grade patterns. Use when implementing database client setup with dual auth (DefaultAzureCredential + emulator), service layer classes with CRUD operations, partition key strategies, parameterized queries, or TDD patterns for Cosmos. Triggers…
azure-microsoft-playwright-testing-ts
Run Playwright tests at scale using Azure Playwright Workspaces (formerly Microsoft Playwright Testing). Use when scaling browser tests across cloud-hosted browsers, integrating with CI/CD pipelines, or publishing test results to the Azure portal.
cosmos-run-integration-tests
Run azure-cosmos integration/customer-workflow tests locally, closely following the CI pipeline (build+install, then failsafe verify with a test profile) using one consistent JDK for both steps. USE WHEN: asked to run cosmos integration tests, customer-workflow tests (fi-customer-workflows / fi-sm-customer-workflows)…
dup-classes
Verify whether generated Java classes duplicate openai-java models by comparing fields/types (names may differ). Use when checking for duplicate model coverage.
run-tests
Run project tests using Maven (mvn). Use when the user asks to run tests.