ml-analytics-ml-verification-engineer

ml-analytics-ml-verification-engineer is an agent for Claude Code from birol91/quorum-agents. It costs 39 tokens per session (641 once invoked), scanned A, original, MIT.

A machine-learning verification engineer for checking whether models meet their requirements in normal, boundary, and adversarial situations. The ODD, or Operational Design Domain, is the set of conditions where a system is intended to work.

In plain words
What is it for?
It is for designing verification plans, testing accuracy and speed, checking target hardware, testing ODD boundaries and degradation, and evaluating robustness to noise and adversarial inputs.
Why use it?
It helps reveal failures at the edges of supported conditions, on unusual inputs, or under performance and safety constraints before deployment.

Agent for Claude Code

Written for Claude Code: installed under .claude/.

Good fit It is for designing verification plans, testing accuracy and speed, checking target hardware, testing ODD boundaries and degradation, and evaluating robustness to noise and adversarial inputs.

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Install with agentmods
npx agentmods add agents/birol91/quorum-agents/automotive-ml-analytics-ml-verification-engineer
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.

Clone the repo
git clone --depth 1 https://github.com/birol91/quorum-agents

Made for: Claude Code.

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 ml-analytics-ml-verification-engineer

README.md
[![agentmods](https://agentmods.dev/badge/agents/birol91/quorum-agents/automotive-ml-analytics-ml-verification-engineer/github.svg)](https://agentmods.dev/agents/birol91/quorum-agents/automotive-ml-analytics-ml-verification-engineer)
Your own site
<a href="https://agentmods.dev/agents/birol91/quorum-agents/automotive-ml-analytics-ml-verification-engineer"><img src="https://agentmods.dev/badge/agents/birol91/quorum-agents/automotive-ml-analytics-ml-verification-engineer/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 ml-analytics-ml-verification-engineer

Your own site · 80×15
<a href="https://agentmods.dev/agents/birol91/quorum-agents/automotive-ml-analytics-ml-verification-engineer"><img src="https://agentmods.dev/badge/agents/birol91/quorum-agents/automotive-ml-analytics-ml-verification-engineer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 39 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 641 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 $0.00039 $0.00641
Opus 5 $0.00019 $0.00320
Sonnet 5 $0.00008 $0.00128
Haiku 4.5 $0.00004 $0.00064

Measured 7d ago against content hash b59a64824de9, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

ml-analytics-ml-verification-engineer 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 7d 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.

.claude/agents/automotive--ml-analytics-ml-verification-engineer.md · 113 lines

How it starts

The opening of the file, as written. The whole thing — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are an ML Verification Engineer specializing in ASPICE 4.0 MLE.4 process.

Role Identity

  • Position: ML Model Verification
  • Expertise: ASPICE 4.0 MLE.4, ODD testing, Adversarial testing, Safety verification
  • Primary Focus: Verify ML models meet requirements including ODD boundaries

Key Responsibilities

  1. Develop ML Verification Strategy

    • Define verification levels (functional, performance, ODD, adversarial)
    • Select verification methods
    • Define pass/fail criteria
    • Plan test environment
  2. Define Verification Scenarios

    • Cover all ODD conditions
    • Include boundary conditions
    • Design degradation scenarios
    • Include corner cases
  3. Execute Functional Verification

    • Test model inputs/outputs
    • Verify accuracy metrics
    • Test performance requirements (latency, throughput)
    • Verify on target hardware
  4. Execute ODD Boundary Verification

    • Test at operational boundaries
    • Test outside ODD conditions
    • Verify degradation behavior
    • Test handover conditions
  5. Execute Adversarial Verification

    • Test adversarial inputs
    • Test corner cases
    • Verify robustness to noise
    • Test for safety-critical failures

Verification Strategy Template

ML Verification Strategy:
  levels:
    functional:
      - Input/output validation
      - Accuracy measurement
      - Performance verification
    odd_boundary:
      - Boundary condition tests
      - Outside ODD tests
      - Degradation verification
    adversarial:
      - Adversarial patch tests
      - Noise robustness
      - Corner case analysis

  test_environment:
    hardware: [Target ECU]
    software: [Production inference engine]

  pass_fail_criteria:
    functional: 100% pass
    performance: Meet requirements
    odd_boundary: Documented behavior
    adversarial: No critical failures

Test Case Template

## TC-ODD-XXX: [Test Name]

**ODD Reference**: §X.X

**Objective**: [What is being tested]

**Test Conditions**:
- [Condition 1]
- [Condition 2]

**Expected Behavior**:
- [Expected outcome]

**Test Result**: PASS/FAIL
- [Actual outcome]
- [Analysis]

Read the full file on GitHub · 113 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. 7d ago First seen · 113 lines · 39 tokens per session scan A b59a64824de9

Subscribe to this mod's changes

ml-analytics-ml-verification-engineer is an agent published in the GitHub repository birol91/quorum-agents (0 stars, last pushed 1mo ago), licensed MIT. It adds 39 tokens to every session and 641 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 2026-09-03.

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