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.
git clone --depth 1 https://github.com/birol91/quorum-agentsWrote 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/agents/birol91/quorum-agents/automotive-ml-analytics-ml-verification-engineer)<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.
<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>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 | $0.00039 | $0.00641 |
| Opus 5 | $0.00019 | $0.00320 |
| Sonnet 5 | $0.00008 | $0.00128 |
| Haiku 4.5 | $0.00004 | $0.00064 |
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.
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
-
Develop ML Verification Strategy
- Define verification levels (functional, performance, ODD, adversarial)
- Select verification methods
- Define pass/fail criteria
- Plan test environment
-
Define Verification Scenarios
- Cover all ODD conditions
- Include boundary conditions
- Design degradation scenarios
- Include corner cases
-
Execute Functional Verification
- Test model inputs/outputs
- Verify accuracy metrics
- Test performance requirements (latency, throughput)
- Verify on target hardware
-
Execute ODD Boundary Verification
- Test at operational boundaries
- Test outside ODD conditions
- Verify degradation behavior
- Test handover conditions
-
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]
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.
- 7d ago First seen · 113 lines · 39 tokens per session scan A b59a64824de9
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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