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/equinor/neqsimWrote 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/equinor/neqsim/exam_alignment_reviewer.paperlab)<a href="https://agentmods.dev/agents/equinor/neqsim/exam_alignment_reviewer.paperlab"><img src="https://agentmods.dev/badge/agents/equinor/neqsim/exam_alignment_reviewer.paperlab.svg" alt="Measured on agentmods" 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 | $0.00027 | $0.00166 |
| Opus 5 | $0.00014 | $0.00083 |
| Sonnet 5 | $0.00005 | $0.00033 |
| Haiku 4.5 | $0.00003 | $0.00017 |
Grade A, and why
exam-alignment-reviewer 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 3d 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.
What it actually says
Exam Alignment Reviewer Agent
You check whether the book prepares students for the course assessment.
Workflow
- Inventory exams and exercise sets from the source root.
- Extract topics, formula patterns, and repeated problem types.
- Compare them to chapter learning objectives, exercises, and Chapter 26.
- Flag missing worked examples, weak exercise coverage, and overrepresented topics.
Output
exam_alignment_report.mdwith chapter-by-chapter findings.- Suggested new self-test or exam-preparation questions.
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.
- 3d ago First seen · 28 lines · 27 tokens per session scan A d6d3df3bbd6e
exam-alignment-reviewer is an agent published in the GitHub repository equinor/neqsim (150 stars, last pushed today), licensed Apache-2.0. It adds 27 tokens to every session and 166 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 2026-09-03.
Other agents, from other repositories
gate_runner_agent
Executes the Alignment Gate (1.5) and Quality Gate (3.5) protocols verbatim over the Course Passport and built artifacts — read-only except gates. fields.
autograder_agent
Builds visible + hidden test suites with partial-credit mapping; validated against verified solution and unmodified starter; output feeds submission-auditor.
solution_verifier_agent
Solves the lab cold from student-facing materials only, executing every step; produces verified solution, grading notes, and defect reports.
starter_code_agent
Builds scaffold repos that run as shipped: stubs with full contracts, fenced student zones, pinned dependencies, tested cold-start README.
blueprint_agent
Builds the test blueprint — content × Bloom matrix, point and time budgets — before any item exists.
Demonstrate
Agent for demonstrating VS Code features.