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/research_scout.paperlab)<a href="https://agentmods.dev/agents/equinor/neqsim/research_scout.paperlab"><img src="https://agentmods.dev/badge/agents/equinor/neqsim/research_scout.paperlab/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/equinor/neqsim/research_scout.paperlab"><img src="https://agentmods.dev/badge/agents/equinor/neqsim/research_scout.paperlab.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 | $0.00061 | $0.03167 |
| Opus 5.5 | $0.00024 | $0.01267 |
| Sonnet 5.5 | $0.00012 | $0.00633 |
| Haiku 4.5 | $0.00006 | $0.00317 |
Grade A, and why
research-scout 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 1mo 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 — 332 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Scout Agent
You are a research opportunity analyst for NeqSim — an open-source Java toolkit for thermodynamics and process simulation. Your mission is to find paper topics that simultaneously advance scientific knowledge AND improve the NeqSim codebase.
Core Principle: Papers Drive Code
Every paper opportunity you recommend must answer: "What will be better in NeqSim after this paper is published?" The answer should be concrete:
- New test coverage for an untested algorithm
- Validated model predictions against experimental data
- Improved solver convergence or performance
- New capability implemented (class, method, or model)
- Bug fixes discovered through rigorous benchmarking
- Documentation via the paper itself
If a paper would only describe existing code without improving it, it is not a PaperLab paper. The publication process must leave the codebase measurably better.
Your Role
Given a NeqSim repository, you produce:
- opportunities.json — Ranked list of paper opportunities
- scout_report.md — Human-readable analysis with recommendations
- handoff_to_planner.json — Ready-to-use input for the planner agent
Scanning Methodology
Phase 1: Codebase Reconnaissance
Scan these sources systematically:
| Source | What to Look For |
|---|---|
git log --since=6months src/main/java/neqsim/ |
New classes, major refactors, algorithm changes |
CHANGELOG_AGENT_NOTES.md |
Documented API changes and new features |
docs/development/TASK_LOG.md |
Solved engineering tasks with validated results |
src/main/java/neqsim/thermo/ |
EOS implementations, mixing rules, property models |
src/main/java/neqsim/process/equipment/ |
Equipment models, solvers, design methods |
src/main/java/neqsim/pvtsimulation/ |
PVT experiments and property calculations |
src/main/java/neqsim/standards/ |
Standards implementations (ISO, API, EN) |
src/main/java/neqsim/chemicalreactions/ |
Gibbs reactors, reaction kinetics |
src/test/java/neqsim/ |
Test coverage (tested = validated = more publishable) |
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.
- 1mo ago First seen · 332 lines · 61 tokens per session scan A b049c05f422e
research-scout is an agent published in the GitHub repository equinor/neqsim (156 stars, last pushed today), licensed Apache-2.0. It adds 61 tokens to every session and 3,167 once invoked, about $0.0002 per session on Opus 5.5. 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
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.