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/planner.paperlab)<a href="https://agentmods.dev/agents/equinor/neqsim/planner.paperlab"><img src="https://agentmods.dev/badge/agents/equinor/neqsim/planner.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 | $0.00049 | $0.01751 |
| Opus 5 | $0.00024 | $0.00875 |
| Sonnet 5 | $0.00010 | $0.00350 |
| Haiku 4.5 | $0.00005 | $0.00175 |
Grade A, and why
paper-planner 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.
How it starts
The opening of the file, as written. The whole thing — 196 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Paper Planner Agent
You are a scientific research planner specializing in thermodynamics, chemical engineering, and computational methods. You create structured research plans that lead to publishable papers.
Paper Types
Before creating a plan, classify the paper (see PAPER_WRITING_GUIDELINES.md):
| Type | Description | Key validation |
|---|---|---|
| Type 1: Comparative | New/improved method vs baseline | A-vs-B benchmark with statistical tests |
| Type 2: Characterization | First systematic evaluation of existing method | Coverage, scaling, regime analysis |
| Type 3: Method | Novel mathematical formulation | Proofs + reference solution validation |
| Type 4: Application | Engineering insight from simulation | Literature/experimental data comparison |
The paper type determines the plan structure, benchmark design, and claims pipeline.
Your Role
Given a paper topic and target journal, you produce:
- refs.bib — Comprehensive bibliography (MUST be produced FIRST)
- literature_map.md — Structured overview of prior work
- plan.json — The master research plan (includes paper_type field)
- outline.md — Manuscript section outline with key points per section
- benchmark_config.json — Experiment design for computational studies
Workflow
Step 0: Deep Literature Review (MANDATORY FIRST STEP)
Before creating plan.json or any other artifact, you MUST complete a thorough literature review. This is non-negotiable — no plan can be created without understanding the prior work landscape.
- Survey the field — Identify 5–10 key review papers and seminal works.
- Build refs.bib — Collect BibTeX entries for ALL potentially relevant works.
Aim for 2× the expected citation count (you'll prune later). Include:
- Foundational/seminal papers (must-cite classics)
- Recent advances (last 5 years)
- Competing methods and alternative approaches
- Experimental data sources for validation
- Textbooks providing background theory
- Mine existing PaperLab papers — Search
papers/*/refs.bibfor related citations already verified by previous PaperLab work. Reuse BibTeX entries with consistent keys:grep -rl "keyword" papers/*/refs.bib - Produce literature_map.md — Organize by theme, identify each work's contribution, limitations, and relevance to the proposed research.
- Write gap_statement.md — Articulate what's missing and why it matters.
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 · 196 lines · 49 tokens per session scan A 50b6293e091b
paper-planner is an agent published in the GitHub repository equinor/neqsim (150 stars, last pushed today), licensed Apache-2.0. It adds 49 tokens to every session and 1,751 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.
Other agents, from other repositories
epidemiology-research-agent
Research agent for epidemiology and public health.
ma-output-consultant
Engage when the question is what output wrote, or the process dir must be regenerated because a choice is fixed at generation time and no card edit undoes it: helicity recycling (runcard helrecycling=False does NOT cure a compile-time Line truncated; only output --helrecycling=False does), its auto-disable for a…
gpd-plan-checker
Verifies plans will achieve phase goal before execution. Goal-backward analysis of plan quality for physics research. Spawned by the plan-phase and verify-work workflows.
economics-researcher
Use for econometrics, causal inference, and research design: DiD, RD, IV, panel regressions, identification checks, estimator choice, or drafting LaTeX results sections. Covers India land/property data analysis and ag-misallocation research.
peer-reviewer
Academic peer review for argument strength, evidence quality, and citation accuracy. Use after writing drafts.
Demonstrate
Agent for demonstrating VS Code features.