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.
npx skills add equinor/neqsim --skill paperlab_derivation_symbolic_checkinggit 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/skills/equinor/neqsim/paperlab_derivation_symbolic_checking)<a href="https://agentmods.dev/skills/equinor/neqsim/paperlab_derivation_symbolic_checking"><img src="https://agentmods.dev/badge/skills/equinor/neqsim/paperlab_derivation_symbolic_checking/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/skills/equinor/neqsim/paperlab_derivation_symbolic_checking"><img src="https://agentmods.dev/badge/skills/equinor/neqsim/paperlab_derivation_symbolic_checking.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00035 | $0.00345 |
| Opus 5 | $0.00017 | $0.00172 |
| Sonnet 5 | $0.00007 | $0.00069 |
| Haiku 4.5 | $0.00003 | $0.00034 |
Grade A, and why
paperlab_derivation_symbolic_checking 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.
What it actually says
PaperLab Derivation Symbolic Checking
When to Use
USE WHEN: a paper or book includes a new method, equation derivation, algorithm description, Jacobian, residual formulation, or thermodynamic identity.
Audit Dimensions
| Dimension | Questions |
|---|---|
| Assumptions | Are phase, EOS, ideality, and differentiability assumptions explicit? |
| Units | Are both sides dimensionally consistent? |
| Symbols | Are symbols defined once and reused consistently? |
| Limiting cases | Does the equation reduce correctly for simple or ideal cases? |
| Code linkage | Is there a Java method, notebook, or test implementing the equation? |
Output Schema
{
"equations": [
{
"label": "residual_jacobian",
"status": "needs_fix",
"issues": ["symbol J reused for flux and Jacobian"],
"limiting_cases_checked": ["ideal gas", "zero association"],
"implementation_link": "neqsim.package.Class#method"
}
]
}
Pass Criteria
- Each derivation states assumptions.
- Units and symbols are consistent.
- At least one limiting case or reference comparison is described.
- Implementation or test linkage exists for computational methods.
Safety Rules
- Do not present dimensional consistency as mathematical proof.
- If symbolic algebra cannot be run, state that the review is manual.
- Keep corrections minimal and tied to the manuscript text.
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 · 52 lines · 35 tokens per session scan A c66bcf8c4fa1
paperlab_derivation_symbolic_checking is a skill published in the GitHub repository equinor/neqsim (151 stars, last pushed today), licensed Apache-2.0. It adds 35 tokens to every session and 345 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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