paperlab-equation-dimensional-audit

paperlab-equation-dimensional-audit is a skill for Claude Code, Codex from equinor/neqsim. It costs 46 tokens per session (442 once invoked), scanned A, a copy of paperlab_equation_dimensional_audit, Apache-2.0.

An equation review that checks symbols, units, dimensions, sources, assumptions, repeated notation, and links to computational implementations. Dimensional consistency means that both sides of a formula represent compatible physical quantities.

In plain words
What is it for?
Use it to audit engineering equations and correlations in textbooks, especially when formulas are tied to standards, derivations, or software methods.
Why use it?
It catches undefined symbols, impossible or unclear units, conflicting notation, unsupported formulas, and differences between equations used in separate chapters.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to audit engineering equations and correlations in textbooks, especially when formulas are tied to standards, derivations, or software methods.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/equinor/neqsim/paperlab-equation-dimensional-audit
Install

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.

Any agent
npx skills add equinor/neqsim --skill paperlab-equation-dimensional-audit
Clone the repo
git clone --depth 1 https://github.com/equinor/neqsim

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for paperlab-equation-dimensional-audit

README.md
[![agentmods](https://agentmods.dev/badge/skills/equinor/neqsim/paperlab-equation-dimensional-audit/github.svg)](https://agentmods.dev/skills/equinor/neqsim/paperlab-equation-dimensional-audit)
Your own site
<a href="https://agentmods.dev/skills/equinor/neqsim/paperlab-equation-dimensional-audit"><img src="https://agentmods.dev/badge/skills/equinor/neqsim/paperlab-equation-dimensional-audit/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.

agentmods 80×15 button for paperlab-equation-dimensional-audit

Your own site · 80×15
<a href="https://agentmods.dev/skills/equinor/neqsim/paperlab-equation-dimensional-audit"><img src="https://agentmods.dev/badge/skills/equinor/neqsim/paperlab-equation-dimensional-audit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 46 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 442 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 100% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00046 $0.00442
Opus 5.5 $0.00018 $0.00177
Sonnet 5.5 $0.00009 $0.00088
Haiku 4.5 $0.00005 $0.00044

Measured 27d ago against content hash b0182cf57e9b, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

paperlab-equation-dimensional-audit 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 27d 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.

Origin

This is a copy

100% identical to paperlab_equation_dimensional_audit — 8 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

neqsim-paperlab/skills/paperlab-equation-dimensional-audit/SKILL.md · 63 lines

What it actually says

PaperLab Equation Dimensional Audit

When to Use

USE WHEN: reviewing equations, formula notation, unit consistency, or repeated technical correlations in an engineering textbook.

Pair with:

  • paperlab-scientific-traceability-audit for equation provenance,
  • neqsim-in-writing for implementation links,
  • paperlab-standards-clause-traceability for standards-based formulas.

Audit Dimensions

Dimension Check
symbol defined near first use or in nomenclature
unit stated and dimensionally plausible
source citation, standard, derivation, or implementation link
assumption valid range or simplification stated
duplicate repeated formula uses same notation or explains differences
implementation NeqSim method/test link when used computationally

Workflow

  1. Extract display equations delimited by $$...$$ and inline formulas using $...$ when they contain engineering symbols.
  2. Normalize symbols and compare against nomenclature.yaml.
  3. Check units and dimensions where possible.
  4. Detect conflicting definitions such as q for heat duty and volumetric flow in the same chapter without clarification.
  5. Detect formula variants that need an assumptions note.
  6. Recommend nomenclature additions and local text fixes.

Output Pattern

## Equation Audit: <chapter>

- Status: ready | minor-revision | major-revision | blocked
- Undefined symbols:
  - <symbol and equation context>
- Unit issues:
  - <quantity and suspected issue>
- Formula variants:
  - <formula family and recommended distinction>
- Recommended next edit: <one concrete action>

Safety Rules

  • Do not declare an equation wrong from notation alone.
  • Treat approximate teaching equations as acceptable when assumptions are clear.
  • Never invent standards clauses or citations for equations.
Changes

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.

  1. 27d ago First seen · 63 lines · 46 tokens per session scan A b0182cf57e9b

Subscribe to this mod's changes

paperlab-equation-dimensional-audit is a skill published in the GitHub repository equinor/neqsim (156 stars, last pushed today), licensed Apache-2.0. It adds 46 tokens to every session and 442 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to paperlab_equation_dimensional_audit, differing in 8 lines, and is treated as a copy.

Related

Other skills, from other repositories

local-ai-agents

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…

microsoft/ai-agents-for-beginners · 200 tokens

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

chat-pet-sprite-creation

Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.

microsoft/vscode · 53 tokens

cpu-profile-analysis

Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…

microsoft/vscode · 71 tokens

insight-error-page

Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…

vercel/next.js · 83 tokens