paperlab-exercise-difficulty-ramp

paperlab-exercise-difficulty-ramp is a skill for Claude Code, Codex from equinor/neqsim. It costs 36 tokens per session (468 once invoked), scanned A, a copy of paperlab_exercise_difficulty_ramp, Apache-2.0.

An exercise-design review that arranges questions from basic explanations and calculations through interpretation, design choices, uncertainty, and capstone work.

In plain words
What is it for?
Use it to review or create textbook exercises, classify their difficulty, cover conceptual and quantitative skills, and prepare students for larger integrated assignments.
Why use it?
It shows whether exercises match learning objectives and give students a sensible increase in difficulty instead of jumping straight to complex tasks.

Skill for Claude CodeCodex

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

Good fit Use it to review or create textbook exercises, classify their difficulty, cover conceptual and quantitative skills, and prepare students for larger integrated assignments.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/equinor/neqsim/paperlab-exercise-difficulty-ramp
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-exercise-difficulty-ramp
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-exercise-difficulty-ramp

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/equinor/neqsim/paperlab-exercise-difficulty-ramp"><img src="https://agentmods.dev/badge/skills/equinor/neqsim/paperlab-exercise-difficulty-ramp.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 36 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 468 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 $0.00036 $0.00468
Opus 5.5 $0.00014 $0.00187
Sonnet 5.5 $0.00007 $0.00094
Haiku 4.5 $0.00004 $0.00047

Measured 27d ago against content hash ca22b2645af1, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

paperlab-exercise-difficulty-ramp 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_exercise_difficulty_ramp — 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-exercise-difficulty-ramp/SKILL.md · 65 lines

What it actually says

PaperLab Exercise Difficulty Ramp

When to Use

USE WHEN: preparing a book for teaching, revising exercises, or checking that learning objectives are assessed with an appropriate difficulty progression.

Pair with:

  • paperlab-learning-objective-matrix for objective coverage,
  • paperlab-exam-alignment for assessment fit,
  • paperlab-student-readability for student-facing wording.

Exercise Types

Type Student Action
concept-check explain, define, compare
calculation compute a value with stated inputs
interpretation explain what a result means physically
sensitivity vary one or more inputs and compare outputs
design-decision choose between alternatives using constraints
uncertainty-risk use P10/P50/P90, risk matrix, or scenario range
capstone integrate multiple chapters or recurring cases

Difficulty Levels

  • L1: recall or identify.
  • L2: explain or classify.
  • L3: calculate with provided method.
  • L4: diagnose, compare, or justify.
  • L5: design, optimize, or evaluate uncertainty.

Pass Criteria

  • Each chapter has at least one concept-check exercise.
  • Quantitative chapters have at least one calculation or notebook-linked task.
  • Design chapters include at least one decision exercise.
  • Late-book chapters include integration or capstone tasks.
  • Objectives with verbs such as calculate, design, evaluate, or diagnose are assessed by matching exercise types.

Output Schema

{
  "chapter": "ch23_co2_field_development",
  "exercise_mix": {"concept-check": 2, "calculation": 1, "design-decision": 2},
  "missing_types": ["uncertainty-risk"],
  "recommended_exercise": "Estimate dense-phase transport margin under low-temperature uncertainty."
}

Safety Rules

  • Do not require private data for public exercises.
  • Keep solutions and grading rubrics separate from student-facing chapters.
  • Match difficulty to prerequisite coverage.
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 · 65 lines · 36 tokens per session scan A ca22b2645af1

Subscribe to this mod's changes

paperlab-exercise-difficulty-ramp is a skill published in the GitHub repository equinor/neqsim (156 stars, last pushed today), licensed Apache-2.0. It adds 36 tokens to every session and 468 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to paperlab_exercise_difficulty_ramp, 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