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Agent skill

Math Help

by parcadei in parcadei/Continuous-Claude-v3

Guide to the math cognitive stack - what tools exist and when to use each

MITAuto-check passedResearch & Science

Install Math Help

skills CLI
$ npx skills add parcadei/Continuous-Claude-v3 --skill math-help -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install parcadei/Continuous-Claude-v3 math-help --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/parcadei/Continuous-Claude-v3.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/math-help .claude/skills/math-help && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
math-help
GitHub stars
3.9k
Used in
1 other repo
Token cost
~2.6k tokens
SKILL.md length
484 words
Files
1
Skills in repo
141
Repo updated
First seen
Licence
MIT

At a glance

Guide to the math cognitive stack - what tools exist and when to use each

  • Works in 3 steps: Solve with sympy_compute.py → Verify solution with math_scratchpad.py → Plot to visualize (optional)
  • Tasks that involve Math and symbolic computation
  • SKILL.md covers Quick Reference, The Five Layers, Numerical Tools and Visualization, plus 5 more sections
  • Calls uv

What it does

Math Help is an agent skill from parcadei/Continuous-Claude-v3. Guide to the math cognitive stack - what tools exist and when to use each

Its SKILL.md is about 2.6k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Research & Science, covering Math and symbolic computation. It works with SymPy. The repository describes itself as: Context management for Claude Code. Hooks maintain state via ledgers and handoffs. MCP execution without context pollution. Agent orchestration with isolated context windows. The licence is MIT.

When your agent uses it

  • Tasks that involve Math and symbolic computation

Example prompts

  • “/math-help”

Requirements

  • Python 3

Workflow steps

3 steps, taken from the first numbered list in SKILL.md.

  1. Solve with sympy_compute.py
  2. Verify solution with math_scratchpad.py
  3. Plot to visualize (optional)

What it can do on your machine

Read from SKILL.md and the folder at commit d07ff4b. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • uv

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use uv, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Math Help loads about 2.6k tokens when it runs. Until then it costs about 21 tokens; SKILL.md has 484 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~21
When it runs · the whole SKILL.md, loaded when a task matches
~2.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from parcadei/Continuous-Claude-v3 at commit d07ff4b, republished under its MIT licence (© parcadei). 484 words, ~2,559 tokens.

Download SKILL.mdSave it as .claude/skills/math-help/SKILL.md (or your agent's skills folder).
name
math-help
description
Guide to the math cognitive stack - what tools exist and when to use each
triggers
help, guide, how do I, what math, math help, math tools, which tool, math tutorial
user-invocable
false

Math Cognitive Stack Guide

Cognitive prosthetics for exact mathematical computation. This guide helps you choose the right tool for your math task.

Quick Reference

I want to...Use thisExample
Solve equationssympy_compute.py solvesolve "x**2 - 4 = 0" --var x
Integrate/differentiatesympy_compute.pyintegrate "sin(x)" --var x
Compute limitssympy_compute.py limitlimit "sin(x)/x" --var x --to 0
Matrix operationssympy_compute.py / numpy_compute.pydet "[[1,2],[3,4]]"
Verify a reasoning stepmath_scratchpad.py verifyverify "x = 2 implies x^2 = 4"
Check a proof chainmath_scratchpad.py chainchain --steps '[...]'
Get progressive hintsmath_tutor.py hinthint "Solve x^2 - 4 = 0" --level 2
Generate practice problemsmath_tutor.py generategenerate --topic algebra --difficulty 2
Prove a theorem (constraints)z3_solve.py proveprove "x + y == y + x" --vars x y
Check satisfiabilityz3_solve.py satsat "x > 0, x < 10, x*x == 49"
Optimize with constraintsz3_solve.py optimizeoptimize "x + y" --constraints "..."
Plot 2D/3D functionsmath_plot.pyplot2d "sin(x)" --range -10 10
Arbitrary precisionmpmath_compute.pypi --dps 100
Numerical optimizationscipy_compute.pyminimize "x**2 + 2*x" "5"
Formal machine proofLean 4 (lean4 skill)/lean4

The Five Layers

Layer 1: SymPy (Symbolic Algebra)

When: Exact algebraic computation - solving, calculus, simplification, matrix algebra.

Key Commands:

bash
# Solve equation
uv run python -m runtime.harness scripts/sympy_compute.py \
    solve "x**2 - 5*x + 6 = 0" --var x --domain real

# Integrate
uv run python -m runtime.harness scripts/sympy_compute.py \
    integrate "sin(x)" --var x

# Definite integral
uv run python -m runtime.harness scripts/sympy_compute.py \
    integrate "x**2" --var x --bounds 0 1

# Differentiate (2nd order)
uv run python -m runtime.harness scripts/sympy_compute.py \
    diff "x**3" --var x --order 2

# Simplify (trig strategy)
uv run python -m runtime.harness scripts/sympy_compute.py \
    simplify "sin(x)**2 + cos(x)**2" --strategy trig

# Limit
uv run python -m runtime.harness scripts/sympy_compute.py \
    limit "sin(x)/x" --var x --to 0

# Matrix eigenvalues
uv run python -m runtime.harness scripts/sympy_compute.py \
    eigenvalues "[[1,2],[3,4]]"

Best For: Closed-form solutions, calculus, exact algebra.

Layer 2: Z3 (Constraint Solving & Theorem Proving)

When: Proving theorems, checking satisfiability, constraint optimization.

Key Commands:

bash
# Prove commutativity
uv run python -m runtime.harness scripts/cc_math/z3_solve.py \
    prove "x + y == y + x" --vars x y --type int

# Check satisfiability
uv run python -m runtime.harness scripts/cc_math/z3_solve.py \
    sat "x > 0, x < 10, x*x == 49" --type int

# Optimize
uv run python -m runtime.harness scripts/cc_math/z3_solve.py \
    optimize "x + y" --constraints "x >= 0, y >= 0, x + y <= 100" \
    --direction maximize --type real

Best For: Logical proofs, constraint satisfaction, optimization with constraints.

Layer 3: Math Scratchpad (Reasoning Verification)

When: Verifying step-by-step reasoning, checking derivation chains.

Key Commands:

bash
# Verify single step
uv run python -m runtime.harness scripts/cc_math/math_scratchpad.py \
    verify "x = 2 implies x^2 = 4"

# Verify with context
uv run python -m runtime.harness scripts/cc_math/math_scratchpad.py \
    verify "x^2 = 4" --context '{"x": 2}'

# Verify chain of reasoning
uv run python -m runtime.harness scripts/cc_math/math_scratchpad.py \
    chain --steps '["x^2 - 4 = 0", "(x-2)(x+2) = 0", "x = 2 or x = -2"]'

# Explain a step
uv run python -m runtime.harness scripts/cc_math/math_scratchpad.py \
    explain "d/dx(x^3) = 3*x^2"

Best For: Checking your work, validating derivations, step-by-step verification.

Layer 4: Math Tutor (Educational)

When: Learning, getting hints, generating practice problems.

Key Commands:

bash
# Step-by-step solution
uv run python scripts/cc_math/math_tutor.py steps "x**2 - 5*x + 6 = 0" --operation solve

# Progressive hint (level 1-5)
uv run python scripts/cc_math/math_tutor.py hint "Solve x**2 - 4 = 0" --level 2

# Generate practice problem
uv run python scripts/cc_math/math_tutor.py generate --topic algebra --difficulty 2

Best For: Learning, tutoring, practice.

Layer 5: Lean 4 (Formal Proofs)

When: Rigorous machine-verified mathematical proofs, category theory, type theory.

Access: Use /lean4 skill for full documentation.

Best For: Publication-grade proofs, dependent types, category theory.

Show full SKILL.md (192 more words)Show less

Numerical Tools

For numerical (not symbolic) computation:

NumPy (160 functions)
bash
# Matrix operations
uv run python scripts/cc_math/numpy_compute.py det "[[1,2],[3,4]]"
uv run python scripts/cc_math/numpy_compute.py inv "[[1,2],[3,4]]"
uv run python scripts/cc_math/numpy_compute.py eig "[[1,2],[3,4]]"
uv run python scripts/cc_math/numpy_compute.py svd "[[1,2,3],[4,5,6]]"

# Solve linear system
uv run python scripts/cc_math/numpy_compute.py solve "[[3,1],[1,2]]" "[9,8]"
SciPy (289 functions)
bash
# Minimize function
uv run python scripts/cc_math/scipy_compute.py minimize "x**2 + 2*x" "5"

# Find root
uv run python scripts/cc_math/scipy_compute.py root "x**3 - x - 2" "1.5"

# Curve fitting
uv run python scripts/cc_math/scipy_compute.py curve_fit "a*exp(-b*x)" "0,1,2,3" "1,0.6,0.4,0.2" "1,0.5"
mpmath (153 functions, arbitrary precision)
bash
# Pi to 100 decimal places
uv run python scripts/cc_math/mpmath_compute.py pi --dps 100

# Arbitrary precision sqrt
uv run python -m scripts.mpmath_compute mp_sqrt "2" --dps 100

Visualization

math_plot.py
bash
# 2D plot
uv run python scripts/cc_math/math_plot.py plot2d "sin(x)" \
    --var x --range -10 10 --output plot.png

# 3D surface
uv run python scripts/cc_math/math_plot.py plot3d "x**2 + y**2" \
    --xvar x --yvar y --range 5 --output surface.html

# Multiple functions
uv run python scripts/cc_math/math_plot.py plot2d-multi "sin(x),cos(x)" \
    --var x --range -6.28 6.28 --output multi.png

# LaTeX rendering
uv run python scripts/cc_math/math_plot.py latex "\\int e^{-x^2} dx" --output equation.png

Educational Features

5-Level Hint System
LevelCategoryWhat You Get
1ConceptualGeneral direction, topic identification
2StrategicApproach to use, technique selection
3TacticalSpecific steps, intermediate goals
4ComputationalIntermediate results, partial solutions
5AnswerFull solution with explanation

Usage:

bash
# Start with conceptual hint
uv run python scripts/cc_math/math_tutor.py hint "integrate x*sin(x)" --level 1

# Get more specific guidance
uv run python scripts/cc_math/math_tutor.py hint "integrate x*sin(x)" --level 3
Step-by-Step Solutions
bash
uv run python scripts/cc_math/math_tutor.py steps "x**2 - 5*x + 6 = 0" --operation solve

Returns structured steps with:

  • Step number and type
  • From/to expressions
  • Rule applied
  • Justification

Common Workflows

Workflow 1: Solve and Verify
  1. Solve with sympy_compute.py
  2. Verify solution with math_scratchpad.py
  3. Plot to visualize (optional)
bash
# Solve
uv run python -m runtime.harness scripts/sympy_compute.py \
    solve "x**2 - 4 = 0" --var x

# Verify the solutions work
uv run python -m runtime.harness scripts/cc_math/math_scratchpad.py \
    verify "x = 2 implies x^2 - 4 = 0"
Workflow 2: Learn a Concept
  1. Generate practice problem with math_tutor.py
  2. Use progressive hints (level 1, then 2, etc.)
  3. Get full solution if stuck
bash
# Generate problem
uv run python scripts/cc_math/math_tutor.py generate --topic calculus --difficulty 2

# Get hints progressively
uv run python scripts/cc_math/math_tutor.py hint "..." --level 1
uv run python scripts/cc_math/math_tutor.py hint "..." --level 2

# Full solution
uv run python scripts/cc_math/math_tutor.py steps "..." --operation integrate
Workflow 3: Prove and Formalize
  1. Check theorem with z3_solve.py (constraint-level proof)
  2. If rigorous proof needed, use Lean 4
bash
# Quick check with Z3
uv run python -m runtime.harness scripts/cc_math/z3_solve.py \
    prove "x*y == y*x" --vars x y --type int

# For formal proof, use /lean4 skill

Choosing the Right Tool

Is it SYMBOLIC (exact answers)?
  └─ Yes → Use SymPy
      ├─ Equations → sympy_compute.py solve
      ├─ Calculus → sympy_compute.py integrate/diff/limit
      └─ Simplify → sympy_compute.py simplify

Is it a PROOF or CONSTRAINT problem?
  └─ Yes → Use Z3
      ├─ True/False theorem → z3_solve.py prove
      ├─ Find values → z3_solve.py sat
      └─ Optimize → z3_solve.py optimize

Is it NUMERICAL (approximate answers)?
  └─ Yes → Use NumPy/SciPy
      ├─ Linear algebra → numpy_compute.py
      ├─ Optimization → scipy_compute.py minimize
      └─ High precision → mpmath_compute.py

Need to VERIFY reasoning?
  └─ Yes → Use Math Scratchpad
      ├─ Single step → math_scratchpad.py verify
      └─ Chain → math_scratchpad.py chain

Want to LEARN/PRACTICE?
  └─ Yes → Use Math Tutor
      ├─ Hints → math_tutor.py hint
      └─ Practice → math_tutor.py generate

Need MACHINE-VERIFIED formal proof?
  └─ Yes → Use Lean 4 (see /lean4 skill)
  • /math or /math-mode - Quick access to the orchestration skill
  • /lean4 - Formal theorem proving with Lean 4
  • /lean4-functors - Category theory functors
  • /lean4-nat-trans - Natural transformations
  • /lean4-limits - Limits and colimits

Requirements

All math scripts are installed via:

bash
uv sync

Dependencies: sympy, z3-solver, numpy, scipy, mpmath, matplotlib, plotly

© parcadei, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .claude/skills/math-help of parcadei/Continuous-Claude-v3.

Open the folder on GitHubat commit d07ff4b

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in parcadei/Continuous-Claude-v3, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Math Help next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Math Help this skillparcadei/Continuous-Claude-v33.9k1 repos~2.6kAutomated safety check: PassMIT
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Edu Solid Geometrywy51ai/edulab1.4k1 repos~1.1kAutomated safety check: PassApache-2.0
Math Toolsananddtyagi/cc-marketplace6872 repos~1.3kAutomated safety check: PassNone
Edu Chem Reactionwy51ai/edulab1.4k—~1.2kAutomated safety check: PassApache-2.0

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Works with

Categories

Questions about Math Help

What does Math Help do?

Guide to the math cognitive stack - what tools exist and when to use each. Math Help is an agent skill from parcadei/Continuous-Claude-v3.

When should I use Math Help?

Math Help fits situations like: tasks that involve Math and symbolic computation.

How do I install Math Help in Claude Code?

Run `npx skills add parcadei/Continuous-Claude-v3 --skill math-help -a claude-code`. Or copy the skill folder (.claude/skills/math-help in parcadei/Continuous-Claude-v3) into .claude/skills/math-help in your project. Claude Code loads it when a task matches its description.

How do I install Math Help in Codex?

Run `npx skills add parcadei/Continuous-Claude-v3 --skill math-help -a codex`. Or copy the skill folder (.claude/skills/math-help in parcadei/Continuous-Claude-v3) into .agents/skills/math-help in your project. Codex loads it when a task matches its description.

Can I use Math Help in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add parcadei/Continuous-Claude-v3 --skill math-help -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/math-help, .gemini/skills/math-help, .github/skills/math-help and .opencode/skills/math-help in your project.

What does Math Help need to run?

Going by SKILL.md and its folder, Math Help needs the command-line tools its instructions call (uv). Our summary lists: Python 3.

Does Math Help access the network?

SKILL.md contains no URLs. Its commands use uv, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Math Help safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Math Help use?

Math Help is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Math Help use?

About 2.6k tokens (SKILL.md is roughly 10k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Math Help?

Skills that share tags, products or a category with Math Help: Sympy (zLanqing/codex-claude-academic-skills, 4.6k stars), Edu Analytic Geometry (wy51ai/edulab, 1.4k stars), Edu Solid Geometry (wy51ai/edulab, 1.4k stars) and Math Tools (ananddtyagi/cc-marketplace, 687 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Math Help?

parcadei (a GitHub user) maintains it in parcadei/Continuous-Claude-v3, which has 3,940 GitHub stars. The repository holds 141 skills in this directory. The repository was last updated on January 26, 2026.

Source: parcadei/Continuous-Claude-v3 on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.