Cognitive prosthetics for exact mathematical computation. This guide helps you choose the right tool for your math task.
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AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionmath-helpExecute the skills CLI command in your project's root directory to begin installation:
Fetches math-help from parcadei/continuous-claude-v3 and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate math-help. Access via /math-help in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
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Cognitive prosthetics for exact mathematical computation. This guide helps you choose the right tool for your math task.
| I want to... | Use this | Example |
|---|---|---|
| Solve equations | sympy_compute.py solve | solve "x**2 - 4 = 0" --var x |
| Integrate/differentiate | sympy_compute.py | integrate "sin(x)" --var x |
| Compute limits | sympy_compute.py limit | limit "sin(x)/x" --var x --to 0 |
| Matrix operations | sympy_compute.py / numpy_compute.py | det "[[1,2],[3,4]]" |
| Verify a reasoning step | math_scratchpad.py verify | verify "x = 2 implies x^2 = 4" |
| Check a proof chain | math_scratchpad.py chain | chain --steps '[...]' |
| Get progressive hints | math_tutor.py hint | hint "Solve x^2 - 4 = 0" --level 2 |
| Generate practice problems | math_tutor.py generate | generate --topic algebra --difficulty 2 |
| Prove a theorem (constraints) | z3_solve.py prove | prove "x + y == y + x" --vars x y |
| Check satisfiability | z3_solve.py sat | sat "x > 0, x < 10, x*x == 49" |
| Optimize with constraints | z3_solve.py optimize | optimize "x + y" --constraints "..." |
| Plot 2D/3D functions | math_plot.py | plot2d "sin(x)" --range -10 10 |
| Arbitrary precision | mpmath_compute.py | pi --dps 100 |
| Numerical optimization | scipy_compute.py | minimize "x**2 + 2*x" "5" |
| Formal machine proof | Lean 4 (lean4 skill) | /lean4 |
When: Exact algebraic computation - solving, calculus, simplification, matrix algebra.
Key Commands:
# 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.
When: Proving theorems, checking satisfiability, constraint optimization.
Key Commands:
# 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.
When: Verifying step-by-step reasoning, checking derivation chains.
Key Commands:
# 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.
When: Learning, getting hints, generating practice problems.
Key Commands:
# 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.
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.
For numerical (not symbolic) computation:
# 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]"
# 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"
# 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
# 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
| Level | Category | What You Get |
|---|---|---|
| 1 | Conceptual | General direction, topic identification |
| 2 | Strategic | Approach to use, technique selection |
| 3 | Tactical | Specific steps, intermediate goals |
| 4 | Computational | Intermediate results, partial solutions |
| 5 | Answer | Full solution with explanation |
Usage:
# 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
uv run python scripts/cc_math/math_tutor.py steps "x**2 - 5*x + 6 = 0" --operation solve
Returns structured steps with:
# 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"
# 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
# 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
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-VEMake data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
parcadei/continuous-claude-v3
mattpocock/skills
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
ailabs-393/ai-labs-claude-skills
I recommend math-help for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
math-help fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Solid pick for teams standardizing on skills: math-help is focused, and the summary matches what you get after install.
Registry listing for math-help matched our evaluation — installs cleanly and behaves as described in the markdown.
math-help has been reliable in day-to-day use. Documentation quality is above average for community skills.
math-help reduced setup friction for our internal harness; good balance of opinion and flexibility.
We added math-help from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Keeps context tight: math-help is the kind of skill you can hand to a new teammate without a long onboarding doc.
Solid pick for teams standardizing on skills: math-help is focused, and the summary matches what you get after install.
math-help is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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