limits
npx machina-cli add skill parcadei/Continuous-Claude-v3/limits --openclawLimits
When to Use
Use this skill when working on limits problems in real analysis.
Decision Tree
-
Direct Substitution
- Try plugging in the value directly
- If you get a determinate form, that's the answer
-
Indeterminate Form? (0/0, inf/inf)
- Try algebraic manipulation (factor, rationalize)
- Try L'Hopital's rule:
sympy_compute.py diffon numerator/denominator
-
Squeeze Theorem
- If bounded: find g(x) <= f(x) <= h(x) where lim g = lim h
- Verify bounds with
z3_solve.py prove
-
Epsilon-Delta Proof
- For rigorous proof: set up |f(x) - L| < epsilon
- Find delta in terms of epsilon
- Verify with
math_scratchpad.py verify
Tool Commands
Sympy_Limit
uv run python -m runtime.harness scripts/sympy_compute.py limit "sin(x)/x" --var x --at 0
Sympy_Diff
uv run python -m runtime.harness scripts/sympy_compute.py diff "x**2" --var x
Z3_Prove
uv run python -m runtime.harness scripts/z3_solve.py prove "limit_bound" --vars x
Cognitive Tools Reference
See .claude/skills/math-mode/SKILL.md for full tool documentation.
Source
git clone https://github.com/parcadei/Continuous-Claude-v3/blob/main/.claude/skills/math/real-analysis/limits/SKILL.mdView on GitHub Overview
This skill provides practical strategies for solving limits in real analysis. It guides you through a four-step decision tree—direct substitution, indeterminate forms, the Squeeze Theorem, and epsilon-delta proofs—and shows how to verify results with symbolic tools.
How This Skill Works
Use a decision-tree workflow: start with direct substitution, then tackle 0/0 or inf/inf with algebraic manipulation or L'Hôpital's rule, apply the Squeeze Theorem when bounds exist, or construct an epsilon-delta proof for rigor. The workflow demonstrates tool-assisted verification using Sympy and Z3 as shown in the Skill.
When to Use It
- Direct substitution yields a determinate form
- Encounter 0/0 or inf/inf indeterminate forms requiring algebraic manipulation or L'Hôpital's rule
- You need to prove a limit using the Squeeze Theorem with known bounds
- You require a rigorous epsilon-delta proof of a limit
- You want to verify limit-related computations with symbolic tooling (Sympy or Z3)
Quick Start
- Step 1: Try direct substitution; if you get a determinate form, record the limit.
- Step 2: If you get an indeterminate form, apply algebraic simplification or L'Hôpital's rule (`sympy_compute.py diff` on numerator/denominator).
- Step 3: When a bound exists, use the Squeeze Theorem or craft an epsilon-delta proof and verify with the tool commands (Sympy_Limit, Sympy_Diff, Z3_Prove).
Best Practices
- Start with direct substitution and look for a determinate form
- If indeterminate, apply algebraic manipulation (factoring, rationalizing) before resorting to L'Hôpital
- Use L'Hôpital's rule only after simplifying and only on valid indeterminate forms
- Use the Squeeze Theorem when you can bound f(x) between g(x) and h(x) with known limits
- For rigor, craft an epsilon-delta proof and verify the delta-epsilon relationship with a tool like math_scratchpad.py verify
Example Use Cases
- lim_{x→0} sin(x)/x
- lim_{x→0} (1 - cos x)/x^2
- lim_{x→∞} (1 + 1/x)^x
- lim_{x→1} (x^2 - 1)/(x - 1) = 2
- Definition of derivative: lim_{x→a} (f(x) - f(a))/(x - a) = f'(a)