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/entropy

Problem-solving strategies for entropy in information theory

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continuous-claude-v3
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$ npx -y skills add parcadei/Continuous-Claude-v3 --skill entropy --agent claude-code

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  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/entropy

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Problem-solving strategies for entropy in information theory

SKILL.md

entropy.SKILL.md
name: entropy
description: "Problem-solving strategies for entropy in information theory"
allowed-tools: [Bash, Read]

Entropy

When to Use

Use this skill when working on entropy problems in information theory.

Decision Tree

1. **Shannon Entropy**

  • H(X) = -sum p(x) log2 p(x)
  • Maximum for uniform distribution: H_max = log2(n)
  • Minimum = 0 for deterministic (one outcome certain)
  • `scipy.stats.entropy(p, base=2)` for discrete

2. **Entropy Properties**

  • Non-negative: H(X) >= 0
  • Concave in p
  • Chain rule: H(X,Y) = H(X) + H(Y|X)
  • `z3_solve.py prove "entropy_nonnegative"`

3. **Joint and Conditional Entropy**

  • H(X,Y) = -sum sum p(x,y) log2 p(x,y)
  • H(Y|X) = H(X,Y) - H(X)
  • H(Y|X) <= H(Y) with equality iff independent

4. **Differential Entropy (Continuous)**

  • h(X) = -integral f(x) log f(x) dx
  • Can be negative!
  • Gaussian: h(X) = 0.5 * log2(2*pi*e*sigma^2)
  • `sympy_compute.py integrate "-f(x)*log(f(x))" --var x`

5. **Maximum Entropy Principle**

  • Given constraints, max entropy distribution is least biased
  • Uniform for no constraints
  • Exponential for E[X] = mu constraint
  • Gaussian for E[X], Var[X] constraints

Tool Commands

Scipy_Entropy

uv run python -c "from scipy.stats import entropy; p = [0.25, 0.25, 0.25, 0.25]; H = entropy(p, base=2); print('Entropy:', H, 'bits')"

Scipy_Kl_Div

uv run python -c "from scipy.stats import entropy; p = [0.5, 0.5]; q = [0.9, 0.1]; kl = entropy(p, q); print('KL divergence:', kl)"

Sympy_Entropy

uv run python -m runtime.harness scripts/sympy_compute.py simplify "-p*log(p, 2) - (1-p)*log(1-p, 2)"

Key Techniques

*From indexed textbooks:*

  • [Elements of Information Theory] Elements of Information Theory -- Thomas M_ Cover &amp; Joy A_ Thomas -- 2_, Auflage, New York, NY, 2012 -- Wiley-Interscience -- 9780470303153 -- 2fcfe3e8a16b3aeefeaf9429fcf9a513 -- Anna’s Archive. What is the channel capacity of this channel? This is the multiple\-access channel solved by Liao and Ahlswede.

Cognitive Tools Reference

See `.claude/skills/math-mode/SKILL.md` for full tool documentation.

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