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Problem-solving strategies for channel capacity in information theory

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continuous-claude-v3
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$ npx -y skills add parcadei/Continuous-Claude-v3 --skill channel-capacity --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/channel-capacity

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Problem-solving strategies for channel capacity in information theory

SKILL.md

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

Channel Capacity

When to Use

Use this skill when working on channel-capacity problems in information theory.

Decision Tree

1. **Mutual Information**

  • I(X;Y) = H(X) + H(Y) - H(X,Y)
  • I(X;Y) = H(X) - H(X|Y) = H(Y) - H(Y|X)
  • Symmetric: I(X;Y) = I(Y;X)
  • `scipy.stats.entropy(p) + scipy.stats.entropy(q) - joint_entropy`

2. **Channel Model**

  • Input X, output Y, channel P(Y|X)
  • Channel matrix: rows = inputs, columns = outputs
  • Element (i,j) = P(Y=j | X=i)

3. **Channel Capacity**

  • C = max_{p(x)} I(X;Y)
  • Maximize over input distribution
  • Achieved by capacity-achieving distribution

4. **Common Channels** | Channel | Capacity | |---------|----------| | Binary Symmetric (BSC) | 1 - H(p) where p = crossover prob | | Binary Erasure (BEC) | 1 - epsilon where epsilon = erasure prob | | AWGN | 0.5 * log2(1 + SNR) |

5. **Blahut-Arimoto Algorithm**

  • Iterative algorithm to compute capacity
  • Alternates between optimizing p(x) and p(y|x)
  • Converges to capacity
  • `z3_solve.py prove "capacity_upper_bound"`

Tool Commands

Scipy_Mutual_Info

uv run python -c "from scipy.stats import entropy; p = [0.5, 0.5]; q = [0.6, 0.4]; H_X = entropy(p, base=2); H_Y = entropy(q, base=2); print('H(X)=', H_X, 'H(Y)=', H_Y)"

Sympy_Bsc_Capacity

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

Z3_Capacity_Bound

uv run python -m runtime.harness scripts/z3_solve.py prove "I(X;Y) <= H(X)"

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. Using a randomly generated code, Shannon showed that one can send information at any rate below the capacity *C* of the channel with an arbitrarily low probability of error. The idea of a randomly generated code is very unusual.

Cognitive Tools Reference

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

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