maestro/skills/math/math/information-theory/channel-capacity/SKILL.md
Problem-solving strategies for channel capacity in information theory
npx skillsauth add scooter-lacroix/maestro channel-capacityInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Use this skill when working on channel-capacity problems in information theory.
Mutual Information
scipy.stats.entropy(p) + scipy.stats.entropy(q) - joint_entropyChannel Model
Channel Capacity
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) |
Blahut-Arimoto Algorithm
z3_solve.py prove "capacity_upper_bound"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)"
uv run python -m runtime.harness scripts/sympy_compute.py simplify "1 + p*log(p, 2) + (1-p)*log(1-p, 2)"
uv run python -m runtime.harness scripts/z3_solve.py prove "I(X;Y) <= H(X)"
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