channel-capacity
Problem-solving strategies for channel capacity in information theory
Best use case
channel-capacity is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Problem-solving strategies for channel capacity in information theory
Teams using channel-capacity should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/channel-capacity/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How channel-capacity Compares
| Feature / Agent | channel-capacity | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Problem-solving strategies for channel capacity in information theory
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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
```bash
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
```bash
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
```bash
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 & 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.
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