pymatching-decoder
Minimum-weight perfect matching decoder skill for surface code error correction
Profile →npx skills add a5c-ai/babysitter --skill pymatching-decoder --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# PyMatching Decoder ## Purpose Provides expert guidance on minimum-weight perfect matching decoding for surface codes and other topological quantum error correction codes. ## Capabilities - MWPM decoding for surface codes - Weighted edge matching - Detector error model processing - Logical error rate calculation - Integration with Stim simulations - Custom graph construction - Belief propagation integration - Parallelized decoding ## Usage Guidelines 1. **Graph Construction**: Build matching graph from detector error model 2. **Weight Assignment**: Configure edge weights based on error probabilities 3. **Decoding Execution**: Run MWPM algorithm on syndrome data 4. **Error Analysis**: Calculate logical error rates from decoding results 5. **Optimization**: Tune decoder parameters for specific code structures ## Tools/Libraries - PyMatching - NetworkX - Stim - NumPy
- Purpose
- Capabilities
- Usage Guidelines
- Tools/Libraries
What does the pymatching-decoder skill do?
Minimum-weight perfect matching decoder skill for surface code error correction
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill pymatching-decoder --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From a5c-ai/babysitter, a repository with 1,642 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.