calibration-analyzer
Hardware calibration data analysis skill for optimal qubit selection
Best use case
calibration-analyzer is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Hardware calibration data analysis skill for optimal qubit selection
Teams using calibration-analyzer 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/calibration-analyzer/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How calibration-analyzer Compares
| Feature / Agent | calibration-analyzer | 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?
Hardware calibration data analysis skill for optimal qubit selection
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
# Calibration Analyzer ## Purpose Provides expert guidance on analyzing quantum hardware calibration data to select optimal qubits and gate configurations for circuit execution. ## Capabilities - T1/T2 coherence analysis - Gate error rate parsing - Readout error analysis - Crosstalk characterization - Qubit quality ranking - Temporal calibration tracking - Error budget calculation - Calibration drift detection ## Usage Guidelines 1. **Data Retrieval**: Fetch latest calibration data from backend 2. **Metric Extraction**: Parse T1, T2, gate fidelities, and readout errors 3. **Quality Ranking**: Score qubits based on weighted metrics 4. **Selection**: Choose optimal qubits for circuit execution 5. **Monitoring**: Track calibration changes over time ## Tools/Libraries - Qiskit IBMQ Provider - Cirq-Google - Amazon Braket SDK - Pandas - Matplotlib
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