necatiincekara/review-circuit
.agents/skills/review-circuit/SKILL.md
Review a quantum circuit or hybrid QML block with repository-aware best practices for expressivity, gradient flow, and training stability.
testing
Updated Apr 16, 2026
$ install --global
skillsauthnpx skillsauth add necatiincekara/Quanvolutional-Neural-Network review-circuitInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
Security Scan Results
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
3
Scanners Passed
9
Scanners in report
Clean
TrivyContainer and dependency vulnerability scanner
95%
Clean
SemgrepStatic code analysis for vulnerabilities
95%
Clean
mcp-scan (Snyk)Model Context Protocol security validation
95%
Skipped
Snyk (dep)Open source security scanning
50%
Skipped
Socket.devSupply chain security analysis
50%
Skipped
VirusTotalMulti-engine malware detection
50%
Skipped
CrowdStrikeAdvanced threat intelligence
50%
Skipped
OSV-ScannerOpen Source Vulnerability database check
50%
Skipped
OWASP Dep-Check
50%
Last scanned: Apr 16, 2026, 3:32 PM80.0s1 file scanned
SKILL.md
- name:
- review-circuit
- description:
- Review a quantum circuit or hybrid QML block with repository-aware best practices for expressivity, gradient flow, and training stability.
Review Circuit
Use this skill for quantum circuit review, not for generic style feedback.
Workflow
- Identify the target:
src/model.pysrc/trainable_quantum_model.pyimproved_quantum_circuit.py- another user-specified file
- Read the local implementation and surrounding training code.
- Spawn the
quantum_ml_reviewercustom agent when the review is substantial. - Focus on:
- ansatz suitability
- entanglement pattern
- differentiability method
- barren plateau risk
- runtime vs expressivity tradeoff
- correctness of the classical-quantum interface
- Return findings first, then concrete improvement options.
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