Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add beita6969/ScienceClaw --skill quantum-computinggit clone --depth 1 https://github.com/beita6969/ScienceClawWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/beita6969/scienceclaw/quantum-computing)<a href="https://agentmods.dev/skills/beita6969/scienceclaw/quantum-computing"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/quantum-computing/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/beita6969/scienceclaw/quantum-computing"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/quantum-computing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00046 | $0.00747 |
| Opus 5 | $0.00023 | $0.00374 |
| Sonnet 5 | $0.00009 | $0.00149 |
| Haiku 4.5 | $0.00005 | $0.00075 |
Grade A, and why
quantum-computing scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 53 lines — stays where its author put it; the contents beside it link to each section on GitHub.
When to Trigger
Activate this skill when the user mentions:
- Quantum circuits, quantum gates (Hadamard, CNOT, Toffoli)
- Qubits, superposition, entanglement, measurement
- Quantum algorithms (Shor's, Grover's, VQE, QAOA)
- Quantum error correction, decoherence, noise models
- Quantum advantage, quantum supremacy, complexity classes (BQP)
- Quantum hardware (superconducting, trapped ion, photonic)
- Quantum simulation, quantum chemistry applications
Step-by-Step Methodology
- Problem formulation - Determine if the problem has a known quantum advantage. Map the problem to a quantum computing framework: gate-based, adiabatic, or measurement-based. Identify required qubit count and circuit depth.
- Algorithm selection - For search: Grover's (quadratic speedup). For factoring: Shor's. For optimization: QAOA or quantum annealing. For chemistry: VQE or QPE. For machine learning: quantum kernels or variational classifiers.
- Circuit design - Construct the quantum circuit using elementary gates (H, CNOT, Rz, Ry). Decompose multi-qubit gates into native gate sets. Minimize circuit depth and CNOT count for near-term hardware compatibility.
- Simulation - Simulate circuit on classical hardware using Qiskit Aer, Cirq, or PennyLane. For small systems (<30 qubits), use statevector simulation. For larger systems, use tensor network or MPS methods.
- Noise analysis - Model realistic noise: single-qubit and two-qubit gate errors, measurement errors, T1/T2 decoherence times. Use noise models from real hardware backends (IBM Quantum, IonQ).
- Error mitigation / correction - For near-term (NISQ): zero-noise extrapolation, probabilistic error cancellation, dynamical decoupling. For fault-tolerant: surface codes, repetition codes, logical qubit encoding.
- Results analysis - Compare quantum vs. classical performance. Report circuit metrics (depth, gate count, qubit count). Assess scalability and resource requirements for practical problem sizes.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 53 lines · 46 tokens per session scan A c52615b8a84f
quantum-computing is a skill published in the GitHub repository beita6969/ScienceClaw (898 stars, last pushed 3mo ago), licensed MIT. It adds 46 tokens to every session and 747 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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