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 xjtulyc/awesome-rosetta-skills --skill qiskit-quantumgit clone --depth 1 https://github.com/xjtulyc/awesome-rosetta-skillsWrote 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/xjtulyc/awesome-rosetta-skills/qiskit-quantum)<a href="https://agentmods.dev/skills/xjtulyc/awesome-rosetta-skills/qiskit-quantum"><img src="https://agentmods.dev/badge/skills/xjtulyc/awesome-rosetta-skills/qiskit-quantum/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/xjtulyc/awesome-rosetta-skills/qiskit-quantum"><img src="https://agentmods.dev/badge/skills/xjtulyc/awesome-rosetta-skills/qiskit-quantum.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.05591 |
| Opus 5.5 | $0.00018 | $0.02236 |
| Sonnet 5.5 | $0.00009 | $0.01118 |
| Haiku 4.5 | $0.00005 | $0.00559 |
Grade A, and why
qiskit-quantum 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 1mo 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.
The source is not reproduced here
A licence we could not identify
The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.
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.
- 1mo ago First seen · 625 lines · 46 tokens per session scan A dfed944c66ac
qiskit-quantum is a skill published in the GitHub repository xjtulyc/awesome-rosetta-skills (34 stars, last pushed 6mo ago), with no licence file. It adds 46 tokens to every session and 5,591 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-08-30.
Other skills, from other repositories
cudaq-importing
Use when porting circuits from another framework (e.g. Qiskit) into CUDA-Q kernels while preserving the source algorithm and validation fidelity.
quantum-algorithms-expert
Develop production-ready quantum algorithms for optimization, simulation, and machine learning on near-term quantum devices. Use when the user mentions quantum computing, Qiskit or Cirq, quantum circuits, QAOA, VQE, Grover or Shor, or quantum machine learning on NISQ devices.
cudaq-importing
Use when porting circuits from another framework (e.g. Qiskit) into CUDA-Q kernels while preserving the source algorithm and validation fidelity.
jupyter-notebook
Iterative Python via live Jupyter kernel (hamelnb).
matlab
Builds, reviews, migrates, and plans MATLAB or GNU Octave numerical workflows. Use for arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.
pennylane
Builds and differentiates PennyLane quantum circuits, hybrid PyTorch or JAX models, molecular VQE and QAOA workflows. Use for variational quantum algorithms, quantum machine learning, simulator validation, and moving validated circuits to provider plugins. For hardware-specific compilation use qiskit or cirq; for…