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 dralkh/seerai --skill cirqgit clone --depth 1 https://github.com/dralkh/seeraiWrote 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/dralkh/seerai/cirq)<a href="https://agentmods.dev/skills/dralkh/seerai/cirq"><img src="https://agentmods.dev/badge/skills/dralkh/seerai/cirq/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/dralkh/seerai/cirq"><img src="https://agentmods.dev/badge/skills/dralkh/seerai/cirq.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 | $0.00067 | $0.02793 |
| Opus 5 | $0.00034 | $0.01396 |
| Sonnet 5 | $0.00013 | $0.00559 |
| Haiku 4.5 | $0.00007 | $0.00279 |
Grade A, and why
cirq 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.
This is a copy
100% identical to cirq — 20 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 369 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cirq - Quantum Computing with Python
Cirq is Google Quantum AI's open-source framework for designing, simulating, and running quantum circuits on quantum computers and simulators.
When to Use This Skill
Use this skill when:
- Building, simulating, or optimizing NISQ circuits in Python
- Running jobs on Google Quantum AI processors (via
cirq-google) or partner backends (IonQ, Azure Quantum, AQT, Pasqal) - Modeling noise, compiling to hardware gatesets, or designing characterization experiments
- Using parameter sweeps, transformers, or the ReCirq experiment patterns
For IBM hardware use qiskit; for quantum ML with autodiff use pennylane; for physics simulations use qutip.
Installation
Requires Python 3.11+. Current stable release: 1.6.1 (August 2025). Vendor packages share the same version number.
uv pip install "cirq==1.6.1"
For hardware integration (pin matching versions for reproducibility):
# Google Quantum Engine (requires approved GCP project access)
uv pip install "cirq-google==1.6.1"
# IonQ
uv pip install "cirq-ionq==1.6.1"
# AQT (Alpine Quantum Technologies)
uv pip install "cirq-aqt==1.6.1"
# Pasqal
uv pip install "cirq-pasqal==1.6.1"
# Azure Quantum (IonQ, Honeywell/Quantinuum backends)
uv pip install "azure-quantum[cirq]"
For latest features during development, omit version pins; for production or hardware runs, pin all packages to the same Cirq release.
Quick Start
Basic Circuit
import cirq
import numpy as np
# Create qubits
q0, q1 = cirq.LineQubit.range(2)
# Build circuit
circuit = cirq.Circuit(
cirq.H(q0), # Hadamard on q0
cirq.CNOT(q0, q1), # CNOT with q0 control, q1 target
cirq.measure(q0, q1, key='result')
)
print(circuit)
# Simulate
simulator = cirq.Simulator()
result = simulator.run(circuit, repetitions=1000)
# Display results
print(result.histogram(key='result'))
Parameterized Circuit
import sympy
# Define symbolic parameter
theta = sympy.Symbol('theta')
# Create parameterized circuit
circuit = cirq.Circuit(
cirq.ry(theta)(q0),
cirq.measure(q0, key='m')
)
# Sweep over parameter values
sweep = cirq.Linspace('theta', start=0, stop=2*np.pi, length=20)
results = simulator.run_sweep(circuit, params=sweep, repetitions=1000)
# Process results
for params, result in zip(sweep, results):
theta_val = params['theta']
counts = result.histogram(key='m')
print(f"θ={theta_val:.2f}: {counts}")
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 369 lines · 67 tokens per session scan A 60bdbfe5cb3b
cirq is a skill published in the GitHub repository dralkh/seerai (76 stars, last pushed 2mo ago), licensed MIT. It adds 67 tokens to every session and 2,793 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to cirq, differing in 20 lines, and is treated as a copy.
Other skills, from other repositories
biopython
Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use…
python-math
Small Python utilities for math and text files.
paper2code
Converts an arxiv paper into a minimal, citation-anchored Python implementation. Trigger when user runs /paper2code with an arxiv URL or paper ID, says "implement this paper", or pastes an arxiv link asking for implementation. Flags all ambiguities honestly. Never invents implementation details not stated in the paper.
optimize-for-gpu
GPU-accelerate Python code using CuPy, Numba CUDA, Warp, cuDF, cuML, cuGraph, KvikIO, cuCIM, cuxfilter, cuVS, cuSpatial, and RAFT. Use whenever the user mentions GPU/CUDA/NVIDIA acceleration, or wants to speed up NumPy, pandas, scikit-learn, scikit-image, NetworkX, GeoPandas, or Faiss workloads. Covers physics…
bio-alignment-io
Read, write, and convert multiple sequence alignment files using Biopython Bio.AlignIO. Supports Clustal, PHYLIP, Stockholm, FASTA, Nexus, and other alignment formats for phylogenetics and conservation analysis. Use when reading, writing, or converting alignment file formats.
biopython
Biopython guidance for sequence I/O, alignments, Bio.PDB, and Entrez parsing. Use for custom bioinformatics code via agentgeneratedcode. Prefer VenusFactory download tools for NCBI/UniProt/AlphaFold bulk fetches so large payloads stay on disk. Do NOT use as a substitute for proteinsequencesimilaritysearch or…