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 qiskitgit 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/qiskit)<a href="https://agentmods.dev/skills/dralkh/seerai/qiskit"><img src="https://agentmods.dev/badge/skills/dralkh/seerai/qiskit/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/qiskit"><img src="https://agentmods.dev/badge/skills/dralkh/seerai/qiskit.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 | $0.00073 | $0.02051 |
| Opus 5.5 | $0.00029 | $0.00820 |
| Sonnet 5.5 | $0.00015 | $0.00410 |
| Haiku 4.5 | $0.00007 | $0.00205 |
Grade A, and why
qiskit 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.
This is a copy
97% identical to qiskit — 3 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 — 273 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Qiskit
Overview
Qiskit is the world's most popular open-source quantum computing framework with 13M+ downloads. Build quantum circuits, optimize for hardware, execute on simulators or real quantum computers, and analyze results. Supports IBM Quantum (100+ qubit systems), IonQ, Amazon Braket, and other providers.
Key Features:
- 83x faster transpilation than competitors
- 29% fewer two-qubit gates in optimized circuits
- Backend-agnostic execution (local simulators or cloud hardware)
- Comprehensive algorithm libraries for optimization, chemistry, and ML
Quick Start
Installation
uv pip install qiskit
uv pip install "qiskit[visualization]" matplotlib
First Circuit
from qiskit import QuantumCircuit
from qiskit.primitives import StatevectorSampler
# Create Bell state (entangled qubits)
qc = QuantumCircuit(2)
qc.h(0) # Hadamard on qubit 0
qc.cx(0, 1) # CNOT from qubit 0 to 1
qc.measure_all() # Measure both qubits
# Run locally
sampler = StatevectorSampler()
result = sampler.run([qc], shots=1024).result()
counts = result[0].data.meas.get_counts()
print(counts) # {'00': ~512, '11': ~512}
Visualization
from qiskit.visualization import plot_histogram
qc.draw('mpl') # Circuit diagram
plot_histogram(counts) # Results histogram
Core Capabilities
1. Setup and Installation
For detailed installation, authentication, and IBM Quantum account setup:
- See
references/setup.md
Topics covered:
- Installation with uv
- Python environment setup
- IBM Quantum account and API token configuration
- Local vs. cloud execution
2. Building Quantum Circuits
For constructing quantum circuits with gates, measurements, and composition:
- See
references/circuits.md
Topics covered:
- Creating circuits with QuantumCircuit
- Single-qubit gates (H, X, Y, Z, rotations, phase gates)
- Multi-qubit gates (CNOT, SWAP, Toffoli)
- Measurements and barriers
- Circuit composition and properties
- Parameterized circuits for variational algorithms
What ships with it
8 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.
- 1mo ago First seen · 273 lines · 73 tokens per session scan A 8e573376f5a0
qiskit is a skill published in the GitHub repository dralkh/seerai (94 stars, last pushed 20d ago), licensed MIT. It adds 73 tokens to every session and 2,051 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 97% identical to qiskit, differing in 3 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.
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…
datamol
Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery including SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters…