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 legendtkl/agentic-skill-router --skill skill-033git clone --depth 1 https://github.com/legendtkl/agentic-skill-routerWrote 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/legendtkl/agentic-skill-router/skill-033)<a href="https://agentmods.dev/skills/legendtkl/agentic-skill-router/skill-033"><img src="https://agentmods.dev/badge/skills/legendtkl/agentic-skill-router/skill-033/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/legendtkl/agentic-skill-router/skill-033"><img src="https://agentmods.dev/badge/skills/legendtkl/agentic-skill-router/skill-033.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 | $0.00150 | $0.02632 |
| Opus 5.5 | $0.00060 | $0.01053 |
| Sonnet 5.5 | $0.00030 | $0.00526 |
| Haiku 4.5 | $0.00015 | $0.00263 |
Grade A, and why
skill-033 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
89% identical to qutip — 9 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 — 312 lines — stays where its author put it; the contents beside it link to each section on GitHub.
QuTiP: Quantum Toolbox in Python
Overview
QuTiP provides comprehensive tools for simulating and analyzing quantum mechanical systems. It handles both closed (unitary) and open (dissipative) quantum systems with multiple solvers optimized for different scenarios.
Installation
uv pip install qutip
Optional packages for additional functionality:
# Quantum information processing (circuits, gates)
uv pip install qutip-qip
# Quantum trajectory viewer
uv pip install qutip-qtrl
Quick Start
from qutip import *
import numpy as np
import matplotlib.pyplot as plt
# Create quantum state
psi = basis(2, 0) # |0⟩ state
# Create operator
H = sigmaz() # Hamiltonian
# Time evolution
tlist = np.linspace(0, 10, 100)
result = sesolve(H, psi, tlist, e_ops=[sigmaz()])
# Plot results
plt.plot(tlist, result.expect[0])
plt.xlabel('Time')
plt.ylabel('⟨σz⟩')
plt.show()
Core Capabilities
1. Quantum Objects and States
Create and manipulate quantum states and operators:
# States
psi = basis(N, n) # Fock state |n⟩
psi = coherent(N, alpha) # Coherent state |α⟩
rho = thermal_dm(N, n_avg) # Thermal density matrix
# Operators
a = destroy(N) # Annihilation operator
H = num(N) # Number operator
sx, sy, sz = sigmax(), sigmay(), sigmaz() # Pauli matrices
# Composite systems
psi_AB = tensor(psi_A, psi_B) # Tensor product
See references/core_concepts.md for comprehensive coverage of quantum objects, states, operators, and tensor products.
2. Time Evolution and Dynamics
Multiple solvers for different scenarios:
# Closed systems (unitary evolution)
result = sesolve(H, psi0, tlist, e_ops=[num(N)])
# Open systems (dissipation)
c_ops = [np.sqrt(0.1) * destroy(N)] # Collapse operators
result = mesolve(H, psi0, tlist, c_ops, e_ops=[num(N)])
# Quantum trajectories (Monte Carlo)
result = mcsolve(H, psi0, tlist, c_ops, ntraj=500, e_ops=[num(N)])
Solver selection guide:
sesolve: Pure states, unitary evolutionmesolve: Mixed states, dissipation, general open systemsmcsolve: Quantum jumps, photon counting, individual trajectoriesbrmesolve: Weak system-bath couplingfmmesolve: Time-periodic Hamiltonians (Floquet)
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 · 312 lines · 150 tokens per session scan A 72b1bb112593
skill-033 is a skill published in the GitHub repository legendtkl/agentic-skill-router (6 stars, last pushed 4mo ago), licensed MIT. It adds 150 tokens to every session and 2,632 once invoked, about $0.0006 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 89% identical to qutip, differing in 9 lines, and is treated as a copy.
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