tensorcircuit-ng: Skill for Claude Code

.agents/skills/tc-rosetta/SKILL.md

tc-rosetta is a skill for Claude Code from tensorcircuit/tensorcircuit-ng. It costs 56 tokens per session (881 once invoked), scanned A, original, Apache-2.0.

A migration guide that rewrites quantum-computing scripts from Qiskit or PennyLane for TensorCircuit-NG, a different quantum software framework. It preserves the mathematical goal while adapting the code to TensorCircuit-NG and JAX.

In plain words
What is it for?
Use it to migrate algorithms such as VQE, QAOA, or quantum Fisher information scripts, then produce a before-and-after execution-time comparison.
Why use it?
A line-by-line conversion can carry over assumptions from the old framework and miss the new framework's functional and vectorized style.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: installed under .agents/ (shared by several agents).

This is tensorcircuit/tensorcircuit-ng's own configuration. It tells Claude Code how to work on tensorcircuit-ng itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything tensorcircuit-ng configures →

Reuse

Borrowing it

Nothing to install: this file belongs to tensorcircuit/tensorcircuit-ng. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/tensorcircuit/tensorcircuit-ng/master/.agents/skills/tc-rosetta/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/tensorcircuit/tensorcircuit-ng

Made for: Claude Code.

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README.md
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Per session 56 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 881 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00056 $0.00881
Opus 5.5 $0.00022 $0.00352
Sonnet 5.5 $0.00011 $0.00176
Haiku 4.5 $0.00006 $0.00088

Measured 2mo ago against content hash d302c3c734a3, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

tc-rosetta 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 2mo 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.

.agents/skills/tc-rosetta/SKILL.md · 48 lines

How it starts

The opening of the file, as written. The whole thing — 48 lines — stays where its author put it; the contents beside it link to each section on GitHub.

When tasked with translating quantum computing code from frameworks like Qiskit, PennyLane, or Cirq into TensorCircuit-NG (TC-NG), you act as a Principal Quantum Software Migration Expert.

Your goal is NOT to do a naive 1:1 syntax or line-by-line translation. You must perform End-to-End Intent Understanding to refactor the code into the idiomatic, differentiable, and functional programming paradigm of TC-NG.

1. End-to-End Intent Extraction (Do NOT Translate Line-by-Line)

  • Read the Entire Script: Absorb the global objective of the source code (e.g., VQE, QAOA, QFI).
  • Extract the Math/Physics Core: Extract only the fundamental mathematical entities: the Ansatz architecture, the target Hamiltonian, and the loss function.
  • Discard Legacy Paradigms: Explicitly abandon the original framework's constraints (e.g., Qiskit's parameter-binding for loops).

2. Idiomatic Synthesis in TC-NG

  • Start Fresh: Write the TC-NG script from scratch based on the extracted intent.
  • JAX-Native Initialization:
    import tensorcircuit as tc
    import jax
    import jax.numpy as jnp
    import time
    
    tc.set_backend("jax")
    
  • Functional Paradigm & Vectorization: Construct the circuit execution as a pure, differentiable Python function. Apply tc.backend.vmap natively for any batched operations.
  • Programming Paradigms: Avoid over-defensive programming; trust internal invariants where reasonable. Use try...except sparingly and never use broad catch-all blocks like except Exception:. Fail fast and expose problems early rather than masking them with silent failures or broad error handling.

3. Execution, Verification & Strict Benchmarking

  • Run the Source Code: If the original script is executable, run it and strictly record its total execution time using time or a simple bash time python script.py.
  • Run the TC-NG Code: Execute your newly synthesized TC-NG script. Ensure it outputs numerically equivalent results (e.g., matching loss curves or final energies).
  • Record Performance: Measure the exact execution time of the TC-NG script, noting separately the JIT compilation time (if applicable/measurable) and the actual execution time.

Read the full file on GitHub · 48 lines

Files

What ships with it

1 file 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.

Changes

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.

  1. 2mo ago First seen · 48 lines · 56 tokens per session scan A d302c3c734a3

Subscribe to this mod's changes

tc-rosetta is a skill published in the GitHub repository tensorcircuit/tensorcircuit-ng (93 stars, last pushed today), licensed Apache-2.0. It adds 56 tokens to every session and 881 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.

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