agentmods.dev

tensorcircuit-ng AGENTS.md

Repository instructions for TensorCircuit-NG, a quantum-computing framework that represents calculations as tensor networks and can run them on different computing backends.

In plain words
What is it for?
Use them when adding or changing core quantum-computing code, especially tensor operations and backend-independent algorithms.
Why use it?
They help changes remain compatible with automatic differentiation, just-in-time compilation, and all supported backends.

Instructions file for CodexOpenCode

88 20d ago A scan 1,291 tokens original Apache-2.0
View the source file tensorcircuit/tensorcircuit-ng
Install

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.

agentmods
npx agentmods add instructions/tensorcircuit/tensorcircuit-ng/agents-md
Clone the repo
git clone --depth 1 https://github.com/tensorcircuit/tensorcircuit-ng

Made for: Codex, OpenCode.

Per session 1,291 This file is loaded in full into every session.
When invoked 1,291 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
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 $0.01291 $0.01291
Opus 5 $0.00646 $0.00646
Sonnet 5 $0.00258 $0.00258
Haiku 4.5 $0.00129 $0.00129

Measured 2d ago against content hash c8c249818416, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

tensorcircuit-ng AGENTS.md 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 2d 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.md · 72 lines

How it starts

The opening of the file, as written. The whole thing — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.

TensorCircuit-NG Repository Guide for AI Agents

Mission

TensorCircuit is a tensor-network-first, multi-backend quantum computing framework. Optimize for backend-agnostic, differentiable, JIT-friendly code changes that match existing repository style.

Non-Negotiable Rules

  • Use tc.backend for core tensor operations. Do not call backend-specific APIs such as NumPy, JAX, or TensorFlow directly in core logic.
  • Prefer backend-native abstractions already used in the repo, including tc.backend.jit, tc.backend.grad, tc.backend.vmap, and backend control-flow helpers when applicable.
  • Preserve differentiability. Avoid graph-breaking conversions, in-place tensor mutation, and other patterns that block autodiff.
  • Preserve JIT compatibility. Avoid Python control flow that depends on tensor values; prefer backend control-flow helpers or static structure.
  • Keep changes minimal and consistent with existing architecture.
  • Prefer simple, direct implementations. Avoid defensive complexity and broad try...except blocks.
  • Fail fast. Expose real problems early instead of masking them with silent fallbacks or workaround-heavy logic.
  • Do not cheat around repository invariants, tests, or framework behavior just to make a local change appear to pass.
  • Focus only on git-tracked files. Do not search for or modify files in .gitignore unless explicitly specified by the user.
  • tensorcircuit/applications and benchmarks/ are deprecated, do not add or modify files there unless the user explicitly specified.

Environment Rules

  • Never install packages into system or user Python unless the user explicitly asks.
  • If a command fails because of missing Python packages or ModuleNotFoundError, ask the user which environment to use.
  • Once the environment is known, run Python tooling through that environment, for example conda run -n <env> ....
  • Dependency and tool configuration lives in requirements/, pyproject.toml, and .pylintrc.
  • Keep sandbox- or machine-specific execution workarounds out of tracked source files unless the user explicitly asks for a repo-level workaround. Examples include redirecting MPLCONFIGDIR, cache directories, or other local writable paths just to satisfy the current sandbox.
  • Do not hardcode or publicly expose local conda environment names, usernames, absolute paths, hostnames, or other machine-specific execution details in tracked docstrings, examples, or user-facing scripts. Use generic placeholders such as conda run -n <env> ... unless the repository explicitly needs a concrete value.

Read the full file on GitHub · 72 lines

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. 2d ago First seen · 72 lines · 1,291 tokens per session scan A c8c249818416

Subscribe to this mod's changes

tensorcircuit-ng AGENTS.md is an instructions file published in the GitHub repository tensorcircuit/tensorcircuit-ng (88 stars, last pushed 20d ago), licensed Apache-2.0. It adds 1,291 tokens to every session, about $0.0065 per session on Opus 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.

PyTorchScientificjaxmachine-learningnisqopen-quantum-systemsquantum-algorithm
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