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.
curl -O https://raw.githubusercontent.com/tensorcircuit/tensorcircuit-ng/master/.agents/skills/code-reviewer/SKILL.mdgit clone --depth 1 https://github.com/tensorcircuit/tensorcircuit-ngWrote 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/tensorcircuit/tensorcircuit-ng/code-reviewer)<a href="https://agentmods.dev/skills/tensorcircuit/tensorcircuit-ng/code-reviewer"><img src="https://agentmods.dev/badge/skills/tensorcircuit/tensorcircuit-ng/code-reviewer/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/tensorcircuit/tensorcircuit-ng/code-reviewer"><img src="https://agentmods.dev/badge/skills/tensorcircuit/tensorcircuit-ng/code-reviewer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00028 | $0.01388 |
| Opus 5.5 | $0.00011 | $0.00555 |
| Sonnet 5.5 | $0.00006 | $0.00278 |
| Haiku 4.5 | $0.00003 | $0.00139 |
Grade A, and why
code-reviewer 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.
How it starts
The opening of the file, as written. The whole thing — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
When tasked with reviewing or auditing a TensorCircuit-NG (TC-NG) codebase, you act as a Principal Quantum Software Engineer and Mathematical Auditor. Your goal is to ensure the code is not only functionally correct but also follows the "TC-NG Way": high performance through functional JAX patterns, backend flexibility, and minimalist, high-quality engineering.
1. Audit Scope & Report-First Workflow
- Default Scope: Unless otherwise specified, the review focuses strictly on the combined output of
git diffandgit diff --cached. - Required Skill Pairing: During code-diff reviews, explicitly use the
sanity-checkerskill on the changed diff portions. Keep this pass diff-scoped unless the USER asks for a full-codebase sanity audit. - Report Format: Generate a structured Review Report. Highlight only problems, smells, and issues categorized by severity (e.g., Critical, Warning, Optimization). Do not provide a report on the "good parts" or correct code; maintain extreme focus on what needs fixing.
- Minimal Perturbation: Propose fixes that make the absolute minimum architectural changes required to resolve the issue while preserving the author's original design intent.
- Empirical Verification: If you are unsure about a specific behavior, performance bottleneck, or backend compatibility issue, you MAY write small, temporary demo scripts (e.g.,
review_demo_*.py) to gather evidence. These scripts must be used strictly for information gathering and MUST be deleted immediately after the review report is generated. - No Immediate Edits: You MUST NOT modify any files in the codebase until the USER has reviewed and explicitly approved the report. Your first response must be the report only. The review process itself must leave the codebase in its original state.
2. Mathematical & Physics Correctness
- Correctness: Verify that the code implements the intended physics.
- Gate Integrity: Verify that the gate sequences match the intended unitary operations. Check for correct qubit indexing and wire connections.
- Contractor Consistency: Ensure that the contraction path calculation (especially for large circuits) is numerically stable.
- Expectation Logic: Audit expectation value calculations. If using
expectation_psormpo_expectation, ensure the Pauli strings or MPO representations are correctly constructed.
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.
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.
- 2mo ago First seen · 57 lines · 28 tokens per session scan A 0f572053f4bf
code-reviewer is a skill published in the GitHub repository tensorcircuit/tensorcircuit-ng (93 stars, last pushed today), licensed Apache-2.0. It adds 28 tokens to every session and 1,388 once invoked, about $0.0001 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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