numerical

numerical is a skill for Claude Code from brody-0125/my-claude-skills. It costs 107 tokens per session (4,134 once invoked), scanned A, original, MIT.

A specialist workflow for checking and optimising numerical Python and Dart projects. Numerical computing means using code for calculations with arrays, tensors, or scientific data.

In plain words
What is it for?
Use it to inspect NumPy, SciPy, CuPy, or Dart tensor code, verify broadcasting and floating-point behaviour, and review SIMD, GPU, and test-case choices.
Why use it?
It helps verify that calculations are correct and that array shapes, decimal arithmetic, memory use, and hardware acceleration are handled properly.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: reads .claude/ paths; mentions subagents.

Part of the numerical plugin — 1 skill shipped together

Good fit Use it to inspect NumPy, SciPy, CuPy, or Dart tensor code, verify broadcasting and floating-point behaviour, and review SIMD, GPU, and test-case choices.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/brody-0125/my-claude-skills/numerical
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.

Any agent
npx skills add brody-0125/my-claude-skills --skill numerical
Clone the repo
git clone --depth 1 https://github.com/brody-0125/my-claude-skills

Made for: Claude Code.

Or install numerical, the plugin that ships this one along with the rest of its 1 skill.

Wrote 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.

agentmods badge for numerical

README.md
[![agentmods](https://agentmods.dev/badge/skills/brody-0125/my-claude-skills/numerical/github.svg)](https://agentmods.dev/skills/brody-0125/my-claude-skills/numerical)
Your own site
<a href="https://agentmods.dev/skills/brody-0125/my-claude-skills/numerical"><img src="https://agentmods.dev/badge/skills/brody-0125/my-claude-skills/numerical/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.

agentmods 80×15 button for numerical

Your own site · 80×15
<a href="https://agentmods.dev/skills/brody-0125/my-claude-skills/numerical"><img src="https://agentmods.dev/badge/skills/brody-0125/my-claude-skills/numerical.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 107 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,134 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.
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 $0.00107 $0.04134
Opus 5 $0.00053 $0.02067
Sonnet 5 $0.00021 $0.00827
Haiku 4.5 $0.00011 $0.00413

Measured 11d ago against content hash 453b34c4e227, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

numerical 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 11d ago.

The scan reads SKILL.md. This mod also ships 4 executable files (scripts/_common.sh, scripts/discover-project.sh, scripts/setup-hooks.sh, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

plugins/numerical/skills/numerical/SKILL.md · 355 lines

How it starts

The opening of the file, as written. The whole thing — 355 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Numerical — Numerical Computing Verification & Optimization Agent

An agent that verifies correctness and optimizes performance of numerical computing code through Analyze → Verify → Optimize workflow

Role

A workflow agent specializing in numerical computing for Python and Dart projects. It automatically detects the project language (Python/Dart/Mixed), numeric libraries (NumPy, SciPy, CuPy, dart_tensor, etc.), and provides expert-level verification and optimization of array/tensor operations, floating-point arithmetic, broadcasting, memory layout, SIMD utilization, and GPU processing.

Core Principles

  1. Lazy-load context documents per Phase to ensure verification thoroughness
  2. Sequential execution of Analyze → Verify → Optimize
  3. Repeat Verify loop the number of times specified by the user (Ralph-style)
  4. Automatically adjust verification level based on computational complexity (Tiered Verification)
  5. IEEE 754 compliance as the baseline for all floating-point analysis

Quick Start (Zero-Config)

Phase 0 자동으로 모든 설정을 완료하므로 사용자 개입이 필요 없다:

1. Project Discovery    — pyproject.toml / pubspec.yaml 분석 → 언어, 라이브러리, GPU 지원 자동 감지
2. Numeric Profile      — dtype 사용 패턴, 배열 차원, 연산 유형 자동 프로파일링
3. Tool Detection       — pytest/ruff/mypy/dart analyze 등 검증 도구 자동 감지
4. Hooks Installation   — lint-on-edit, secret-guard, test-quality-gate 자동 설치

첫 실행 시 추가 프롬프트 없이 위 4단계가 순차적으로 실행된다. 감지된 설정을 변경하려면 해당 파일을 직접 편집하면 된다:

  • 정적 분석 도구: .numerical/analysis-tools.txt (줄 단위, 삭제 시 재감지)
  • Hooks: .claude/settings.jsonhooks 섹션 (삭제 시 재설치)

Phase Workflow Diagram

┌──────────────────────────────────────────────────────────────────────┐
│                         numerical                         │
└──────────────────────────────────────────────────────────────────────┘
                                   │
                                   ▼
                    ┌──────────────────────────┐
                    │      Phase 0: Discovery   │
                    │  • 언어/라이브러리/GPU 감지   │
                    │  • 수치 프로파일 캐시 저장    │
                    └────────────┬─────────────┘
                                 │
                    ┌────────────▼─────────────┐
                    │      Phase 1: Analyze     │
                    │  • 수치 연산 패턴 분석       │
                    │  • dtype/shape 추적         │
                    │  • 정밀도 위험 감지          │
                    └────────────┬─────────────┘
                                 │
              ┌──── dry-run? ────┴──────────────────┐
              │                                      │
         ┌────▼────┐                    ┌───────────▼───────────┐
         │  HALT   │                    │   Phase 2: Verify      │
         │ (리포트만)│                    │  • 부동소수점 정합성 검증  │
         └─────────┘                    │  • 브로드캐스팅 규칙 검증  │
                                        │  • 테스트 케이스 검증      │
                                        │  • 에지 케이스 분석        │
                                        └───────────┬───────────┘
                                                    │
                    ┌───────────────────────────────▼───────────────────────────────┐
                    │                    Phase 3: Optimize                          │
                    │  ┌─────────────────────────────────────────────────────────┐  │
                    │  │  • SIMD 정렬/벡터화 최적화 제안                            │  │
                    │  │  • GPU 메모리 관리 최적화                                  │  │
                    │  │  • 메모리 레이아웃 (C/F-contiguous) 최적화                  │  │
                    │  │  • 알고리즘 수치 안정성 개선                                │  │
                    │  └─────────────────────────┬───────────────────────────────┘  │
                    │                            │                                   │
                    │           ┌────────────────▼────────────────┐                  │
                    │           │         종료 조건 확인           │                  │
                    │           │  • 위반 0개?                    │                  │
                    │           │  • 정밀도 목표 달성?             │                  │
                    │           │  • 동일 이슈 3회 반복?           │                  │
                    │           │  • max loop 도달?               │                  │
                    │           └────────────────┬────────────────┘                  │
                    │                            │                                   │
                    │         ┌─── EXIT ─────────┴─────── CONTINUE ───┐              │
                    │         │                                        │              │
                    │         │                              Loop N++ (재검증)        │
                    │         │                                        │              │
                    └─────────┼────────────────────────────────────────┘              │
                              │                                                       │
                              ▼                                                       │
                    ┌──────────────────────────┐                                     │
                    │        Complete          │◄────────────────────────────────────┘
                    │  • 분석 리포트 출력        │
                    │  • PROGRESS.md 기록       │
                    └──────────────────────────┘

Read the full file on GitHub · 355 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. 11d ago First seen · 355 lines · 107 tokens per session scan A 453b34c4e227

Subscribe to this mod's changes

numerical is a skill published in the GitHub repository brody-0125/my-claude-skills (2 stars, last pushed 6mo ago), licensed MIT. It adds 107 tokens to every session and 4,134 once invoked, about $0.0005 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 2026-08-31.

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