openpiv

openpiv is a skill for Claude Code from K-Dense-AI/scientific-agent-skills. It costs 72 tokens per session (4,568 once invoked), scanned A, original, MIT.

A Python-based tool for measuring two-dimensional fluid motion from paired Particle Image Velocimetry images. Particle Image Velocimetry is a method that tracks particles in images to estimate how a fluid moves.

In plain words
What is it for?
Use it to calculate velocity fields, remove or replace outliers, smooth measurements, and derive vorticity, strain rate, or turbulence statistics.
Why use it?
It provides steps for cleaning images, calculating motion vectors, checking bad measurements, and converting results into physical units. This reduces the manual work involved in turning flow images into usable measurements.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

not rated 44krepo +1.6k today A scan Socket: passSnyk: passSkillSpector: pass 72 tokens original MIT

Good fit Use it to calculate velocity fields, remove or replace outliers, smooth measurements, and derive vorticity, strain rate, or turbulence statistics.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/k-dense-ai/scientific-agent-skills/openpiv
About the project

Scientific Agent Skills is a collection of reusable procedures that give AI agents capabilities for scientific research across areas such as biology, chemistry, medicine, and drug discovery. It is used by researchers and by people building AI scientist workflows with compatible coding agents. The catalogue contains many of the project's skills and supporting instructions.

K-Dense-AI/scientific-agent-skills · 44,469 stars · on GitHub · arxiv.org

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 K-Dense-AI/scientific-agent-skills --skill openpiv
Clone the repo
git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills

Made for: Claude Code.

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 openpiv

README.md
[![agentmods](https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/openpiv/github.svg)](https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/openpiv)
Your own site
<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/openpiv"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/openpiv/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 openpiv

Your own site · 80×15
<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/openpiv"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/openpiv.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 72 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,568 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
  • Socket pass 3 Aug 2026
  • Snyk pass 29 Jul 2026
  • 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 $0.00072 $0.04568
Opus 5 $0.00036 $0.02284
Sonnet 5 $0.00014 $0.00914
Haiku 4.5 $0.00007 $0.00457

Measured 8d ago against content hash 7b77eeb059c6, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

openpiv 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 8d ago.

The scan reads SKILL.md. This mod also ships 4 executable files (scripts/__init__.py, scripts/analyze.py, scripts/run_example.py, …), 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.

skills/openpiv/SKILL.md · 470 lines

How it starts

The opening of the file, as written. The whole thing — 470 lines — stays where its author put it; the contents beside it link to each section on GitHub.

OpenPIV

Overview

OpenPIV (Open Particle Image Velocimetry) analyzes fluid flow from PIV image pairs. It covers preprocessing, cross-correlation, vector validation, outlier replacement, smoothing, and scaling to physical units.

Everything below is verified against openpiv 0.25.4. The API moves between releases — check inspect.signature() before trusting a snippet against a different version.

When to use

Use this skill when working with experimental PIV or flow-visualization image pairs: measuring 2D velocity fields, tuning interrogation-window parameters, validating vectors, or deriving vorticity, strain rate, and turbulence statistics. For simulating flow rather than measuring it, use a CFD skill instead.

Quick Start

Install OpenPIV:

uv pip install openpiv

# Pin it when the analysis needs to be reproducible -- this is the version every
# snippet below was checked against.
uv pip install "openpiv==0.25.4"

Run PIV analysis on an image pair:

import numpy as np
from openpiv import tools, pyprocess, validation, filters, scaling

frame_a = tools.imread("image_a.bmp")
frame_b = tools.imread("image_b.bmp")

# Cross-correlate. Returns (u, v, s2n) whenever sig2noise_method is not None.
u, v, s2n = pyprocess.extended_search_area_piv(
    frame_a.astype(np.int32),
    frame_b.astype(np.int32),
    window_size=32,
    overlap=12,
    dt=0.02,
    search_area_size=38,
    correlation_method="linear",   # required for search_area_size > window_size
    sig2noise_method="peak2peak",
)

x, y = pyprocess.get_coordinates(
    image_size=frame_a.shape,
    search_area_size=38,
    overlap=12,
)

# flags is a boolean array: True marks a spurious vector.
flags = validation.sig2noise_val(s2n, threshold=1.05)
u, v = filters.replace_outliers(u, v, flags, method="localmean", max_iter=3, kernel_size=2)

# Scale to physical units, then flip to image coordinates for plotting.
x, y, u, v = scaling.uniform(x, y, u, v, scaling_factor=96.52)
x, y, u, v = tools.transform_coordinates(x, y, u, v)

tools.save("vectors.txt", x, y, u, v, flags)

Read the full file on GitHub · 470 lines

Files

What ships with it

5 files 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. 8d ago First seen · 470 lines · 72 tokens per session scan A 7b77eeb059c6

Subscribe to this mod's changes

openpiv is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (44,469 stars, last pushed today), licensed MIT. It adds 72 tokens to every session and 4,568 once invoked, about $0.0004 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 2026-09-03.

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