openpiv

openpiv is a skill for Claude Code from agentlas-ai/agentlas-desktop. It costs 72 tokens per session (4,568 once invoked), scanned A, a copy of openpiv, Apache-2.0.

A Python workflow for Particle Image Velocimetry, a way to measure fluid motion by tracking particles between two images. OpenPIV turns image pairs into velocity fields and derived flow measurements.

In plain words
What is it for?
Use it to measure two-dimensional velocity, validate and replace outliers, smooth vector fields, and calculate vorticity, strain rate, and turbulence statistics.
Why use it?
It provides steps for cleaning images, matching particle patterns, rejecting unreliable vectors, and converting pixel measurements into physical units. This helps make flow measurements more consistent.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Good fit Use it to measure two-dimensional velocity, validate and replace outliers, smooth vector fields, and calculate vorticity, strain rate, and turbulence statistics.

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Install with agentmods
npx agentmods add skills/agentlas-ai/agentlas-desktop/openpiv
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 agentlas-ai/agentlas-desktop --skill openpiv
Clone the repo
git clone --depth 1 https://github.com/agentlas-ai/agentlas-desktop

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/agentlas-ai/agentlas-desktop/openpiv/github.svg)](https://agentmods.dev/skills/agentlas-ai/agentlas-desktop/openpiv)
Your own site
<a href="https://agentmods.dev/skills/agentlas-ai/agentlas-desktop/openpiv"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/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/agentlas-ai/agentlas-desktop/openpiv"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/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.
Origin 100% copy Near-identical to another mod 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 5.5 $0.00029 $0.01827
Sonnet 5.5 $0.00014 $0.00914
Haiku 4.5 $0.00007 $0.00457

Measured 1mo ago against content hash 7b77eeb059c6, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, 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 1mo 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.

Origin

This is a copy

100% identical to openpiv — 144 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

plugins/agentlas-science-skills/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. 1mo 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 agentlas-ai/agentlas-desktop (6 stars, last pushed yesterday), licensed Apache-2.0. It adds 72 tokens to every session and 4,568 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to openpiv, differing in 144 lines, and is treated as a copy.

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