Claude Skill MartBrowse skillsWhat is a Skill?
 
← Back to list

OpenPIV Flow Analysis

Extract velocity fields from PIV image pairs and derive vorticity, strain rate, and turbulence statistics with OpenPIV.

Data & AnalyticsAdvanced★ 33,030⑂ 3,248AI score 8/10Last updated: Aug 9, 2026

What it does

This skill teaches Claude the full OpenPIV workflow for experimental Particle Image Velocimetry data.

  • End-to-end pipeline: preprocessing (dynamic masking) → cross-correlation → vector validation → outlier replacement → smoothing → scaling to physical units.
  • Covers both single-pass extended_search_area_piv and multi-pass window deformation via windef.simple_multipass.
  • Computes vorticity, the 2D strain-rate tensor, RMS fluctuations and turbulent kinetic energy.
  • Plots vector fields over the source image and saves vectors.txt / params.npz outputs.
  • Ships a CLI runner (runner.py), an example verifier (run_example.py), and a PIVAnalyzer class.

The standout feature is the honesty about API traps: differing s2n scales, windef silently ignoring dt, unrecognized replace_outliers methods failing quietly, and the vorticity sign flip after transform_coordinates.

Who it's for

  • Fluid dynamics researchers and flow-visualization labs
  • Graduate students processing PIV image pairs for the first time
  • Engineers who want reproducible, version-pinned PIV pipelines
  • Anyone working with measured flow data rather than CFD simulations

Example uses

  1. Basic velocity field — "Process frame_a.bmp and frame_b.bmp with window_size 32, overlap 12, dt 0.02 and render the vector field over the first image."
  2. Large displacements — "This jet has big displacements; run multi-pass 64→32→16 and convert the px/frame output to px/s."
  3. Post-processing — "From results/params.npz, compute vorticity and shear strain, then summarize RMS and TKE in a table."

· · · Install guide · · ·

Install in the Claude app (no terminal)
  1. Download the ZIP with the button below.
  2. In Claude, open Settings → Capabilities and turn on 'Code execution and file creation'. (one time)
  3. Go to Customize → Skills → + → 'Upload a skill' and upload the ZIP.
↓ Download ZIP
Install in Claude Code

Let Claude do it — paste this into Claude Code

Install the skill I found on Claude Skill Mart.
Copy the skills/openpiv folder from the GitHub repo K-Dense-AI/scientific-agent-skills into my ~/.claude/skills/openpiv/.
When it's done, tell me in one line what this skill can do.

Install with a command instead

git clone https://github.com/K-Dense-AI/scientific-agent-skills.git /tmp/sci-skills && cp -r /tmp/sci-skills/skills/openpiv ~/.claude/skills/openpiv

⚠ This is a third-party skill. Check the source repository before installing.

  1. Open a terminal and confirm Python 3.10+ is available: python3 --version
  2. Install OpenPIV, pinned for reproducibility: uv pip install "openpiv==0.25.4" (or pip install "openpiv==0.25.4" if you don't use uv).
  3. Clone the skill repository: git clone https://github.com/K-Dense-AI/scientific-agent-skills.git /tmp/sci-skills
  4. Copy the skill into your Claude skills folder: mkdir -p ~/.claude/skills && cp -r /tmp/sci-skills/skills/openpiv ~/.claude/skills/openpiv
  5. Restart Claude Code and try a prompt such as "Compute the velocity field from this PIV image pair."
  6. Verify the install end to end: python ~/.claude/skills/openpiv/scripts/run_example.py --output_dir /tmp/openpiv-demo and check that the demo outputs appear.
View source on GitHub ↗License: MIT