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OpenPIV Flow Measurement

Measure fluid velocity fields from PIV image pairs, validate vectors, and compute vorticity and turbulence stats.

Source Download skill
Repository
K-Dense-AI/scientific-agent-skills
Stars
55,780
Licence
MIT
Files
6 files

What it does

Particle Image Velocimetry (PIV) is a lab method that films tiny tracer particles in a flowing fluid twice in quick succession and works out how far they moved. This skill teaches the agent how to run the OpenPIV Python library on such image pairs: setting window sizes, cross-correlating frames, filtering out bad vectors, scaling results to physical units, and deriving quantities like vorticity and strain rate. It also includes a command-line runner and helper scripts that save results and plots.

Great for

  • Experimental fluid dynamics researchers processing lab PIV image pairs
  • Students learning how PIV processing parameters affect the resulting velocity field
  • Engineers checking vector validation and outlier replacement on measured flow data

Skip it if

  • Simulating flow with CFD: the skill is for measured images, and the SKILL.md says to use a CFD skill instead
  • Safety-critical decisions made from unverified output, since the parameters and thresholds need expert review for each experiment

Try asking

  • “Compute a velocity field from my two PIV images frame_a.bmp and frame_b.bmp with 32-pixel windows”
  • “Validate the vectors in my PIV results and replace outliers using local mean”
  • “Calculate vorticity and strain rate from my saved velocity field params.npz”
  • “Run the OpenPIV example to confirm my installation works”
Step-by-step guide: how to use OpenPIV Flow MeasurementInstall, first run, examples, tips and FAQs

Install in 3 steps

  1. 1

    Download the skill

    The ZIP contains the 6 original files, the MIT licence and an attribution note.

    openpiv.zip
  2. 2

    Add it to your agent

    Claude Code (all projects)

    mkdir -p ~/.claude/skills && unzip openpiv.zip -d ~/.claude/skills/

    Claude Code / Agent SDK (this project only)

    mkdir -p .claude/skills && unzip openpiv.zip -d .claude/skills/

    Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.

  3. 3

    Ask for the task

    No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.

Get better results

  • Pin openpiv==0.26.1 when results must be reproducible, since the snippets were tested against that version.
  • Use the peak2peak signal-to-noise method with thresholds around 1.05 to 1.3, and do not reuse thresholds tuned for peak2mean.
  • Inspect dynamic masks on both frames before analysis, because windows straddling an object can be biased.

What's inside

  • references/advanced_algorithms.md
  • scripts/__init__.py
  • scripts/analyze.py
  • scripts/run_example.py
  • scripts/runner.py
  • SKILL.md

Redistributed unmodified from K-Dense-AI/scientific-agent-skills under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.