Shammunul Islam
Community@shammun · Virginia, USA
Shammunul Islam's fluidpy project converts Kundu's Fluid Mechanics textbook into verified Python code, teaching notebooks, and interactive web explainers.
Agent Skills by Shammunul Islam
Showing 17 vetted skills indexed across 1 GitHub repositories.
shammunteaching-style
Applies a structured pedagogical style guide when writing fluidpy notebooks and interactive explainers.
shammunpython-viz
Creates matplotlib figures, animations, and plotly interactive plots for fluidpy teaching notebooks.
shammuncolab-notebook
Builds, executes, and publishes chapter notebooks with Colab twins and HTML pages.
shammunbuild-viz
Builds, reviews, and embeds one interactive HTML explainer for a fluid mechanics chapter.
shammunpublish-chapter
Publishes a chapter's executed notebook, Colab twin, and explainer pages to GitHub Pages.
shammunsetup-project
Bootstraps the fluidpy project environment with venv, PDF splitting, tests, and git initialization.
shammuninteractive-viz
Builds and audits full-window interactive HTML explainers for fluid mechanics chapters.
shammunstatus
Prints fluidpy chapter progress, open items, environment state, and the next command to run.
shammunnotebook-chapter
Rebuilds and headlessly executes one chapter's teaching notebook with optional change requests.
shammunmath-to-python
Converts textbook equations and derivations into tested Python implementations for fluid dynamics.
shammunfluids-book
Enforces project conventions for building tested Python modules, notebooks, and explainers from a fluid mechanics textbook.
shammunverify-implementation
Validates Python implementations of textbook fluid mechanics equations using a seven-level evidence ladder.
shammundata-and-benchmarks
Defines a four-tier data sourcing policy for fluid dynamics benchmarks, datasets, and reference values.
shammundo-chapter
Orchestrates a multi-agent pipeline converting a textbook chapter into tested Python code, notebooks, and interactive explainers.
shammunexplain-concept
Explains fluid mechanics concepts with step-by-step mathematics, worked examples, and runnable Python code.
shammunchapter-knowledge
Maintains cumulative knowledge files that carry lessons and primitives between textbook chapters.
shammunverify-chapter
Re-runs chapter verification tests, fix loops, and parity checks for fluid mechanics notebooks.
Frequently Asked Questions About Shammunul Islam
FAQPage SchemaWhat tasks can I accomplish with Shammunul Islam's fluidpy skills?▼
You can transcribe Fluid Mechanics (Kundu 5e) chapters into tested Python, verify implementations against analytic solutions and published benchmarks, build 4-5 interactive explainers per chapter, generate teaching notebooks with Colab twins, and publish everything to GitHub Pages via commands like /do-chapter, /build-viz, and /publish-chapter.
Who are these skills designed for?▼
They target the fluidpy project maintainer and contributors—educators, fluid dynamics students, and scientific Python developers who need faithful textbook-to-code transcription, rigorous numerical verification, tiered teaching content, and interactive web visualizations for each book chapter.
How do I set up and run the fluidpy project?▼
Run /setup-project once: it verifies the book PDF and chapter map, creates a Python venv with all dependencies (including Playwright and imageio-ffmpeg), registers the fluidpy-venv kernel, splits the book into chapters, runs self-tests, and initializes git with a public-repo guard. Then use /do-chapter N to process chapters.
How does fluidpy prove textbook-derived code is correct?▼
It applies the V1-V7 evidence ladder: analytic solutions, symbolic checks, convergence studies, conservation laws, published benchmarks, book reference values, and limiting cases. Tests scale with curation tiers, tolerances are explicit, and results are documented in a verification report via /verify-chapter.
What are the prerequisites and data policies for fluidpy?▼
You need the Kundu Fluid Mechanics 5e PDF, a Python environment with Playwright and a browser (Edge/Chrome), and git. Data policy is analytic/synthetic first, then cited published benchmarks, then free datasets, with Google Drive fallback for manual downloads accessible from Colab.