Dev Asset Lesson

Create a new asset pipeline lesson. This is a hybrid track — the pipeline orchestrator is Python, performance-critical processing uses compiled C tools (meshoptimizer, MikkTSpace), and procedural geometry lives in a header-only C library (common/shapes/forge_shapes.h).

The goal is not just pedagogical. Every library, tool, and pipeline component built in an asset lesson must be production-quality — well-tested, documented, and designed for reuse beyond the lesson. The project's own PLAN.md has a "Project Integration" section where forge-gpu's existing assets (models, textures, skyboxes) are processed through the pipeline we build. So the pipeline, plugins, and C tools are not toy examples scoped to a single lesson — they are the actual tooling this project depends on.

Concretely this means:

• Libraries and tools are shared, not lesson-local. Python code goes in pipeline/, C libraries go in common/, C tools go in tools/. The lesson directory contains the walkthrough, not the implementation.
• Test thoroughly. Every module gets a test suite (tests/pipeline/ for Python, tests/test_*.c for C). Edge cases, error paths, and realistic inputs — not just happy-path smoke tests.
• Design for integration. The Python pipeline will process forge-gpu's own models and textures. The C mesh tool will be invoked by the pipeline as a subprocess. The shapes library is already used by GPU and physics lessons. Build APIs that work for real projects.
• Don't cut corners for pedagogy. If the correct approach requires more code, write more code. Simplifying for the lesson at the cost of correctness or reusability defeats the purpose.

When to use this skill:

• You need to teach asset import, processing, or optimization concepts
• A learner wants to build tooling that transforms raw art into GPU-ready formats
• The lesson involves texture compression, mesh optimization, or asset bundling
• The lesson adds a web UI for browsing, previewing, or configuring assets
• The lesson creates procedural geometry from parametric equations
• The lesson integrates third-party C libraries (meshoptimizer, MikkTSpace)

Smart behavior:

• Before creating a lesson, check if an existing asset lesson already covers it
• Asset lessons are tool-building lessons — every concept must produce a working CLI command, C tool, library, or web page
• Focus on why each processing step matters for GPU performance
• Cross-reference GPU lessons that consume the processed assets
• Determine the lesson type (Python, C tool, or C library) before scaffolding

Arguments

The user (or you) can provide:

• Number: two-digit lesson number (e.g. 01, 02)
• Topic name: kebab-case (e.g. pipeline-scaffold, texture-processing)
• Description: what this teaches (e.g. "Plugin discovery, CLI entry point, TOML config")

If any are missing, infer from context or ask.

Lesson Types

The asset pipeline track has three lesson types. Determine which type applies before scaffolding.

Type A: Python lessons

Pipeline scaffold, texture processing, asset bundles, web frontend. These add functionality to the shared pipeline/ package at the repo root (not lesson-local code). The lesson directory contains only the README, diagrams, example config, and sample assets.

Directory structure:

pipeline/                     # shared library (repo root) — code goes HERE
  __init__.py
  __main__.py
  config.py, plugin.py, scanner.py, ...
  plugins/
    <type>.py                 # built-in plugins grow lesson by lesson
tests/
  pipeline/                   # tests for the shared library
    test_<module>.py
lessons/assets/NN-topic-name/
  README.md                   # lesson walkthrough pointing at pipeline/ code
  pipeline.toml               # example config for hands-on testing
  assets/                     # sample source files, diagrams

Not added to CMakeLists.txt — Python projects are not C targets. pyproject.toml is at the repo root — one package for the whole pipeline.

Type B: C tool lessons

Mesh processing with third-party C libraries (meshoptimizer, MikkTSpace). The C tool is a standalone executable that the Python pipeline invokes as a subprocess.

Directory structure:

lessons/assets/NN-topic-name/
  README.md
  main.c                    # standalone C tool
  CMakeLists.txt            # builds the tool, fetches dependencies
  tests/
    test_<tool>.c           # C test suite

Added to CMakeLists.txt — C tools need a build target. Add under an "Asset Pipeline Lessons" section (create it if needed, after Physics Lessons or at the end before Tests).

Type C: C library lessons

Procedural geometry and other header-only libraries that live in common/. These produce a library, a test suite, and optionally a GPU lesson that renders the output.

Directory structure:

common/<lib>/
  forge_<lib>.h             # header-only library
  README.md                 # API reference
lessons/assets/NN-topic-name/
  README.md                 # lesson walkthrough (may also have a GPU demo)
  PLAN.md                   # main.c decomposition (if GPU demo included)
  main.c                    # GPU showcase program (optional)
  CMakeLists.txt
  shaders/                  # if GPU demo
  assets/
tests/
  test_<lib>.c              # comprehensive test suite

Added to CMakeLists.txt — register the test target and any GPU demo.

Steps

1. Analyze what's needed

• Determine lesson type: Python (A), C tool (B), or C library (C)?
• Check existing asset lessons: Is there already a lesson for this topic?
• Identify the scope: What specific pipeline concepts does this lesson cover?
• Find cross-references: Which GPU/engine/math lessons relate?
• Check PLAN.md: Where does this lesson fit in the asset pipeline track?

2. Create the lesson directory

Follow the directory structure for the determined lesson type (A, B, or C).

3. Create the lesson content

For Python lessons (Type A)

Package conventions:

• Python 3.10+ — Use modern Python features (type hints, match statements, dataclasses, pathlib)
• CLI: Use argparse or click for command-line interface
• Config: TOML for project/asset configuration (tomllib in 3.11+, or tomli as fallback)
• Testing: pytest for unit tests
• Naming: snake_case for modules and functions, PascalCase for classes
• No global state — Pass configuration explicitly

Create pyproject.toml:

[project]
name = "forge-asset-pipeline"
version = "0.1.0"
description = "Asset processing pipeline for forge-gpu"
requires-python = ">=3.10"
dependencies = [
    # Add per-lesson dependencies here
]

[project.optional-dependencies]
dev = [
    "pytest>=7.0",
    "ruff>=0.4",
]

[project.scripts]
forge-pipeline = "pipeline.__main__:main"

For C tool lessons (Type B)

• Follow all forge-gpu C conventions (C99, naming, error handling)
• Use FetchContent to pull third-party libraries (meshoptimizer, MikkTSpace)
• Build a standalone CLI tool that reads input files and writes output files
• The Python pipeline invokes the tool as a subprocess
• Test with forge-gpu's existing test harness pattern

For C library lessons (Type C)

• Follow the forge_math.h pattern: header-only, static inline, thorough inline documentation
• Use SDL_malloc/SDL_free (not malloc/free)
• Use forge_math.h types (vec3, vec2, mat4, quat)
• Create a comprehensive test suite following tests/test_math.c pattern
• Optionally include a GPU demo that renders the library output
• For files over 800 lines, use the chunked-write pattern (mandatory)

4. Create README.md

Structure varies by lesson type but always includes:

• What you'll learn (bullet list)
• Result (screenshot, CLI output, or demo)
• Main explanation with diagrams
• Code walkthrough
• Key concepts
• Cross-references to other tracks
• Exercises
• Further reading

5. Update project files

• README.md (root): Add a row to the asset lessons table
• lessons/assets/README.md: Add a row to the lessons table
• PLAN.md: Check off the asset lesson entry
• CMakeLists.txt (root): Add targets for C tools/libraries (Types B and C only — Python lessons are not registered here)

6. Cross-reference other lessons

• Find related GPU lessons: Which rendering features consume these assets?
• Find related engine lessons: Build systems, dependency management
• Find related math lessons: Vectors, parametric equations, trigonometry
• Update those lesson READMEs: Add cross-reference notes
• Update asset lesson README: List related lessons in "Where it connects"

7. Test

Python lessons:

cd lessons/assets/NN-topic-name
pip install -e ".[dev]"
pytest
ruff check .

C tool/library lessons:

cmake -B build
cmake --build build --config Debug --target <test-target>
ctest --test-dir build -R <test-name>

Use a Task agent with model: "haiku" for build commands per project conventions.

8. Run markdown linting

npx markdownlint-cli2 "**/*.md"

Asset Lesson Conventions

Scope

• Core pipeline (Python) — CLI scaffold, plugin discovery, configuration, scanning, fingerprinting
• Texture processing (Python) — Resize, compress, mipmap generation, format conversion
• Mesh processing (C tool) — Vertex deduplication, index optimization, tangent generation (MikkTSpace), LOD generation (meshoptimizer), binary output
• Procedural geometry (C library) — Parametric surface generation, smooth and flat normals, struct-of-arrays GPU layout
• Asset bundles (Python) — Packing, compression, table of contents, dependency tracking
• Web frontend (Python) — Asset browser, 3D preview, import settings editor, scene editor

Python style

• Python 3.10+ with type hints
• snake_case for functions and variables, PascalCase for classes
• Docstrings on public functions and classes
• pathlib.Path for file paths (not string concatenation)
• dataclasses or attrs for structured data
• Lint with Ruff (same config as existing pyproject.toml in repo root)

C style

Follow the same conventions as all forge-gpu code:

• C99, matching SDL's style
• ForgeShapes prefix for public types, forge_shapes_ for functions (adjust prefix per library)
• PascalCase for typedefs, lowercase_snake_case for locals
• UPPER_SNAKE_CASE for #define constants
• No magic numbers — #define or enum everything
• SDL_malloc/SDL_free — not malloc/free
• Extensive comments explaining why and purpose

Plugin architecture

The Python pipeline uses a plugin system where each asset type registers a processor. C tools are invoked as subprocesses by the Python plugin:

import subprocess
from pathlib import Path

class MeshPlugin(AssetPlugin):
    """Mesh processing plugin — invokes compiled C tool."""
    name = "mesh"
    extensions = [".gltf", ".glb", ".obj"]

    def process(self, source: Path, config: dict) -> AssetResult:
        result = subprocess.run(
            ["forge-mesh-tool", str(source), "--output", str(output)],
            capture_output=True, text=True
        )
        if result.returncode != 0:
            raise ProcessingError(result.stderr)
        return AssetResult(source=source, output=output, metadata={...})

Incremental builds

Every processing step must support incremental builds:

1. Fingerprint source files (content hash, not timestamp)
2. Compare against cached fingerprints from the last build
3. Skip unchanged assets
4. Track dependencies — if a texture changes, re-process meshes that reference it

Configuration

Use TOML for pipeline and per-asset configuration:

# pipeline.toml — project-level config
[pipeline]
source_dir = "assets/raw"
output_dir = "assets/processed"
bundle_dir = "assets/bundles"

[texture]
default_format = "bc7"
max_size = 2048
generate_mipmaps = true

[mesh]
deduplicate = true
generate_tangents = true
lod_levels = [1.0, 0.5, 0.25]

Tone

Asset pipeline lessons should be practical and tool-focused. Pipeline tooling is infrastructure that enables art and rendering — treat it with the same rigor as the rendering code it serves. The output of these lessons is not disposable teaching material; it is production tooling that forge-gpu itself will use to process its own assets.

• Name the techniques and formats — BC7, KTX2, glTF, meshoptimizer, MikkTSpace — named tools and formats carry weight and help readers find documentation
• Show the data flow — Diagrams showing source -> process -> output are essential for pipeline lessons
• Measure improvement — Show file sizes, load times, or vertex counts before and after processing
• Connect to GPU — Always explain how the processed output maps to GPU concepts (texture formats, vertex layouts, draw calls)
• Build for real use — Every API, CLI flag, and config option should work for a real project, not just the lesson's sample assets

Example: Pipeline Scaffold Lesson (Type A — Python)

1. Scope: CLI entry point, plugin discovery, asset scanning, fingerprinting, TOML configuration
2. Create: lessons/assets/01-pipeline-scaffold/
3. Package: pipeline/ with __main__.py, config.py, scanner.py, plugin.py
4. Program: CLI that scans a directory for assets, fingerprints them, and reports what would be processed. No actual processing yet.
5. README: Explain plugin architecture, fingerprinting, TOML config, CLI design
6. Exercises: Add a new file type to the scanner, implement cache invalidation, add --verbose output

Example: Mesh Processing Lesson (Type B — C tool)

1. Scope: meshoptimizer for vertex/index optimization, MikkTSpace for tangent generation, binary output format, LOD generation
2. Create: lessons/assets/03-mesh-processing/
3. Tool: main.c that reads glTF/OBJ, processes with meshoptimizer and MikkTSpace, writes optimized binary output
4. CMake: FetchContent for meshoptimizer and MikkTSpace
5. Python plugin: plugins/mesh.py invokes the compiled tool as subprocess
6. README: Explain vertex cache optimization, overdraw optimization, tangent space, LOD simplification metrics
7. Exercises: Add vertex quantization, compare draw call performance before and after optimization

Example: Procedural Geometry Lesson (Type C — C library)

1. Scope: forge_shapes.h — parametric surface generation (sphere, icosphere, cylinder, cone, torus, plane, cube, capsule), struct-of-arrays layout, smooth vs flat normals
2. Create: common/shapes/forge_shapes.h, common/shapes/README.md, lessons/assets/04-procedural-geometry/, tests/test_shapes.c
3. Library: Header-only with FORGE_SHAPES_IMPLEMENTATION guard
4. GPU demo: Five-shape showcase with Blinn-Phong lighting
5. Tests: 28 tests covering vertex counts, normals, UVs, winding, memory
6. README: Parametric surfaces, slices/stacks, seam duplication, smooth vs flat normals, struct-of-arrays vs interleaved

When NOT to Create an Asset Lesson

• The topic is covered by an existing asset lesson
• The concept is about GPU rendering only (belongs in a GPU lesson)
• The concept is about C fundamentals only (belongs in an engine lesson)
• The concept is pure math only (belongs in a math lesson)
• The topic is too narrow for a full lesson (add to an existing lesson instead)

In these cases, update existing documentation or plan for later.

Tips

• Start with the CLI — Get the command-line interface working first, then add processing logic. A well-structured CLI with no-op plugins is a solid foundation.
• Test with real assets — Use assets from existing GPU lessons as test inputs. This validates that the pipeline produces output the C code can actually consume.
• Fingerprint, don't timestamp — Content hashes are deterministic and portable. Timestamps break on copy, git clone, and CI.
• Show before/after — File size comparisons, vertex count reductions, and load time improvements make the value of the pipeline concrete.
• Keep the web UI simple — Static HTML/CSS/JS served by Python. No npm, no webpack, no framework. The pipeline is the lesson, not the frontend stack.
• Chunked writes for large C files — forge_shapes.h and GPU demo main.c will exceed 800 lines. Use the chunked-write pattern per .claude/large-file-strategy.md.