hwcc 0.1.0

Hardware Context Compiler — transforms hardware docs into AI-optimized context

hwcc — Hardware Context Compiler

AI coding tools already read everything in your repo — source code, Makefiles, configs. But they can't read the vendor PDF on your desk. They hallucinate register addresses, get init sequences wrong, and miss errata.

hwcc fixes this. It transforms hardware documentation (datasheets, SVD files, reference manuals, device trees) into AI-optimized context files that any coding tool can consume — Claude Code, Codex, Cursor, Gemini CLI, or Copilot.

Install

pip install git+https://github.com/Ecro/hwcc.git

Or install from source:

git clone https://github.com/Ecro/hwcc.git
cd hwcc
pip install -e .

Requires Python 3.11+. No API keys needed — runs 100% locally by default.

Quick Start

# Initialize in your project
cd my-firmware-project
hwcc init --chip STM32F407

# Add hardware documentation
hwcc add docs/STM32F407.svd
hwcc add docs/reference-manual.pdf
hwcc add docs/errata.pdf

# Check what's indexed
hwcc status

That's it. hwcc add automatically compiles context into your CLAUDE.md (and other tool files) with hardware register maps, peripheral details, and coding conventions — all between safe <!-- BEGIN/END HWCC CONTEXT --> markers that never touch your existing content.

What It Produces

After hwcc add, your project gets:

File	Tool	Content
CLAUDE.md	Claude Code	Hardware context section
AGENTS.md	Codex CLI	Same context, Codex format
.cursor/rules/hardware.mdc	Cursor	Cursor rules format
.gemini/GEMINI.md	Gemini CLI	Gemini format
.github/copilot-instructions.md	GitHub Copilot	Copilot format

Plus internal context files in .rag/context/:

• hot.md — Concise hardware summary (~120 lines), always loaded
• peripherals/*.md — Per-peripheral deep context (registers, usage patterns, errata)

Supported Documents

Format	What It Extracts
.svd	Register maps with bit-fields, reset values, access types
.pdf	Text + tables from datasheets, reference manuals, errata
.dts / .dtsi	Device tree topology, peripheral nodes, pin configs
.md / .txt	Passthrough (application notes, custom docs)

Commands

hwcc init [--chip <mcu>]                Initialize project
hwcc add <file> [--type <type>]         Add document(s) to index
hwcc remove <doc_id>                    Remove a document
hwcc status                             Show project status
hwcc compile [--target <tool>]          Regenerate context files
hwcc version                            Show version

Useful Options

# Add with explicit chip tag (useful for multi-chip projects)
hwcc add power-ic.pdf --chip TPS65217

# Compile for a specific tool only
hwcc compile --target claude

# Skip auto-compile after adding (compile manually later)
hwcc add docs/*.svd --no-compile

Configuration

hwcc init creates .rag/config.toml:

[project]
name = "motor-controller"

[hardware]
mcu = "STM32F407VGT6"
mcu_family = "STM32F4"

[software]
rtos = "FreeRTOS 10.5.1"
hal = "STM32 HAL v1.27.1"

[conventions]
register_access = "HAL functions only, no direct register writes"

[embedding]
provider = "chromadb"  # Default: built-in, zero-config

[output]
targets = ["claude", "codex", "cursor", "gemini"]

Edit this file to tune what context gets generated.

Example Output

See examples/stm32f407-motor/ for a complete sample project with pre-generated output. Key files:

• CLAUDE.md — User content preserved, hardware context injected between markers
• peripherals/spi1.md — Full SPI1 register map with bit-fields and reset values
• peripherals/tim1.md — TIM1 advanced timer registers

How It Works

Raw Docs → Parse → Chunk → Embed → Store → Compile → Output Files
             |        |       |       |         |
           SVD/PDF  512-tok  ONNX  ChromaDB  Jinja2 templates
           parser   splits   local  vectors   per-tool output

All processing is deterministic and local. No LLM calls in the core pipeline. Embedding uses ChromaDB's built-in ONNX model (all-MiniLM-L6-v2) — zero configuration needed.

Why Not Just Give the AI the PDF?

• PDFs are huge (500+ pages). Context windows are expensive and lossy.
• SVD register data is structured — parsing it deterministically is more reliable than LLM extraction.
• Pre-compiled context survives context compaction (Claude Code's auto-compaction can drop raw PDFs).
• RAG is ~1,250x cheaper than stuffing full documents into every prompt.

Development

git clone https://github.com/Ecro/hwcc.git
cd hwcc
pip install -e ".[dev]"

pytest tests/           # 675 tests
ruff check src/ tests/  # lint
mypy src/hwcc/          # type check

License

MIT