Contributing to Shadow

March 16, 2026 ยท View on GitHub

Shadow is a working product being open-sourced. The capture pipeline, storage engine, search, and intelligence layers are tested and running. We welcome contributions that make Shadow better, more reliable, and more useful.

Getting Started

Prerequisites

  • Apple Silicon Mac (M1 or later)
  • macOS 14 (Sonoma) or later
  • Xcode 16.4+
  • Rust via rustup
  • XcodeGen (brew install xcodegen)
  • Python 3.8+ (for CLIP model provisioning)

Build from source

git clone https://github.com/ghostwright/shadow.git
cd shadow

# Build Rust storage engine and generate Swift bindings
./scripts/build-rust.sh

# Download CLIP models (~190 MB, one-time)
pip3 install huggingface_hub open_clip_torch
python3 scripts/provision-clip-models.py

# Generate Xcode project and build
cd Shadow && xcodegen generate && cd ..
xcodebuild -project Shadow/Shadow.xcodeproj -scheme Shadow -configuration Debug build

# Launch
open ~/Library/Developer/Xcode/DerivedData/Shadow-*/Build/Products/Debug/Shadow.app

Run tests

# Rust tests (109 tests)
cd shadow-core && cargo test

# Swift tests (1,091 tests)
xcodebuild -project Shadow/Shadow.xcodeproj -scheme Shadow -configuration Debug test

After a rebuild

Every ad-hoc rebuild changes the binary signature, which invalidates macOS TCC permissions. Run the reset script to re-grant:

scripts/reset-permissions.sh

This resets permissions and onboarding state. It does NOT delete captured data at ~/.shadow/data/.

Where Help Is Needed

Testing

  • Different hardware: M1, M2, M3, M4 (different RAM, GPU core counts)
  • Multi-display setups (2+ monitors, hot-plug during recording)
  • Edge cases: sleep/wake cycles, display connect/disconnect, long recording sessions
  • Different permission grant sequences during onboarding

Episode Intelligence

  • Smarter activity boundary detection (currently time + app-switch based)
  • Cross-episode linking (connecting related work across days)
  • Better LLM summaries with fewer hallucinations
  • Episode clustering by project/topic
  • Memory graph construction on top of episodes (connecting people, projects, commitments)
  • Better search ranking and result presentation
  • Faster vector search for large embedding collections

MCP Server

  • Exposing Shadow's knowledge to any AI agent via Model Context Protocol
  • Designing the right tool interface (what queries should be supported)
  • Privacy controls for MCP access (scoping, filtering, consent)

New Capture Tracks

  • Browser extensions for richer DOM/tab context
  • IDE plugins for LSP events (completions, diagnostics, refactors)
  • Clipboard content type detection (code vs text vs URL vs image)

Proactive Analysis

  • Better models for context-switch detection and focus scoring
  • Meeting follow-up tracking (commitments made vs fulfilled)
  • Custom user-defined analysis rules

Documentation

  • Architecture deep-dives for specific subsystems
  • API documentation for the Rust storage engine
  • Guide for building on top of Shadow's data

How to Contribute

Pull Requests

  1. Fork the repo
  2. Create a branch from main (git checkout -b your-feature)
  3. Make your changes
  4. Run both test suites (Rust + Swift) and make sure they pass
  5. Open a PR against main
  6. Tag @mcheemaa for review

What makes a good PR

  • Focused on one thing. Don't mix a bug fix with a feature.
  • Tests included when possible. Shadow has 1,200 tests and we want to keep that bar.
  • Clear description of what changed and why.
  • If it touches the capture pipeline, mention what you tested (which permissions, which hardware).

Code Style

  • Swift 6 strict concurrency. @MainActor for UI and AppDelegate code. Actors for shared mutable state.
  • No force unwraps except in tests.
  • All logging via Logger(subsystem:category:) (stderr), never print().
  • No app-specific hacks. Fixes should be generic.
  • Comments explain WHY, not WHAT.

Recipes and Workflows

If you have learned procedures or workflow patterns that work well, we would love to see them contributed as examples.

Architecture Overview

Shadow/Shadow/           Swift app source
  App/                   AppDelegate, permissions, recording state
  Capture/               Screen, audio, input, window tracking, OCR, CLIP, transcription
  Intelligence/
    Agent/               25-tool runtime, orchestrator, task decomposition
    Context/             Heartbeat, episode synthesis, context store
    Grounding/           AX + VLM grounding oracle, LoRA trainer
    LLM/                 Multi-provider orchestrator, local MLX, cloud providers
    Memory/              Semantic + directive memory stores
    Mimicry/             VisionAgent, procedure executor, safety gates
    Proactive/           Live analyzer, trust tuner, policy engine
    Summary/             Meeting summarization
  UI/                    Menu bar, search, timeline, onboarding, diagnostics, settings

shadow-core/src/         Rust storage engine
  lib.rs                 UniFFI exports
  search.rs              Tantivy full-text search
  timeline.rs            SQLite timeline index
  vector.rs              CLIP vector embeddings
  workflow_extractor.rs  Behavioral pattern extraction
  behavioral.rs          Behavioral sequence search
  retention.rs           3-tier storage lifecycle
  storage.rs             MessagePack event log writer

scripts/                 Build, provisioning, and development tools

Questions?

Open an issue or start a discussion. We are building this in the open and want to hear what you think.