Context7 Documentation Ingestion Plan

Purpose

This document provides a systematic plan for ingesting all core technology documentation via Context7 to prevent iterative debugging issues like we experienced with Cloudflare Stream.

🔴 IMMEDIATE PRIORITY (Ingest Before Next Feature)

1. Remotion - Video Rendering Engine

Why Critical: Core functionality for video composition and rendering

Context7 Commands:

// Step 1: Resolve library ID
mcp__context7__resolve-library-id({
  libraryName: "remotion"
})

// Step 2: Ingest Player documentation
mcp__context7__get-library-docs({
  context7CompatibleLibraryID: "[ID from step 1]",
  topic: "Player API controls configuration",
  tokens: 15000
})

// Step 3: Ingest Lambda rendering
mcp__context7__get-library-docs({
  context7CompatibleLibraryID: "[ID from step 1]",
  topic: "Lambda rendering costs deployment",
  tokens: 15000
})

// Step 4: Ingest dynamic compositions
mcp__context7__get-library-docs({
  context7CompatibleLibraryID: "[ID from step 1]",
  topic: "dynamic compositions props data-driven",
  tokens: 15000
})

// Step 5: Ingest audio/video sync
mcp__context7__get-library-docs({
  context7CompatibleLibraryID: "[ID from step 1]",
  topic: "audio synchronization video timing",
  tokens: 10000
})

Expected Learnings:

Player API and event handling
Lambda function setup and costs
Dynamic composition patterns
Audio/video synchronization techniques
Bundle optimization strategies

Create After Ingestion:

REMOTION_IMPLEMENTATION.md

2. Convex - Real-time Backend

Why Critical: Entire backend infrastructure

Context7 Commands:

// Step 1: Resolve library ID
mcp__context7__resolve-library-id({
  libraryName: "convex real-time database"
})

// Step 2: Ingest query patterns
mcp__context7__get-library-docs({
  context7CompatibleLibraryID: "[ID from step 1]",
  topic: "queries mutations actions patterns",
  tokens: 15000
})

// Step 3: Ingest real-time subscriptions
mcp__context7__get-library-docs({
  context7CompatibleLibraryID: "[ID from step 1]",
  topic: "real-time subscriptions reactivity",
  tokens: 10000
})

// Step 4: Ingest schema design
mcp__context7__get-library-docs({
  context7CompatibleLibraryID: "[ID from step 1]",
  topic: "schema design indexes validation",
  tokens: 10000
})

// Step 5: Ingest authentication
mcp__context7__get-library-docs({
  context7CompatibleLibraryID: "[ID from step 1]",
  topic: "authentication authorization users",
  tokens: 10000
})

Expected Learnings:

When to use query vs mutation vs action
Real-time subscription patterns
Index design for performance
Schema validation strategies
Authentication integration

Create After Ingestion:

CONVEX_PATTERNS.md

3. HLS.js - Video Streaming

Why Critical: Cloudflare Stream video playback

Context7 Commands:

// Step 1: Resolve library ID
mcp__context7__resolve-library-id({
  libraryName: "hls.js"
})

// Step 2: Ingest player configuration
mcp__context7__get-library-docs({
  context7CompatibleLibraryID: "[ID from step 1]",
  topic: "player configuration initialization",
  tokens: 10000
})

// Step 3: Ingest error handling
mcp__context7__get-library-docs({
  context7CompatibleLibraryID: "[ID from step 1]",
  topic: "error handling recovery retry",
  tokens: 10000
})

Expected Learnings:

Player initialization and configuration
Error recovery strategies
Quality level switching
Live streaming support

Create After Ingestion:

Add to CLOUDFLARE_STREAM_IMPLEMENTATION.md

🟡 MEDIUM PRIORITY (Before AI Features)

4. Anthropic Claude SDK

Context7 Commands:

mcp__context7__resolve-library-id({
  libraryName: "anthropic claude"
})

mcp__context7__get-library-docs({
  context7CompatibleLibraryID: "[ID]",
  topic: "streaming messages function calling",
  tokens: 15000
})

Expected Learnings:

Streaming API patterns
Function/tool calling
Token counting
Error handling

5. Google Generative AI (Gemini)

Context7 Commands:

mcp__context7__resolve-library-id({
  libraryName: "google generative ai gemini"
})

mcp__context7__get-library-docs({
  context7CompatibleLibraryID: "[ID]",
  topic: "gemini flash API streaming",
  tokens: 10000
})

Expected Learnings:

Gemini Flash configuration
Cost optimization
Streaming responses
Function calling

6. OpenAI SDK

Context7 Commands:

mcp__context7__resolve-library-id({
  libraryName: "openai"
})

mcp__context7__get-library-docs({
  context7CompatibleLibraryID: "[ID]",
  topic: "chat completions streaming function calling",
  tokens: 15000
})

Expected Learnings:

Chat completions API
Streaming responses
Function calling patterns
Cost optimization

Create After Ingestion:

DEDALUS_INTEGRATION.md (multi-model routing)

7. React Dropzone

Context7 Commands:

mcp__context7__resolve-library-id({
  libraryName: "react-dropzone"
})

mcp__context7__get-library-docs({
  context7CompatibleLibraryID: "[ID]",
  topic: "drag drop file validation",
  tokens: 8000
})

Expected Learnings:

Accept configuration
File validation
Multiple file handling
Custom styling

🟢 LOWER PRIORITY (As Needed)

8. Playwright Testing

Context7 Commands:

mcp__context7__resolve-library-id({
  libraryName: "playwright"
})

mcp__context7__get-library-docs({
  context7CompatibleLibraryID: "[ID]",
  topic: "page object model async testing",
  tokens: 10000
})

Create After Ingestion:

TESTING_STRATEGY.md

9. Tailwind CSS

Context7 Commands:

mcp__context7__resolve-library-id({
  libraryName: "tailwind css"
})

mcp__context7__get-library-docs({
  context7CompatibleLibraryID: "[ID]",
  topic: "custom configuration dark mode",
  tokens: 8000
})

📊 Ingestion Tracking

Completed ✅

Cloudflare Stream (TUS uploads, HLS streaming)
Cloudflare R2 (object storage)
Next.js (App Router, environment variables)
TUS Protocol (resumable uploads)

In Progress 🔄

None currently

Planned 📋

🎯 Ingestion Workflow Template

For each technology:

1. Pre-Ingestion

## Technology: [Name]

### Why We Need This
- [Use case 1]
- [Use case 2]
- [Use case 3]

### Current Knowledge Gaps
- [ ] Gap 1
- [ ] Gap 2
- [ ] Gap 3

### Features We'll Implement
- [ ] Feature 1
- [ ] Feature 2
- [ ] Feature 3

2. Ingestion

// Resolve library ID
const result = await mcp__context7__resolve-library-id({
  libraryName: "technology name"
});

// Ingest documentation with specific topics
const docs = await mcp__context7__get-library-docs({
  context7CompatibleLibraryID: result.id,
  topic: "specific feature or API",
  tokens: 10000-15000
});

3. Analysis

## Key Learnings

### API Patterns
- [Pattern 1 with code example]
- [Pattern 2 with code example]

### Configuration Requirements
- [Requirement 1]
- [Requirement 2]

### Common Pitfalls
- [Pitfall 1 and how to avoid]
- [Pitfall 2 and how to avoid]

### Best Practices
- [Practice 1]
- [Practice 2]

4. Implementation Plan

## Implementation Checklist

### Prerequisites
- [ ] Environment variables needed
- [ ] Dependencies installed
- [ ] Configuration files updated

### Implementation Steps
- [ ] Step 1 based on docs
- [ ] Step 2 based on docs
- [ ] Step 3 based on docs

### Testing Plan
- [ ] Happy path test
- [ ] Error case tests
- [ ] Edge case tests

5. Documentation

## [TECHNOLOGY]_IMPLEMENTATION.md

Created with:
- Architecture explanation
- Configuration guide
- Code examples from official docs
- Troubleshooting section
- Testing checklist
- References to Context7 docs

🚨 Critical Reminder

BEFORE implementing ANY feature that uses one of these technologies:

Check if we've ingested docs (see tracking above)
If not ingested, DO IT FIRST
Read the ingested docs completely
Base implementation on official examples
Document the implementation

Example: Before implementing Remotion Player:

// ❌ DON'T start coding immediately
// ✅ DO ingest Remotion docs first

// Step 1: Resolve library ID
const remotionLibs = await mcp__context7__resolve-library-id({
  libraryName: "remotion"
});

// Step 2: Get Player documentation
const playerDocs = await mcp__context7__get-library-docs({
  context7CompatibleLibraryID: remotionLibs.id,
  topic: "Player API controls events",
  tokens: 15000
});

// Step 3: Read and understand
// Step 4: Implement based on examples
// Step 5: Document in REMOTION_IMPLEMENTATION.md

📈 Success Metrics

For Each Ingestion

Library ID resolved successfully
Documentation fetched (10k-15k tokens)
Key patterns identified
Configuration requirements documented
Code examples extracted
Implementation markdown created
Added to DOCUMENTATION_LIBRARY.md

Overall Progress

Target: 15 technologies documented
Completed: 4 (Cloudflare Stream, R2, Next.js, TUS)
Remaining: 11
Completion: 27%

🎓 Lessons Applied

Based on Cloudflare Stream experience:

Don't assume - Official docs show exact patterns
Don't guess - Configuration structure is documented
Don't iterate - Complete understanding prevents bugs
Don't skip - Every technology deserves Context7 ingestion

📅 Suggested Timeline

Week 1: Video Core (Critical Path)

Day 1: Remotion (Player)
Day 2: Remotion (Lambda + Dynamic)
Day 3: HLS.js
Day 4: Convex (Queries/Mutations/Actions)
Day 5: Convex (Real-time + Auth)

Week 2: AI Integration

Day 1: Anthropic Claude SDK
Day 2: Google Generative AI
Day 3: OpenAI SDK
Day 4: Create DEDALUS_INTEGRATION.md
Day 5: Buffer/catch-up

Week 3: Polish & Testing

Day 1: React Dropzone
Day 2: Playwright
Day 3: Tailwind CSS
Day 4: Create TESTING_STRATEGY.md
Day 5: Final documentation review

🔗 Related Documents

DOCUMENTATION_LIBRARY.md - List of all available documentation
DEVELOPMENT_WORKFLOW.md - How to use documentation in development
CLAUDE.md - Standing orders including documentation-first approach
CLOUDFLARE_STREAM_IMPLEMENTATION.md - Example of post-ingestion documentation

Remember: Every hour spent reading documentation saves 10 hours of debugging.

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Context7 Documentation Ingestion Plan

Purpose

🔴 IMMEDIATE PRIORITY (Ingest Before Next Feature)

1. Remotion - Video Rendering Engine

2. Convex - Real-time Backend

3. HLS.js - Video Streaming

🟡 MEDIUM PRIORITY (Before AI Features)

4. Anthropic Claude SDK

5. Google Generative AI (Gemini)

6. OpenAI SDK

7. React Dropzone

🟢 LOWER PRIORITY (As Needed)

8. Playwright Testing

9. Tailwind CSS

📊 Ingestion Tracking

Completed ✅

In Progress 🔄

Planned 📋

🎯 Ingestion Workflow Template

1. Pre-Ingestion

2. Ingestion

3. Analysis

4. Implementation Plan

5. Documentation

🚨 Critical Reminder

📈 Success Metrics

For Each Ingestion

Overall Progress

🎓 Lessons Applied

📅 Suggested Timeline

Week 1: Video Core (Critical Path)

Week 2: AI Integration

Week 3: Polish & Testing

🔗 Related Documents