Real-World Use Cases

See how ContextDigger solves everyday challenges

ContextDigger is not another assistant. It is the context governance layer underneath your existing AI tools.

Every use case on this page follows the same pattern: set a Context Aperture (typically f/15 = 15 files), respect an Attention Budget (3,000 lines), and work through governed context bundles like .cdg/context/*.txt instead of unbounded repo scans.

Featured Lab

Hands-on: add rate limiting to a checkout API and compare AI behavior with and without governed context bundles.

Open the Checkout API Rate Limiting Lab →

💡 How ContextDigger Works

1️⃣ Terminal (CDG CLI)

Run commands to organize code and create context files:

$ cdg init $ cdg dig backend-api ✓ Created: .cdg/context/backend-api.txt

2️⃣ Your AI Tool

Prompt AI to read context and ask questions:

💬 "Read .cdg/context/backend-api.txt and add validation to User model"
💻

❌ Problem: "Where was I working?"

Daily context switching makes you lose your place

✅ Solution: Resume Exactly Where You Left Off

💻
Terminal (CDG CLI)
$ cdg dashboard
→ See what you worked on yesterday
$ cdg dig backend-api
✓ Created: .cdg/context/backend-api.txt
↓ Prompt AI ↓
🤖
AI Tool (Claude / Cursor / ChatGPT / Copilot)
Your prompt:
"Read .cdg/context/backend-api.txt and add validation to the User model for email uniqueness"
💡 The AI reads the file directly and understands your codebase context

Result: No more "where was I?" moments. Your entire day's context is preserved, navigable, and shareable.

👥

❌ Problem: "How do I learn this codebase?"

New developers take months to be productive

✅ Solution: Onboard in Days, Not Months

💻
Terminal (CDG CLI)
$ cdg init
→ Auto-discover all code areas
$ cdg dashboard
→ See project overview
$ cdg dig backend-api
✓ Created: .cdg/context/backend-api.txt
↓ Prompt AI ↓
🤖
AI Tool (Claude / Cursor / ChatGPT / Copilot)
Your prompt:
"Read .cdg/context/backend-api.txt and explain how authentication works in this codebase"
💡 The AI reads the file directly and understands your codebase context

Result: New developers understand the codebase structure in days instead of months. Team knowledge is preserved and accessible.

🔧

❌ Problem: "What will break if I change this?"

Fear of breaking things during refactoring

✅ Solution: Refactor with Confidence

💻
Terminal (CDG CLI)
$ cdg dig authentication
✓ Created: .cdg/context/authentication.txt
↓ Prompt AI ↓
🤖
AI Tool (Claude / Cursor / ChatGPT / Copilot)
Your prompt:
"Read .cdg/context/authentication.txt and tell me what dependencies would break if I refactor the login flow"
💡 The AI reads the file directly and understands your codebase context

Result: Complete refactoring with full traceability. Every decision documented, every change tracked, zero knowledge lost.

🐛

❌ Problem: "Where is this bug coming from?"

Tracing issues across the codebase is hard

✅ Solution: Trace Bugs with Full Context

💻
Terminal (CDG CLI)
$ cdg mark-spot bug-location
$ cdg dig payment-flow
✓ Created: .cdg/context/payment-flow.txt
↓ Prompt AI ↓
🤖
AI Tool (Claude / Cursor / ChatGPT / Copilot)
Your prompt:
"Read .cdg/context/payment-flow.txt and explain why the payment amount is showing null"
💡 The AI reads the file directly and understands your codebase context

Result: Trace issues with full context. AI gives exact file paths, line numbers, and root cause analysis based on your codebase.

📊

❌ Problem: "What files does this change affect?"

Missing context during code reviews

✅ Solution: Review with Full Context

💻
Terminal (CDG CLI)
$ cdg dig user-management
✓ Created: .cdg/context/user-management.txt
↓ Prompt AI ↓
🤖
AI Tool (Claude / Cursor / ChatGPT / Copilot)
Your prompt:
"Read .cdg/context/user-management.txt and analyze the impact of this PR's changes on user management"
💡 The AI reads the file directly and understands your codebase context

Result: Review changes with full context. AI identifies dependencies, potential issues, and suggests improvements.

🎯

❌ Problem: "Where should I add this feature?"

Planning features without full context

✅ Solution: Plan Features with AI Guidance

💻
Terminal (CDG CLI)
$ cdg dig notifications
✓ Created: .cdg/context/notifications.txt
↓ Prompt AI ↓
🤖
AI Tool (Claude / Cursor / ChatGPT / Copilot)
Your prompt:
"Read .cdg/context/notifications.txt and tell me where I should add push notification support"
💡 The AI reads the file directly and understands your codebase context

Result: Plan features with AI guidance. Get exact file locations, integration points, and implementation suggestions.

📚

❌ Problem: "What code areas need docs?"

Maintaining documentation is challenging

✅ Solution: AI-Powered Documentation

💻
Terminal (CDG CLI)
$ cdg dig database-layer
✓ Created: .cdg/context/database-layer.txt
↓ Prompt AI ↓
🤖
AI Tool (Claude / Cursor / ChatGPT / Copilot)
Your prompt:
"Read .cdg/context/database-layer.txt and generate comprehensive documentation for this database layer"
💡 The AI reads the file directly and understands your codebase context

Result: Auto-generate wikis and maintain team knowledge. AI creates accurate docs based on actual code structure.

⚡

❌ Problem: "Which files are performance hotspots?"

Finding optimization targets is difficult

✅ Solution: Identify and Optimize Hotspots

💻
Terminal (CDG CLI)
$ cdg dig api-endpoints
✓ Created: .cdg/context/api-endpoints.txt
↓ Prompt AI ↓
🤖
AI Tool (Claude / Cursor / ChatGPT / Copilot)
Your prompt:
"Read .cdg/context/api-endpoints.txt and identify performance bottlenecks with optimization suggestions"
💡 The AI reads the file directly and understands your codebase context

Result: Find hotspots and optimize with AI guidance. Track optimization progress and measure impact.

🔍

❌ Problem: "Where are our coverage gaps?"

Identifying and prioritizing technical debt

✅ Solution: Systematic Debt Tracking

💻
Terminal (CDG CLI)
$ cdg dig legacy-code
✓ Created: .cdg/context/legacy-code.txt
↓ Prompt AI ↓
🤖
AI Tool (Claude / Cursor / ChatGPT / Copilot)
Your prompt:
"Read .cdg/context/legacy-code.txt and identify technical debt with prioritized cleanup tasks"
💡 The AI reads the file directly and understands your codebase context

Result: Identify gaps, prioritize improvements, and track cleanup efforts systematically.

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