Task.md

Task Tracker - Prompt Cache Optimizer

Current Sprint: Project Initialization

✅ Completed Tasks

• Created project directory structure
• Created AGENT.md with comprehensive project documentation
• Created Task.md for task tracking

🚧 In Progress

• Initialize Git repository
• Create GitHub repository
• Set up project structure (folders, files)
• Create README.md
• Create basic Python package structure

📋 Backlog - Phase 1: MVP (Weeks 1-4)

Week 1-2: Core Analysis Engine

• Set up Python project with Poetry/pip
• Create analyzer/ module
  • metrics.py: Token counting and cache metrics
  • static.py: Static code analysis for prompt patterns
  • runtime.py: Runtime API call interception
• Write tests for analyzer module
• Create simple CLI with typer
  • cache-optimizer analyze <directory> command
• Add support for Anthropic SDK integration

Week 3: Optimization Engine

• Create optimizer/ module
  • restructure.py: Prompt restructuring logic
  • strategies.py: Different optimization strategies
  • validator.py: Validate optimized prompts
• Implement cache boundary detection algorithm
• Implement prompt reordering algorithm
• Write comprehensive tests
• CLI: cache-optimizer optimize <file> command

Week 4: Savings Calculator & Demo

• Create calculator/ module for cost analysis
• Build before/after comparison tool
• Generate visual reports (text-based for MVP)
• Create example notebooks in /examples
• Test on real-world prompts
• Document findings in blog post draft

📋 Backlog - Phase 2: Proof of Value (Weeks 5-6)

Week 5: Real-World Testing

• Analyze 5-10 real projects (own or open source)
• Collect actual savings data
• Create case study with real numbers
• Take screenshots of API bills before/after
• Draft blog post: "I saved $X/month with this tool"

Week 6: Launch Preparation

• Polish README with animated demos
• Create detailed documentation site (MkDocs or similar)
• Record demo video
• Prepare HN/Reddit posts
• Submit to AI newsletters
• Create Twitter account and thread
• GitHub Actions integration for CI/CD

📋 Backlog - Phase 3: Integrations (Weeks 7-8)

Week 7: Framework Adapters

• Create integrations/ module
• LangChain adapter
  • OptimizedChatAnthropic
  • OptimizedChatOpenAI
• LlamaIndex adapter
• Add integration tests
• Create example code for each integration

Week 8: OpenAI Support

• OpenAI SDK integration
• Test with GPT-4 prompt caching
• Update documentation
• Create comparison chart (Anthropic vs OpenAI caching)

📋 Backlog - Phase 4: Monitoring (Weeks 9-12)

Week 9-10: Background Monitoring

• Create monitoring/ module
• Background agent to track cache hits
• SQLite for local storage
• Real-time metrics collection
• CLI: cache-optimizer monitor start/stop/status

Week 11-12: Dashboard MVP

• Simple Streamlit dashboard
• Real-time cache hit rate display
• Historical cost tracking
• Optimization suggestions
• Alert system for efficiency drops

📋 Backlog - Phase 5: Scale (Month 4+)

Paid Features

• User authentication system
• Team collaboration features
• Cloud-hosted dashboard (React + FastAPI)
• PostgreSQL backend
• Billing integration (Stripe)
• Enterprise SSO

Community & Growth

• Create Discord server
• Weekly office hours
• Contributor guidelines
• First conference talk proposal
• Partnership with Anthropic/OpenAI

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Immediate Next Steps (Today)

1. Initialize Git and GitHub

   • Initialize git repo
   • Create GitHub repository
   • Push initial commit

2. Set up Python project structure

   • Create pyproject.toml
   • Set up virtual environment
   • Create basic package structure
   • Add .gitignore

3. Create README.md

   • Project description
   • Quick start guide
   • Installation instructions
   • Basic usage examples

4. First commit and push

   • Commit all files
   • Push to GitHub
   • Add topics/tags to repo

---

Decisions Log

2026-01-24: Project Initialization

• Decision: Use Python as primary language
• Rationale: AI developers are familiar with Python, easier adoption
• Decision: CLI-first approach with dashboard later
• Rationale: Developers love CLI tools, faster to build, easier to integrate

Future Decisions

• Framework choice: Poetry vs pip (TBD)
• Dashboard: Streamlit vs React (Start with Streamlit)
• Testing: pytest (confirmed)
• Docs: MkDocs or Sphinx (TBD)

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Metrics to Track

GitHub Metrics

• Stars: Target 100 in Week 1
• Forks: Quality indicator
• Issues: Community engagement
• Contributors: Aim for 5+ in Month 1

User Metrics

• CLI downloads (PyPI)
• Active users (telemetry opt-in)
• Average savings per user
• NPS score

Business Metrics

• Newsletter signups
• Trial starts
• Conversion rate
• MRR
• Churn rate

---

Resources Needed

Immediate

• Project idea and direction ✓
• Architecture design ✓
• GitHub account access
• Development environment

Soon

• Domain name (promptcache.dev?)
• Twitter/X account
• Logo/branding
• Analytics setup (Plausible/PostHog)

Later

• Hosting (Vercel/Railway for dashboard)
• Database (PostgreSQL on Supabase/Railway)
• Email service (for alerts)
• Payment processing (Stripe)

---

Notes

• Keep scope minimal for MVP - ship fast
• Focus on developer experience
• Show real cost savings with numbers
• Test on real projects before launch
• Document everything with examples
• Community is key for GitHub stars

Last updated: 2026-01-24