refactor: move all remaining files to orig/ directory

Completed clean root directory structure:
- Root now contains only: .git, .env, docs/, orig/
- Moved all remaining files and directories to orig/:
  - Config files (.claude, .dockerignore, .drone.yml, etc.)
  - All .env variants (except active .env)
  - Git config (.gitconfig, .github, .gitignore, etc.)
  - Tool configs (.golangci.yml, .revive.toml, etc.)
  - Documentation (*.md files, @prompts)
  - Build files (Dockerfiles, Makefile, go.mod, go.sum)
  - Docker compose files
  - All source directories (scripts, tests, tools, etc.)
  - Runtime directories (logs, monitoring, reports)
  - Dependency files (node_modules, lib, cache)
  - Special files (--delete)

- Removed empty runtime directories (bin/, data/)

V2 structure is now clean:
- docs/planning/ - V2 planning documents
- orig/ - Complete V1 codebase preserved
- .env - Active environment config (not in git)

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

orig/@prompts/performance-optimization.md

@@ -0,0 +1,26 @@
+You are an expert in Go performance optimization and high-frequency trading systems. I'm building an MEV bot in Go that needs to process thousands of transactions per second with minimal latency.
+
+I need help with:
+
+1. Optimizing data structures for high-frequency access
+2. Minimizing memory allocations and garbage collection
+3. Implementing efficient caching strategies
+4. Optimizing network I/O for RPC calls
+5. Parallel processing of transactions
+6. Profiling and benchmarking techniques
+
+Please provide production-ready Go code that:
+- Implements lock-free or low-lock data structures where appropriate
+- Minimizes memory allocations through object pooling
+- Uses efficient algorithms for data processing
+- Implements caching for frequently accessed data
+- Handles concurrency properly
+- Follows Go best practices
+- Includes comprehensive comments
+
+The code should:
+- Process transactions with minimal latency
+- Scale efficiently across multiple CPU cores
+- Handle backpressure gracefully
+- Provide metrics for performance monitoring
+- Include benchmarks for critical functions