CLAUDE CODE ECOSYSTEM
AI DEVELOPMENT WORKFLOW SYSTEM
THE STORY
I'm an optimizer and I love making my own workflows faster, better, stronger. I discovered Claude pretty early on and discovered how customizable it was, especially with subagents. These subagents really blew me away because I almost immediately realized that you could section off context in a subagent. This led me down a path of building an entire ecosystem of tools: .claude (478 stars), klaude (a wrapper with async + recursive agents), MCP Boilerplate (78 stars), and PDOCS CLI for spec-driven development. The core philosophy: research 10 times, plan 5 times, execute once—because fixing a bad execution is almost impossible.
QUICK STATS
DEVELOPMENT INSIGHTS
"Speed is really important and long-running tasks are really important. If a task only runs for two minutes, that's enough time to get distracted. However, if the task can run for 15 minutes straight, that's enough to get into flow state and be productive on another project. Spec-driven development is the future—these agents, if you give them enough planning and specs, will execute perfectly. Measure three times, plan twice, execute once. All the inefficiency comes from debugging poor executions, which are a lot harder to debug than just a plan document."
TECHNICAL CHALLENGES
Agent Checkout System (Klaude)
The big innovation was allowing interactive subagents without burning parent context. Requirements gathering needs to be interactive—it needs to ask you questions. But you want it done by subagents so you're not burning context. Because Claude Code is a child process of the wrapper, it can be killed by the parent process, which can then run 'claude code --resume' on a different session ID. From the user's perspective, your terminal UI is killed and then suddenly a new one appears with the subagent interface.
Async Tool Calls (Pre-Hooks)
Built this before hooks existed. Tool calls were asynchronous—when they fired off, a receipt was returned with a tool ID. But the actual response could come back much later as a user message. I had a queue of messages that would get filled up as tool calls completed. This enabled deep research in ~2 minutes instead of 10+ minutes by parallelizing web searches and summarization across multiple subagents.
MCP vs CLI Decision Framework
The problem with MCPs is that with every single message, all instructions for how to use those MCP servers are included—that's 5,000 to 10,000 tokens, burning through credits and lowering output quality. The solution: CLIs with markdown instruction files you tag in when referenced. Without the reference document, the LLM uses the help command. Only burns context the few times you use it rather than every conversation.
KEY FEATURES
- ✦Recursive Agent DelegationAgents can create other subagents with custom prompts and tool collections
- ✦Async ExecutionFire off long-running tasks and continue working while they complete
- ✦Spec-Driven Development (PDOCS)YAML-based documentation system for feature specs and API contracts
- ✦Multi-Model RoutingRoute to different LLMs (Claude, reasoning models) based on task complexity
- ✦Observability & TracingSQLite database tracks all agent sessions, logs, and system responses
- ✦Git Hooks IntegrationAuto-generate CLAUDE.md files after commits for self-documenting repos
IMPACT & RESULTS
- •478 GitHub stars on .claude repository
- •78 GitHub stars on MCP Boilerplate
- •Community adoption demonstrates validation of approach
- •Enables 15+ minute autonomous agent runs (vs 2-minute typical)
- •Greenfield projects: 'Much longer requirements phase, then fire off execution commands for entire features with very little oversight'
RELATED WORK
Check out Mystica and Mercury Notes for related projects