Continuous Claude

A persistent, learning, multi-agent development environment built on Claude Code

Continuous Claude transforms Claude Code into a continuously learning system that maintains context across sessions, orchestrates specialized agents, and eliminates wasting tokens through intelligent code analysis.

Why Continuous Claude?
Design Principles
How to Talk to Claude
Quick Start
Architecture
Core Systems
Workflows
Installation
Updating
Configuration
Contributing
License

Why Continuous Claude?

Claude Code has a compaction problem: when context fills up, the system compacts your conversation, losing nuanced understanding and decisions made during the session.

Continuous Claude solves this with:

Problem	Solution
Context loss on compaction	YAML handoffs - more token-efficient transfer
Starting fresh each session	Memory system recalls + daemon auto-extracts learnings
Reading entire files burns tokens	5-layer code analysis + semantic index
Complex tasks need coordination	Meta-skills orchestrate agent workflows
Repeating workflows manually	109 skills with natural language triggers

The mantra: Compound, don't compact. Extract learnings automatically, then start fresh with full context.

Why "Continuous"? Why "Compounding"?

The name is a pun. Continuous because Claude maintains state across sessions. Compounding because each session makes the system smarter—learnings accumulate like compound interest.

Design Principles

An agent is five things: Prompt + Tools + Context + Memory + Model.

Component	What We Optimize
Prompt	Skills inject relevant context; hooks add system reminders
Tools	TLDR reduces tokens; agents parallelize work
Context	Not just what Claude knows, but how it's provided
Memory	Daemon extracts learnings; recall surfaces them
Model	Becomes swappable when the other four are solid

Anti-Complexity

We resist plugin sprawl. Every MCP, subscription, and tool you add promises improvement but risks breaking context, tools, or prompts through clashes.

Our approach:

Time, not money — No required paid services. Perplexity and NIA are optional, high-value-per-token.
Learn, don't accumulate — A system that learns handles edge cases better than one that collects plugins.
Shift-left validation — Hooks run pyright/ruff after edits, catching errors before tests.

The failure modes of complex systems are structurally invisible until they happen. A learning, context-efficient system doesn't prevent all failures—but it recovers and improves.

How to Talk to Claude

You don't need to memorize slash commands. Just describe what you want naturally.

The Skill Activation System

When you send a message, a hook injects context that tells Claude which skills and agents are relevant. Claude infers from a rule-based system and decides which tools to use.

> "Fix the login bug in auth.py"

🎯 SKILL ACTIVATION CHECK
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

⚠️ CRITICAL SKILLS (REQUIRED):
  → create_handoff

📚 RECOMMENDED SKILLS:
  → fix
  → debug

🤖 RECOMMENDED AGENTS (token-efficient):
  → debug-agent
  → scout

ACTION: Use Skill tool BEFORE responding
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Priority Levels

Level	Meaning
⚠️ CRITICAL	Must use (e.g., handoffs before ending session)
📚 RECOMMENDED	Should use (e.g., workflow skills)
💡 SUGGESTED	Consider using (e.g., optimization tools)
📌 OPTIONAL	Nice to have (e.g., documentation helpers)

Natural Language Examples

What You Say	What Activates
"Fix the broken login"	`/fix` workflow → debug-agent, scout
"Build a user dashboard"	`/build` workflow → plan-agent, kraken
"I want to understand this codebase"	`/explore` + scout agent
"What could go wrong with this plan?"	`/p

...

parcadei/continuous-claude-v3

README

Continuous Claude

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