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.

Table of Contents

• Why Continuous Claude?
• Design Principles
• How to Talk to Claude
• Quick Start
• Architecture
• Core Systems
  • Skills (109)
  • Agents (32)
  • Hooks (30)
  • TLDR Code Analysis
  • Memory System
  • Continuity System
  • Math System
• 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?"	/premortem
"Help me figure out what I need"	/discovery-interview
"Done for today"	create_handoff (critical)
"Resume where we left off"	resume_handoff
"Research auth patterns"	oracle agent + perplexity
"Find all usages of this API"	scout agent + ast-grep

Why This Approach?

Benefit	How
More Discoverable	Don't need to know commands exist
Context-Aware	System knows when you're 90% through context
Reduces Cognitive Load	Describe intent naturally, get curated suggestions
Power User Friendly	Still supports /fix, /build, etc. directly

Skill vs Workflow vs Agent

Type	Purpose	Example
Skill	Single-purpose tool	commit, tldr-code, qlty-check
Workflow	Multi-step process	/fix (sleuth → premortem → kraken → commit)
Agent	Specialized sub-session	scout (exploration), oracle (research)

See detailed skill activation docs →

---

Quick Start

Prerequisites

• Python 3.11+
• uv package manager
• Docker (for PostgreSQL)
• Claude Code CLI

Installation

# Clone
git clone https://github.com/parcadei/Continuous-Claude-v3.git
cd Continuous-Claude-v3/opc

# Run setup wizard (12 steps)
uv run python -m scripts.setup.wizard

Note: