Co-Pilot / 辅助式
更新于 a month ago

continuous-claude-v3

Pparcadei
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parcadei/continuous-claude-v3
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💡 摘要

Continuous Claude 是一个多代理开发环境,增强了编码会话中的学习和上下文保留。

🎯 适合人群

寻求高效编码工具的软件开发人员需要上下文感知编码帮助的数据科学家协调复杂工作流程的项目经理管理部署的DevOps工程师探索多代理系统的AI研究人员

🤖 AI 吐槽:看起来很能打,但别让配置把人劝退。

安全分析中风险

风险:Medium。建议检查:是否执行 shell/命令行指令;是否发起外网请求(SSRF/数据外发);API Key/Token 的获取、存储与泄露风险;文件读写范围与路径穿越风险。以最小权限运行,并在生产环境启用前审计代码与依赖。

Continuous Claude

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

License: MIT Claude Code Skills Agents Hooks

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?

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: The pyproject.toml is in opc/. Always run uv commands from the opc/ directory.

What the Wizard Does

| Step | What It Does | |------|--------------| | 1 | Backup existing .claude/ config (if present) | | 2 | Check prerequisites (Docker, Python, uv) | | 3-5 | Database + API key configuration | | 6-7 | Start Docker stack, run migrations | | 8 | Install Claude Code integration (32 agents, 109 skills, 30 hooks) | | 9 | Math features (SymPy, Z3, Pint - optional) | | 10 | TLDR code analysis tool | | 11-12 | Diagnostics tools + Loogle (optional) |

To Uninstall:

cd Continuous-Claude-v3/opc
  uv run python -m scripts.setup.wizard --uninstall

What it does

  1. Archives your current setup → Moves ~/.claude to ~/.claude-v3.archived.
  2. Restores your backup → Finds the most recent ~/.claude.backup.* (created during install) and restores it
  3. Preserves user data → Copies these back from the archive:
  • history.jsonl (your command history)
  • mcp_config.json (MCP servers)
  • .env (API keys)
  • projects.json (project configs)
  • file-history/ directory
  • projects/ directory
  1. Removes CC-v3 additions → Everything else (hooks, skills, agents, rules)

Safety Features

  • Your current setup is archived with timestamp - nothing gets deleted
  • The wizard asks for confirmation before proceeding
  • It restores from the backup that was made during installation
  • All your Claude Code settings stay intact

Remote Database Setup

By default, CC-v3 runs PostgreSQL locally via Docker. For remote database setups:

1. Database Preparation

# Connect to your remote PostgreSQL instance psql -h hostname -U user -d continuous_claude # Enable pgvector extension (requires superuser or rds_superuser) CREATE EXTENSION IF NOT EXISTS vector; # Apply the schema (from your local clone) psql -h hostname -U user -d continuous_claude -f docker/init-schema.sql

Managed PostgreSQL tips:

  • AWS RDS: Add vector to shared_preload_libraries in DB Parameter Group
  • Supabase: Enable via Database Extensions page
  • Azure Database: Use Extensions pane to enable pgvector

2. Connection Configuration

Set CONTINUOUS_CLAUDE_DB_URL in ~/.claude/settings.json:

{ "env": { "CONTINUOUS_CLAUDE_DB_URL": "postgresql://user:password@hostname:5432/continuous_claude" } }

Or export before running Claude:

export CONTINUOUS_CLAUDE_DB_URL="postgresql://user:password@hostname:5432/continuous_claude" claude

See .env.example for all available environment variables.

First Session

# Start Claude Code claude # Try a workflow > /workflow

First Session Commands

| Command | What it does | |---------|--------------| | /workflow | Goal-based routing (Research/Plan/Build/Fix) | | /fix bug <description> | Investigate and fix a bug | | /build greenfield <feature> | Build a new feature from scratch | | /explore | Understand the codebase | | /premortem | Risk analysis before implementation |


Architecture

┌─────────────────────────────────────────────────────────────────────┐
│                        CONTINUOUS CLAUDE                            │
├─────────────────────────────────────────────────────────────────────┤
│                                                                     │
│  ┌─────────────┐    ┌─────────────┐    ┌─────────────┐             │
│  │   Skills    │    │   Agents    │    │    Hooks    │             │
│  │   (109)     │───▶│    (32)     │◀───│    (30)     │             │
│  └─────────────┘    └
五维分析
清晰度8/10
创新性8/10
实用性9/10
完整性8/10
可维护性7/10
优缺点分析

优点

  • 增强会话间的上下文保留
  • 提供多种技能和代理以应对多样化任务
  • 通过智能分析减少令牌使用

缺点

  • 需要Docker和特定的Python版本
  • 对于初学者来说,设置过程可能复杂
  • 对外部服务的依赖以实现完整功能

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免责声明:本内容来源于 GitHub 开源项目,仅供展示和评分分析使用。

版权归原作者所有 parcadei.