math
🎯Skillfrom parcadei/continuous-claude-v3
|math skill from parcadei/continuous-claude-v3
Installation
npx skills add https://github.com/parcadei/continuous-claude-v3 --skill mathSkill Details
Overview
# Continuous Claude
> A persistent, learning, multi-agent development environment built on Claude Code
[](LICENSE)
[](https://claude.ai/code)
[](#skills-system)
[](#agents-system)
[](#hooks-system)
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?](#why-continuous-claude)
- [Design Principles](#design-principles)
- [How to Talk to Claude](#how-to-talk-to-claude)
- [Quick Start](#quick-start)
- [Architecture](#architecture)
- [Core Systems](#core-systems)
- [Skills (109)](#skills-system)
- [Agents (32)](#agents-system)
- [Hooks (30)](#hooks-system)
- [TLDR Code Analysis](#tldr-code-analysis)
- [Memory System](#memory-system)
- [Continuity System](#continuity-system)
- [Math System](#math-system)
- [Workflows](#workflows)
- [Installation](#installation)
- [Updating](#updating)
- [Configuration](#configuration)
- [Contributing](#contributing)
- [License](#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 b
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