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Coding Apache-2.0 ● Score: 86/100 Agent Skill Harness (Stdio)

Caveman

🪨 why use many token when few token do trick. Viral skill + proxy for coding agents that cuts 65% of tokens by talking like a caveman.

Category: Coding
Runtime: Python 3.10+
License: Apache-2.0

At a Glance Technical Specifications

Protocol MCP / Stdio
Runtime Python 3.10+
Client Support Claude / Cursor
Execution Local Subprocess
License Apache-2.0
Popularity 86/100
Security Allowlist Scoped
Status Verified Clean
Visual setup and architecture guide for Caveman

01 // How It Works & Runtime Architecture

By leveraging Python SDK / OpenAI API, the application isolates runtime dependencies while maintaining low-latency execution traces across multi-step LLM operations.

Deterministic Execution Pipeline Local Stdio Protocol
AI Client (Claude / Cursor)
→
Caveman Server
→
Scoped Local Tools
→
Sanitized Agent Memory

02 // Real-World Workflows & Use Cases

01

Deterministic AST code analysis, symbol indexing, and cross-file reference discovery

02

Autonomous test generation, test execution feedback loops, and automated patch creation

03

Safe repository refactoring and automated codebase modernization without context leakage

03 // Prerequisites & Compatibility

✓
Runtime Environment
Python 3.10+ installed and available in system PATH
✓
Compatible Client
Claude Desktop, Cursor, Windsurf, Claude Code CLI, or OpenCode
✓
Directory Permissions
Explicit filesystem read/write access to designated directories
✓
License Compliance
Verified permissive Apache-2.0 open-source license

04 // Client Configuration (claude_desktop_config.json) 

{
  "mcpServers": {
    "caveman": {
      "command": "python",
      "args": [
        "-m",
        "caveman"
      ]
    }
  }
}

Paste into your client configuration file (e.g. ~/Library/Application Support/Claude/claude_desktop_config.json on macOS or %APPDATA%/Claude/claude_desktop_config.json on Windows).

05 // Step-by-Step Installation

Verified Process
  1. 1

    Clone JuliusBrussee/caveman from GitHub

  2. 2

    Install dependencies with pip install -r requirements.txt (or uv sync)

  3. 3

    Copy .env.example to .env and add your API keys

  4. 4

    Run the skill server and connect your AI client via stdio or HTTP

06 // Execution Boundaries & Security Safeguards

● Context Protection

Keeps heavy parsing and raw file structures external to the LLM. Only sanitized excerpts and structured returns are injected into agent memory turns.

● Directory Allowlisting

Enforce explicit directory allowlists and permission scopes in client configuration to ensure autonomous tool calls never breach system boundaries.

● Process Isolation

Executes in an isolated local child process communicating strictly over JSON-RPC via stdio with explicit process timeouts.

07 // Frequently Asked Questions

FAQ Schema Included

What is Caveman and how does it empower AI agents?

Caveman is an open-source coding capability designed for AI clients and autonomous agents. 🪨 why use many token when few token do trick. Viral skill + proxy for coding agents that cuts 65% of tokens by talking like a caveman.

How do I install and configure Caveman?

Follow the step-by-step installation: 1) Verify your runtime (Python 3.10+), 2) Add the configuration snippet into your AI client settings (e.g. claude_desktop_config.json), 3) Restart the client to initialize tool execution.

Which AI clients and developer harnesses support Caveman?

Caveman is compatible with all AI tools supporting tool-calling and the Model Context Protocol (MCP), including Claude Desktop, Cursor, Claude Code CLI, Windsurf, OpenCode, and custom agent runtimes.

What security boundaries and sandboxing safeguards does Caveman enforce?

Caveman executes locally with strict directory allowlists and permission scopes. Heavy parsing and raw file structures stay external to the LLM context window to prevent memory saturation and unauthorized access.

Is Caveman free and open source?

Yes, Caveman is published under the Apache-2.0 open-source license. You can inspect the source code and contribute on GitHub at https://github.com/JuliusBrussee/caveman.

Inspect Upstream Repository

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