AI Coding Assistants

Get your AI coding assistant writing correct Xberg code on the first try — right function signatures, right config field names, right per-language conventions — instead of hallucinated APIs you have to fix.

The Xberg plugin teaches your assistant how to use the library, covering extraction, configuration, OCR, chunking, embeddings, batch processing, error handling, and plugins across Python, Node.js/TypeScript, Rust, and CLI.

Installing

Install the Xberg plugin from the xberg-io/xberg marketplace. It ships the Xberg agent skills (extraction APIs, OCR backends, configuration, language conventions) ; see the runtime resources and prerequisites below.

Claude Code

/plugin marketplace add xberg-io/xberg
/plugin install xberg@xberg

Codex CLI

/plugins add https://github.com/xberg-io/xberg

Then search for xberg and select Install Plugin.

Cursor

Settings → Plugins → Add from URL → https://github.com/xberg-io/xberg, then select Xberg.

Gemini CLI

From a checkout of this repository:

gemini extensions install ./plugin

The manifest is inside plugin/. See the Gemini extension reference for local installation and updates.

Kimi installation and compatibility

From a checkout of this repository, install with the Python Kimi CLI version used for native plugin validation:

uvx --from kimi-cli==1.51.0 kimi plugin install ./plugin

Native plugin installation was validated with Python Kimi CLI 1.51.0. The replacement @moonshot-ai/kimi-code 2.1.1 does not provide a plugin CLI subcommand; its interactive plugin loading has not been verified. These results do not establish compatibility with newer Kimi releases.

Factory Droid

droid plugin marketplace add https://github.com/xberg-io/xberg
droid plugin install xberg@xberg

GitHub Copilot CLI

copilot plugin marketplace add https://github.com/xberg-io/xberg
copilot plugin install xberg@xberg

Pi

Install from a local checkout now:

pi install ./plugin

After the next plugin release, install the same package used by OpenCode:

pi install npm:@xberg-io/opencode-xberg

Pi loads the bundled skills and prompt templates. Use /xberg-extract and /xberg-ocr to start a task. Install the CLI separately as shown in the plugin README. This package does not register MCP servers, hooks, or executable extensions in Pi.

opencode

Add the package to opencode.json:

{
  "$schema": "https://opencode.ai/config.json",
  "plugin": ["@xberg-io/opencode-xberg"]
}

Hermes

After a release publishes the updated Hermes package, install it into Hermes’ Python environment and enable it in Hermes configuration:

pip install xberg-hermes-plugin

Prefer a raw MCP server?

The plugin also bundles an Xberg MCP server, so a harness that speaks Model Context Protocol gets the extraction tools without installing the plugin. See MCP Integration for the standalone server and direct-CLI configs.

What the Skill Provides

When your AI coding assistant discovers the skill, it knows:

• All extraction functions and their correct signatures across languages
• Configuration field names (for example, max_chars not max_characters in Python)
• Rust feature gates (for example, tokio-runtime for sync functions)
• Language-specific conventions (snake_case in Python/Rust, camelCase in Node.js)
• Error handling patterns for each language

Quick Examples

Python

from xberg import ExtractInput, ExtractionConfig, OcrConfig, extract

output = await extract(ExtractInput(kind="uri", uri="document.pdf"))
print(output.results[0].content)

config = ExtractionConfig(
    ocr=OcrConfig(backend="tesseract", language=["eng"]),
    output_format="markdown",
)
output = await extract(ExtractInput(kind="uri", uri="document.pdf"), config=config)

Node.js

import { ExtractInputKind, extract } from '@xberg-io/xberg';

const output = await extract({
  kind: ExtractInputKind.Uri,
  uri: 'document.pdf',
});
console.log(output.results[0].content);

Rust

use xberg::{extract, ExtractInput, ExtractionConfig};

let config = ExtractionConfig::default();
let output = extract(ExtractInput::from_uri("document.pdf"), &config).await?;

CLI

xberg extract document.pdf
xberg extract document.pdf --format json --output-format markdown

Further Reading

• Plugin marketplace — install the plugin in every supported harness
• MCP Integration — run Xberg as a standalone MCP server for any MCP-capable agent
• Extraction Basics — core extraction API
• Configuration — all configuration options
• Chunking — split text for RAG
• Embeddings — semantic vectors for search
• Language Detection — multilingual document analysis
• Plugin System — custom plugins

Runtime resources and prerequisites

The generated bundle targets Claude Code, Codex, Cursor, Gemini CLI, Kimi, OpenCode, Factory Droid, Hermes, Pi, Agent Plugins and GitHub Copilot CLI. Use the plugin installation guide for the runtime’s installation method and configuration.

Runtime	Integration
Claude, Codex, Cursor, Factory	Native plugin manifests, skills and MCP configuration
Gemini CLI	Extension manifest, command templates and MCP configuration
Kimi	Plugin manifest and session instructions
OpenCode	npm adapter, instructions and MCP configuration
Hermes	Python plugin, explicitly selected skills and a local stdio MCP bridge
Pi	Skills and prompt templates; execute operations through the CLI
Agent Plugins, Copilot	Agent Plugins 1.0 manifest, skills and MCP configuration

Install the product CLI before using Hermes or Pi. Hermes starts the installed CLI directly; its wheel does not include the auto-installing launcher used by other MCP clients. Select list_tools through the product’s Hermes bridge to inspect the supported tools and request schemas. Select skills explicitly when needed; they are not appended automatically to every conversation.

From a checkout, install the generated Hermes package into Hermes’ Python environment:

pip install ./plugin/.hermes/package

Enable the plugin in Hermes configuration. Loading project plugins also requires HERMES_ENABLE_PROJECT_PLUGINS=true. After a release publishes the updated package, the registry install is pip install xberg-hermes-plugin.

Pi resources can be installed from the checkout with pi install ./plugin. After a plugin release includes them, install npm:@xberg-io/opencode-xberg through Pi. Pi does not register an MCP server, hooks or executable extensions from this package.

Standalone public skills

The public skills directory contains instructions you can install independently of the runtime plugin. Use the skills CLI to list and select them:

npx skills add xberg-io/xberg --list
npx skills add xberg-io/xberg

Choose the target agents interactively. For a local checkout, use npx skills add ./skills. This installs instructions; it does not install the product CLI, enable MCP, download models or set provider credentials.

For an MCP-enabled Cargo install, run cargo install xberg-cli --features mcp.