GitCode Release Notes Generator for Openclaw

An AI-powered tool that transforms GitCode commit history into professional, categorized Markdown release notes.

autoxj
v1.0.0
Mar 12, 2026
1
875
0

Install & Download

1. ClawHub CLI

The fastest way to install a skill directly from the registry.

npx clawhub@latest install gitcode-release-notes

2. Manual Installation

Copy the skill folder to one of these locations

Global
~/.openclaw/skills/
Workspace
<project>/skills/

Priority: Workspace > Local > Bundled

3. Prompt Installation

Copy this prompt to OpenClaw to install it automatically.

Help me install gitcode-release-notes using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).

Prefer to download?

Get the raw skill files in a ZIP archive.

What is GitCode Release Notes Generator?

The gitcode-release-notes skill for Openclaw Skills is a specialized tool designed to automate the creation of high-quality documentation for software releases. By leveraging the GitCode API, it pulls commit data across specific tag ranges or timeframes and provides a structured JSON output for AI processing.

The core strength of this skill lies in its ability to take raw commit logs and transform them into human-readable release notes. It intelligently categorizes updates into new features, fixes, documentation, and other changes, ensuring that the final output is professional and free of technical noise like merge commits, internal debugging logs, or empty messages. This ensures that stakeholders receive a clear and actionable summary of every release.

GitCode Release Notes Generator Use Cases

  • Generating formal release notes for GitCode repository version releases.
  • Creating a chronological update log from a specific date to the present for internal tracking.
  • Automating changelog generation between two specific version tags to identify breaking changes.
  • Summarizing repository activity for product managers and external users in a clean Markdown format.

How GitCode Release Notes Generator Works

  1. Extract repository details and versioning parameters (tags, dates, or branches) from the user prompt.
  2. Execute the underlying Python script using the --json flag to fetch raw commit data from the GitCode API.
  3. Analyze the returned JSON payload to filter out low-value commits and technical internal noise.
  4. Categorize remaining commits into specific sections such as Features, Fixes, and Documentation.
  5. Summarize and polish the descriptions to create a professional Markdown document ready for publication on GitCode Release pages.

GitCode Release Notes Generator Setup

To use this skill within Openclaw Skills, you must first obtain a GitCode Personal Access Token. Set this token as an environment variable in your system:

# Linux or macOS
export GITCODE_TOKEN="your_personal_access_token"

# Windows (Command Prompt)
set GITCODE_TOKEN=your_personal_access_token

The skill relies on a Python 3.7+ environment and uses the standard library to interface with the GitCode API via the included script located at scripts/release_notes.py.

GitCode Release Notes Generator Data Schema & Taxonomy

The skill processes repository metadata and commit history to generate its output. The data is organized as follows:

Attribute Description
repo The GitCode repository path in owner/repo format.
commits An array of commit objects containing messages and metadata.
stats Statistical summary of commit counts and categories.
title_line A generated header containing the version range and date.
category Mapping of commits to feat, fix, docs, or other categories.

GitCode Release Notes Generator Advanced Features

  • Intelligent branch detection that automatically tries master, develop, and main branches if not specified.
  • Multi-range support allowing for filtering by tag-to-tag ranges or specific timestamps.
  • AI-driven summarization that replaces raw commit hashes with human-readable feature descriptions.
  • Strict noise reduction that automatically strips out dependency updates and merge commits to maintain high signal-to-noise ratios.

SKILL.md


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