Skills Repository

Overview

This repository contains a collection of skills — modular, self-contained packages that extend AI agent capabilities (Claude Code, Gemini CLI, CodeBuddy, etc.) with specialized workflows, domain knowledge, and reusable tools.

Repository Structure

skills/
├── README.md                  # This file
├── Makefile                   # Build & install automation
├── flake.nix                  # Nix dev environment (optional)
├── <skill-name>/              # One directory per skill
│   ├── SKILL.md               # Required: metadata + instructions
│   ├── agents/openai.yaml     # UI metadata for OpenAI-compatible agents
│   ├── scripts/               # Optional: Python/Bash scripts
│   ├── references/            # Optional: domain documentation
│   └── assets/                # Optional: templates, images, fonts
└── <skill-name>.zip           # Distributable package (generated)

Available Skills

Skill	Description
build-android	Compile, build, and run Gradle tasks for Android projects
claude-configure	Configure Claude Code CLI, update settings.json, and apply recommended permissions
code-quality-review	Multi-language code review with structured prioritized findings
find-java	Locate Java installations, configure JAVA_HOME, resolve JDK version mismatches
framework-rev-engineer	Reverse-engineer Android framework — analyze vendor-customized services and framework APIs
jadx	Decompile and reverse-engineer Android/Java binaries (.apk, .dex, .jar, .aar)
law	Legal consultation, document analysis, and regulatory interpretation
mobile-control	ADB-based Android device control with visual verification and ReAct loop
ollama-vision	Ollama vision model for image analysis and screenshot inspection
prompt-enhance	Enhance and optimize LLM prompts — language/structure and vocabulary-level improvement
python-dev	Python development workflow (lint, venv, type check)
remote-build-android	Remote Android compilation — package, sync via rsync, build, retrieve results
skill-creator	Guide for creating effective skills
tmux-spawn	Spawn interactive CLI agents in tmux sessions with multi-agent orchestration
verify-kmm-build	KMM/Kuikly compile-verify with auto-fix loop across multiple platform targets
whistle	HTTP proxy capture/inspect/mock via whistle — network traffic analysis and mock rules
whypua	Systematic debugging and root cause analysis — for repeated failures and dead-ends
zellij-control	Programmatic session inspection, tab/pane management, screen dumping, and command execution inside Zellij

---

Installation

Prerequisites

• make (standard on macOS/Linux; on Windows use WSL or Git Bash)
• zip command-line tool

One-Key Install (for AI Agents)

AI agents can clone the repository to a temporary directory and install all skills in one command:

git clone https://git.kagurach.uk/kagura/skills.git /tmp/skills && cd /tmp/skills && make install

Install to Claude Code

make install-claude

Copies all skill directories to ~/.claude/skills/ and sets correct file permissions (files: 644, dirs: 755).

Install to Gemini CLI

make install-gemini

Copies all skill directories to ~/.gemini/skills/.

Install to CodeBuddy

make install-codebuddy

Copies all skill directories to ~/.codebuddy/skills/.

Install to Codex

make install-codex

Copies all skill directories to ~/.codex/skills/.

Manual Install (single skill)

To install only one skill, copy its directory to the agent's skills folder:

# Example: install only the python-dev skill for Claude Code
cp -R python-dev ~/.claude/skills/
chmod -R 644 ~/.claude/skills/python-dev
find ~/.claude/skills/python-dev -type d -exec chmod 755 {} +

---

Packaging Skills (for distribution)

To generate .zip packages for all skills:

make all

To package a single skill:

cd skill-creator
python scripts/package_skill.py ../python-dev

The packaging script:

1. Validates SKILL.md frontmatter, structure, and agents/openai.yaml when present
2. Creates a .zip named after the skill (e.g., python-dev.zip)

---

Creating a New Skill

Use the skill-creator skill or follow these steps manually:

Step 1 — Initialize

python skill-creator/scripts/init_skill.py <skill-name> --path . \
  --resources scripts,references \
  --interface display_name="Skill Name" \
  --interface short_description="A concise 25-64 character summary" \
  --interface default_prompt='Use $skill-name to handle this task.'

This creates <skill-name>/SKILL.md, agents/openai.yaml, and only the requested empty resource directories.

Step 2 — Edit SKILL.md

Every SKILL.md requires YAML frontmatter at the top:

---
name: my-skill
description: One sentence explaining what this skill does and when to use it.
---

Write instructions in imperative form (verb-first). Target audience is another AI agent instance.

Step 3 — Add Resources (optional)

Folder	Purpose	Example
agents/	Agent UI metadata	openai.yaml
scripts/	Deterministic Python scripts	rotate_pdf.py
references/	Domain docs loaded on demand	schema.md, api_docs.md
assets/	Output files (templates, images)	logo.png, template.html

Use only Python standard library; maintain Python 3.9+ compatibility.

Step 4 — Package

python skill-creator/scripts/package_skill.py ./<skill-name>

---

Development Environment (Nix, optional)

A flake.nix is provided with Python linters pre-installed:

nix develop
# Activates: flake8, pyright, ruff, shellcheck

---

Makefile Targets

Target	Description
make all	Package all skill directories into .zip files
make install	Install all skills to all detected target agent directories
make install-claude	Install all skills to ~/.claude/skills/
make install-gemini	Install all skills to ~/.gemini/skills/
make install-codebuddy	Install all skills to ~/.codebuddy/skills/
make install-codex	Install all skills to ~/.codex/skills/
make clean	Remove all generated .zip files
make validate	Validate every directory containing SKILL.md

---

How Skills Are Loaded by the Agent

Skills use a three-level progressive disclosure system:

1. Metadata (name + description in frontmatter) — always in context (~100 words)
2. SKILL.md body — loaded when the skill triggers (keep under 5 000 words)
3. Bundled resources (scripts/, references/, assets/) — loaded by the agent only as needed

This design keeps context lean while making full detail available on demand.

---

Quick Validation

To validate a skill without packaging:

python skill-creator/scripts/quick_validate.py ./<skill-name>
make validate

Validation checks the exact name and description frontmatter, naming and body-size rules, TODO markers, and required agents/openai.yaml interface fields when the file exists.