ODW #6: The Pros and Cons of MCP and Agent Skills from a Git Automation Perspective (opens in new tab)
The post presents agent skills as a simpler, more practical alternative to building MCP servers for many AI-agent workflows. It demonstrates how to use Anthropic’s `skill-creator` to build a Git release automation skill that analyzes commits, updates a changelog, bumps versions, commits, tags, and pushes releases. The author emphasizes that precise requirements and explicit constraints are essential for preventing unintended agent behavior. ## Why Agent Skills Are Practical - Agent skills can simplify both implementation and architecture compared with custom MCP servers. - Although online examples explain the concept, the post focuses on a practical, work-oriented use case. - The tutorial assumes familiarity with the basic concept of skills and concentrates on building and applying one. ## Git Smart Release Automation The example skill automates releases for a Git project in the current working directory. - Reads the Git history after the most recent tag. - Summarizes changes and adds them to the top of `CHANGELOG.md`. - Creates `CHANGELOG.md` if it does not exist. - Updates the version in `pyproject.toml`. - Commits the changelog and version changes. - Creates a corresponding Git tag. - Operates based on the terminal’s current `pwd`. ## Using `skill-creator` - Anthropic’s official `skill-creator` skill is used to generate the new automation skill. - The user provides a detailed requirements specification rather than implementing everything manually. - Explicit workflow steps and constraints help keep the agent focused on the correct directory and avoid unnecessary complexity. - The development process is demonstrated with Claude Code. ## Clarifying Requirements Before generating the skill, the agent asks questions to resolve ambiguous behavior. - Support patch, minor, and major version bumps. - Use `v0.1.0` for the first release when no prior tag exists. - Follow a structured changelog format. - Push both commits and tags to the remote repository. - Abort with an explanation if the working directory contains uncommitted changes. ## Generated Skill Structure The completed skill contains: - `SKILL.md` — instructions and metadata for the agent. - `scripts/smart_release.py` — a local Python script that performs Git operations and file modifications. - `evals/evals.json` — evaluation cases for testing the skill. The skill also includes: - Keep a Changelog-style updates. - Dirty working-directory checks. - Automatic remote pushing. - Commit categorization such as `feat`, `fix`, and `docs`. ## `SKILL.md` and the Python Script - The frontmatter in `SKILL.md` acts as a concise discovery description that helps the agent decide when to load the skill. - The Markdown body provides the detailed execution workflow. - `smart_release.py` handles operations requiring deterministic file and Git manipulation, reducing the need for the language model to process raw data directly. - The post then begins testing the skill with a simple Python calculator project. A practical approach is to define release behavior, edge cases, and safety constraints before asking an agent to generate the skill, while delegating file and Git operations to a local script.