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ODW #6: The Pros and Cons of MCP and Agent Skills from a Git Automation Perspective (opens in new tab)

pythonmcpclaude-codegitsemantic-versioningagent-skillsrelease-automation

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.