What happens if a skill file has malformed YAML?

The updater includes error handling that identifies invalid YAML frontmatter and reports it as an error rather than attempting an update that could corrupt the file.

How does it find the latest model IDs?

The skill uses WebSearch and WebFetch tools to browse the official Anthropic documentation in real-time, ensuring it identifies the most recent model releases.

Can I use shorthand model names like 'sonnet'?

Yes, shorthand names are supported and are considered safe because they always resolve to the current version of that tier.

Does this skill change my chosen model tier?

No, it automatically detects whether a skill uses Opus, Sonnet, or Haiku and only updates the version number within that specific tier to preserve your original intent.

Is it safe to run in a production repository?

Yes. You can use the 'update --dry-run' command to see a detailed report of proposed changes without actually modifying any files.

Skill Model Updater

Name: Skill Model Updater
Author: bitwize-music-studio

bybitwize-music-studio

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Productivity & Workflow

Automates the discovery and updating of Claude model references across skill files to ensure optimal performance and currency.

About

The Skill Model Updater is a specialized utility designed to maintain version currency across extensive skill libraries. It programmatically scans for current Anthropic model IDs via official documentation, audits existing skill configurations, and updates outdated references while preserving specific model tiers like Opus, Sonnet, or Haiku. This ensures that AI-driven workflows always utilize the latest model capabilities and performance improvements without requiring manual tracking of Anthropic's release cycles.

Key Features

Smart tier detection to preserve specific model families during updates
3 GitHub stars
Automated discovery of current Anthropic model IDs via WebSearch and WebFetch
Comprehensive auditing of repository skill files to identify outdated model versions
Detailed status reporting for individual skills and overall repository health
Dry-run mode for previewing proposed changes before file modification

Use Cases

Updating a suite of production-ready skills immediately following an Anthropic model launch
Conducting periodic audits to ensure internal AI tools are not using deprecated model versions
Standardizing model naming conventions and versioning across large skill repositories

About

Key Features

Smart tier detection to preserve specific model families during updates
3 GitHub stars
Automated discovery of current Anthropic model IDs via WebSearch and WebFetch
Comprehensive auditing of repository skill files to identify outdated model versions
Detailed status reporting for individual skills and overall repository health
Dry-run mode for previewing proposed changes before file modification

Use Cases

Updating a suite of production-ready skills immediately following an Anthropic model launch
Conducting periodic audits to ensure internal AI tools are not using deprecated model versions
Standardizing model naming conventions and versioning across large skill repositories