What happens if a target doesn't match any configured pattern?

Depending on your configuration, the skill will either return a default entity type (such as 'file') or trigger an error if 'require_match' is set to true.

How does the skill handle multiple matching patterns?

It uses a specificity scoring formula that calculates literal prefix length against wildcard counts to ensure the most specific definition always takes precedence.

What is the primary purpose of the Target Matcher skill?

It identifies the type of entity a user is working on by matching input strings against predefined patterns, providing essential metadata for planning without needing a specific issue ID.

Where are the target patterns and metadata configured?

All targets are defined in the .fractary/plugins/faber/config.json file located in your project root.

Can I use standard glob patterns with this skill?

Yes, it supports standard globbing including single asterisks for files, double asterisks for recursive paths, and character classes.

Target Matcher for Faber

Name: Target Matcher for Faber
Author: fractary

byfractary

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生产力与工作流

Identifies entity types and retrieves metadata from input patterns to enable work-ID-free planning in the Faber workflow.

关于

The Target Matcher skill streamlines development workflows by mapping arbitrary target strings—such as file paths, dataset names, or directory patterns—to specific entity definitions within the Faber ecosystem. By utilizing glob-based pattern matching and a sophisticated specificity scoring system, it allows Claude to understand the context and metadata of a task without requiring a linked issue ID (work_id). This is essential for developers who need to initiate planning phases quickly while maintaining consistent metadata standards across diverse project artifacts like source code, ETL datasets, and documentation.

主要功能

Rich metadata retrieval for entity types and workflow overrides
Automated specificity scoring to resolve overlapping pattern definitions
Seamless integration with the faber-planner agent for automated context resolution
2 GitHub stars
Advanced glob-based pattern matching including deep path segments
Configurable fallback behaviors for unmatched targets and default types

使用场景

Initiating development planning tasks without an existing Jira or GitHub issue ID
Enforcing project-specific metadata standards during AI-driven planning phases
Automating the identification of ETL datasets versus source code files for better context

关于

主要功能

Rich metadata retrieval for entity types and workflow overrides
Automated specificity scoring to resolve overlapping pattern definitions
Seamless integration with the faber-planner agent for automated context resolution
2 GitHub stars
Advanced glob-based pattern matching including deep path segments
Configurable fallback behaviors for unmatched targets and default types

使用场景

Initiating development planning tasks without an existing Jira or GitHub issue ID
Enforcing project-specific metadata standards during AI-driven planning phases
Automating the identification of ETL datasets versus source code files for better context