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dag-semantic-matcher

Match natural language tasks to ranked candidate skills using semantic similarity and intent extraction.

10|1|Updated Mar 17, 2026
Search Tags:#nlp#semantic#matching#skill-registry#explainability#intent-extraction
One-click install
npx skills add https://github.com/curiositech/windags-skills --skill dag-semantic-matcher
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dag-semantic-matcher
Source: https://github.com/curiositech/windags-skills/tree/main/skills/dag-semantic-matcher
Command: npx skills add https://github.com/curiositech/windags-skills --skill dag-semantic-matcher

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Matches natural language task descriptions to the most relevant skills in the registry, enabling rapid discovery and assignment within DAG workflows.

Core Features & Use Cases

  • Intent extraction and semantic matching to map tasks to skills
  • Candidate generation with fuzzy matching and capability alignment
  • Explainable scoring and gap analysis to guide decisions
  • Use Case: Normalize a user’s task description to a list of compatible skills for node assignment in a DAG

Quick Start

Provide a natural language task description to get ranked skill candidates.

Frequently Asked Questions about dag-semantic-matcher

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I match natural language task descriptions to candidate skills?▼

Semantic matching maps natural language task descriptions to candidate skills using intent extraction and fuzzy matching. It produces ranked candidates from the skill registry by applying capability alignment to infer user intent.

What is intent extraction for semantic skill matching?▼

Intent extraction for semantic matching identifies the underlying goal of a natural language task description. It enables capability alignment and fuzzy matching to generate ranked candidate skills from a skill registry.

Can I use semantic matching to assign nodes in a DAG workflow?▼

Yes, semantic matching normalizes user task descriptions into a list of compatible skills for node assignment in a DAG. It generates ranked candidates from the skill registry to support rapid discovery and assignment.

How does explainable scoring work for task-to-skill matching?▼

Explainable scoring for task-to-skill matching provides transparency by ranking candidate skills and identifying capability gaps. It applies fuzzy matching and capability alignment to produce explanations that guide decision making.

What's the best way to identify capability gaps when matching tasks to skills?▼

The best way to identify capability gaps during semantic matching is to analyze the ranked candidate skills generated from the skill registry. The matcher provides explanations and gap analysis to support informed decision making.

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