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dag-capability-ranker

Rank candidate skills by fit, performance, and contextual relevance.

10|1|Updated Mar 17, 2026
Search Tags:#performance#context#skills#registry#scoring#ranking#dag
One-click install
npx skills add https://github.com/curiositech/windags-skills --skill dag-capability-ranker
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dag-capability-ranker
Source: https://github.com/curiositech/windags-skills/tree/main/skills/dag-capability-ranker
Command: npx skills add https://github.com/curiositech/windags-skills --skill dag-capability-ranker

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps teams identify the best-fitting skill among a pool of candidates by evaluating multiple performance and contextual factors, reducing guesswork in skill selection.

Core Features & Use Cases

  • Multi-factor ranking: combines semantic match, historical success, efficiency, and context fit to yield a ranked list.
  • Context-aware recommendations: adjusts rankings based on current task domain, available tools, and recent usage patterns.
  • Explainable results: provides a succinct rationale and recommended alternatives to support decision making.

Quick Start

Rank the available skills for a given task and review the top recommendation with its reasoning.

Frequently Asked Questions about dag-capability-ranker

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

FAQPage Schema
How do I rank and score available skills by contextual fit for a specific task?▼

You can rank skills by applying multi-factor scoring that combines semantic match, historical success rate, efficiency, and context fit to produce a prioritized recommendation list.

How does context-aware skill recommendation work when selecting from a candidate pool?▼

Context-aware recommendations adjust skill rankings by evaluating the current task domain, available tools, and recent usage patterns alongside historical performance data from the skill registry.

What is the best way to compare candidate skills using historical performance and success rates?▼

The best way to compare candidate skills is using multi-factor ranking that leverages previous outcomes and registry performance data to calculate final scores with optional pairing bonuses.

Can I get explanations for why a specific skill was ranked higher than others?▼

Yes, the ranking process generates explainable results that provide a succinct rationale and recommended alternatives to support your skill selection decision making.

Does multi-factor skill ranking require previous outcome data from a skill registry?▼

Yes, the ranking leverages performance data from the skill registry and previous outcomes to compute final scores, though it can still evaluate semantic match and context fit.

What are the limitations of using automated scoring for skill selection in complex workflows?▼

Automated scoring relies on available historical performance data and registry entries, so skills without prior usage data may lack accurate success rate or efficiency metrics for ranking.

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