agent-scorer
Generate semantic + keyword match score. Compares resume content against job description to calculate a fit score and identify gaps.
Best use case
agent-scorer is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Generate semantic + keyword match score. Compares resume content against job description to calculate a fit score and identify gaps.
Teams using agent-scorer should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/agent-scorer/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How agent-scorer Compares
| Feature / Agent | agent-scorer | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Generate semantic + keyword match score. Compares resume content against job description to calculate a fit score and identify gaps.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Scorer Agent
## Overview
The Scorer Agent is responsible for evaluating the fit between a candidate and a job description.
## Workflow Definition
1. **Input**: Resume JSON, Job Description Text.
2. **Vectorization**:
* Embed Resume Skills/Experience (`vectorize-ollama`)
* Embed Job Description (`vectorize-ollama`)
3. **Semantic Match**: Calculate cosine similarity (`similarity-cosine`).
4. **Keyword Statistics**: Compare explicit keywords (`compare-keywords`).
5. **Scoring Logic**:
* Combine Semantic Score (weight 0.7) + Keyword Score (weight 0.3).
6. **Output**: Score object `{ total_score: float, details: {...} }`.Related Skills
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