How the Quick Fit Assessment Categorizes Job Matches as High, Medium, or Low

The quick fit assessment categorizes job matches into High, Medium, and Low tiers based on provisional overall scores of 75 or above, 45 to 74, and below 45 respectively, mapping directly to the full evaluation framework's verdicts.

The quick fit assessment is a lightweight scoring mechanism in the MadsLorentzen/ai-job-search repository that executes immediately after job postings are scraped. It provides an initial fit rating to surface the most promising opportunities before running the complete weighted evaluation. This system enables users to prioritize their job search by filtering opportunities into three distinct categories based on alignment with their candidate profile.

Score Thresholds and Category Mapping

The quick fit assessment translates numerical scores into intuitive category labels. These thresholds correspond directly to the verdicts defined in the full Job Evaluation Framework located at .claude/skills/job-application-assistant/04-job-evaluation.md.

High Fit Matches (75+ Points)

Jobs scoring 75 or above out of 100 receive a High quick fit label. In the full evaluation framework, this corresponds to a Strong Fit verdict. These positions align closely with the candidate's technical skills, experience level, behavioral preferences, career trajectory, and location requirements as defined in .claude/skills/job-application-assistant/01-candidate-profile.md.

Medium Fit Matches (45–74 Points)

Positions scoring between 45 and 74 points are classified as Medium fit. This category maps to either a Good Fit or Moderate Fit verdict in the comprehensive evaluation. These roles may satisfy most core criteria but potentially lack alignment in specific dimensions such as seniority level or particular technical specializations.

Low Fit Matches (Below 45 Points)

Any job posting scoring below 45 points receives a Low quick fit classification, corresponding to Weak Fit or Poor Fit verdicts. These positions typically fail to meet multiple critical criteria from the five core evaluation dimensions, making them unsuitable candidates for application.

How the Quick Fit Algorithm Works

The assessment calculates a provisional overall score using the same five core dimensions as the full evaluation: technical skills, experience, behavioral fit, career alignment, and a location check (pass/fail).

However, the quick fit version simplifies the calculation by:

• Omitting the full weighting adjustments applied in the complete evaluation
• Excluding the optional salary benchmark comparison
• Running immediately after scrape completion in .claude/skills/job-scraper/SKILL.md

This lightweight approach allows the system to tag new job postings instantly while deferring the comprehensive analysis until the user initiates the /apply workflow.

Implementation in the Source Code

The categorization logic resides across several key files in the repository:

• .claude/skills/job-scraper/SKILL.md: Defines the job scraper skill and implements the quick fit logic that tags each new match as high, medium, or low immediately after scraping.

• .claude/skills/job-application-assistant/04-job-evaluation.md: Contains the complete scoring matrix, weighted calculations, and verdict thresholds that the quick fit step references for its category mapping.

• .claude/skills/job-application-assistant/01-candidate-profile.md: Stores the candidate data against which job postings are compared during the quick fit assessment.

• .agents/skills/*-search/cli/*: Houses the CLI implementations for various job portal tools that invoke the quick fit categorization after each scrape operation.

Running the Quick Fit Assessment

Users can execute the quick fit assessment through the command-line interface to categorize newly scraped positions:

# Scrape new postings and display quick fit labels

/jobindex-search --query "Data Scientist" --format table

The output includes a "Fit" column displaying High, Medium, or Low for each position.

To rank all scraped jobs by their quick fit scores:

# Rank jobs by quick fit rating  

/rank --quick-fit

This returns a sorted table. Example output:

• 1️⃣ Company A – Senior Data Scientist – High
• 2️⃣ Company B – ML Engineer – Medium
• 3️⃣ Company C – Junior Analyst – Low

When you subsequently run the full /apply workflow on a specific posting, the system replaces the quick fit label with the detailed verdict (Strong Fit, Good Fit, etc.) generated by the complete weighted evaluation.

Summary

• The quick fit assessment categorizes job matches into High (75+), Medium (45–74), and Low (<45) tiers based on provisional overall scores.
• These categories map directly to the full evaluation framework's verdicts: Strong Fit, Good/Moderate Fit, and Weak/Poor Fit respectively.
• The algorithm evaluates five core dimensions (technical skills, experience, behavioral fit, career alignment, and location) without full weighting or salary benchmarking.
• Implementation spans .claude/skills/job-scraper/SKILL.md and the job evaluation framework files in the MadsLorentzen/ai-job-search repository.
• CLI commands /jobindex-search and /rank --quick-fit expose this functionality for immediate job filtering.

Frequently Asked Questions

What is the difference between quick fit categories and full evaluation verdicts?

The quick fit assessment provides a high-level categorization (High, Medium, Low) using simplified scoring, while the full evaluation generates specific verdicts (Strong Fit, Good Fit, Moderate Fit, Weak Fit, Poor Fit) using weighted dimensions and optional salary benchmarks. When you run /apply on a job posting, the quick fit label is replaced by the detailed verdict from the comprehensive evaluation.

Why does the quick fit assessment skip the salary benchmark?

The quick fit assessment omits the salary benchmark to prioritize speed and reduce computational overhead during the initial scraping phase. Since the goal is rapid triage of new postings, the system calculates a provisional score using only the five core criteria, allowing users to filter opportunities immediately while deferring detailed compensation analysis until the full evaluation stage.

Where are the score thresholds defined in the source code?

The score thresholds of 75+ for High, 45–74 for Medium, and below 45 for Low are defined in .claude/skills/job-application-assistant/04-job-evaluation.md as part of the Job Evaluation Framework. The quick fit logic references these same thresholds in .claude/skills/job-scraper/SKILL.md to maintain consistency between the provisional and full evaluations.

How does the location check affect the quick fit score?

The location check functions as a pass/fail filter within the quick fit calculation. Unlike the other four dimensions (technical skills, experience, behavioral fit, and career alignment) which contribute graduated scores, the location requirement must be satisfied for a job to score above the Low threshold. Positions failing the location check typically fall into the Low category regardless of other qualifications.

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