clementbowe14 / clementbowe14/JobAppsAgentified

EXT-06: Job Match & Gap Analyzer (0-10 Rating Scale)

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Phase 2: Use Cases
Dominant language
JavaScript
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Description

Objective

Parse a job description and compare it against the user's CV to calculate a compatibility rating, document strengths, list technical and soft skill gaps, and provide actionable recommendations.

Description

Implement the matching algorithm inside the side panel. Instruct the LLM to return structured JSON format detailing: match rating (on a scale of 0 to 10, where 1 is a poor match and 10 is a high quality match), strengths (mapped items), gaps (missing skills or keywords), and upskilling/resume adjustments. Format this data as a beautiful, premium fit summary dashboard.

Acceptance Criteria (Subtasks)

  • Output contains an interactive match score meter/gauge indicating the rating on a 0-10 scale
  • Lists "Matches/Strengths" with green checkmarks and "Gaps/Missing Skills" with red/orange alert icons
  • Renders a list of actionable bullet recommendations (e.g. "Add details about Docker usage to your Cloud Engineer bullet")
  • Match reports are saved locally to history so users can review past match analyses

Rating Scale Reference

Rating Label
0-2 Poor Match
3-4 Below Average
5-6 Moderate Match
7-8 Strong Match
9-10 Excellent Match

Files to Modify/Create

  • src/sidepanel/sidepanel.js (implement dashboard gauge and parser)

Contributor guide

No contributing guide indexed for this repository

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Read src/sidepanel/sidepanel.js first to understand the existing side-panel flow and how job descriptions, CV data, and LLM responses are handled. Implement the match dashboard, structured result display, and local history there, then verify the 0–10 gauge, strengths, gaps, recommendations, rating labels, and saved reports against the acceptance criteria.

Written by the indexing model from the issue text.

Assessment

Tech stack
javascript
Domain
ai, frontend
Issue type
Feature
Difficulty
4/5
Estimated time
3-5 days
Activity status
Quiet
Clarity
Mostly clear
Newbie friendliness
55/100

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