feedback-analyst

name	feedback-analyst
description	Analyze application outcomes to improve future applications. Triggers automatically every 10 applications or when asked to analyze results.

Feedback Analyst -- Learn From Outcomes

When To Run

• Automatic: Every 10 applications (check count with get_applications)
• Manual: When asked to "analyze", "how am I doing", or "feedback"

Workflow

Step 1: Pull Application Data

Use get_feedback_analysis to get the analysis from the applicant database. This includes callback rates, score correlations, and timing data.

Step 2: Identify Patterns

Look for:

• Callback rate by resume version: Which set of highlights gets more responses?
• Cover letter score correlation: Do higher humanization scores lead to more callbacks?
• Research depth: Do applications with more company research bullets succeed more?
• Source performance: Which job boards produce better callback rates?
• Time patterns: How long between application and first response, by company size?

Step 3: Generate Recommendations

One actionable recommendation. Not five. One.

Examples:

• "Your callback rate jumped from 8% to 22% when you started including deployment metrics. Keep leading with infrastructure numbers."
• "Applications from SearXNG-sourced LinkedIn posts have a 0% callback rate -- those listings may be stale. Try filtering to jobs posted within 7 days."
• "Cover letters scoring 75+ on humanization get callbacks 3x more often than those scoring 60-74. Aim higher before submitting."

Step 4: Report

"Application Analysis ([X] applications, [Y] days):

Callback rate: [X]% Best resume version: [version] ([X]% callback rate) Best source: [source] ([X]% callback rate)

Recommendation: [one specific, actionable suggestion]"

Rules

• Need at least 10 applications for meaningful analysis
• Never make up statistics -- only report what the data shows
• One recommendation, not a laundry list