How the Score Capping Mechanism Prevents Exceeding Maximum Values in Hiring-Agent

The score capping mechanism in Hiring-Agent enforces hard ceilings at both the individual category level and the final aggregate level, using min() comparisons and conditional checks in score.py to ensure no résumé evaluation exceeds its defined maximums.

The interviewstreet/hiring-agent repository evaluates résumés across multiple dimensions including Open Source contributions and Technical Skills. To maintain realistic ratings, the score capping mechanism implements dual safeguards that limit both individual category scores and the overall total, preventing calculation errors or bonus inflation from producing impossible values.

Per-Category Score Capping in score.py

The system defines hard ceilings for each evaluation dimension in the category_maxes dictionary. When print_evaluation_results processes the evaluation, it clamps each raw score using min() before displaying it.

The category maximums are:

• Open Source: 35 points
• Self Projects: 30 points
• Production Experience: 25 points
• Technical Skills: 10 points

In score.py, the Open Source block (lines 88-92) implements this as:

os_score = evaluation.scores.open_source
capped_score = min(os_score.score, category_maxes["open_source"])
print(f"🌐 Open Source: {capped_score}/{os_score.max}")

Identical logic appears for Self Projects (lines 96-102), Production Experience (lines 106-110), and Technical Skills (lines 115-121).

Overall Score Capping and Bonus Limits

After summing category totals and applying bonus or deduction points, the code validates against the theoretical maximum. The cap is calculated as max_score + 20, representing the 100 base points across all categories plus a 20-point bonus allowance.

At lines 66-69 in score.py, the enforcement logic reads:

max_possible_score = max_score + 20  # 120 (100 categories + 20 bonus)

if total_score > max_possible_score:
    total_score = max_possible_score
    print(f"⚠️  Warning: Total score capped at maximum possible value")

This ensures the final aggregate never exceeds 120 points, regardless of bonus inflation.

Supporting Implementation Files

The capping mechanism relies on data structures defined in models.py, which provides the score objects accessed by the clamping code. The transform.py module converts raw evaluation results into the EvaluationData object consumed by print_evaluation_results, while evaluator.py generates the initial raw scores that undergo capping in score.py.

Summary

• The score capping mechanism applies dual safeguards in score.py: per-category caps and an overall aggregate cap.
• Per-category capping uses min(raw_score, category_max) at lines 88-121 to clamp Open Source (35), Self Projects (30), Production Experience (25), and Technical Skills (10).
• The overall cap limits totals to max_score + 20 (120 points) at lines 66-69, triggering a warning when enforced.
• Supporting infrastructure resides in models.py, transform.py, and evaluator.py.

Frequently Asked Questions

What is the maximum possible score in Hiring-Agent?

The theoretical maximum is 120 points, calculated as the sum of all category maximums (100 points) plus a 20-point bonus buffer. This is enforced in score.py at lines 66-69 where the code caps total_score at max_score + 20.

How does the per-category score cap work?

Each category has a hard ceiling stored in the category_maxes dictionary. When processing results, the code calculates min(os_score.score, category_maxes["open_source"]) (and equivalents for other categories at lines 88-121) to ensure the displayed value never exceeds the defined maximum for that dimension.

What happens when the total score exceeds the maximum?

When the aggregated score surpasses max_score + 20, the system forcibly sets total_score to that limit and prints a warning message: "⚠️ Warning: Total score capped at maximum possible value". This prevents bonus calculations or edge cases from producing impossible final ratings.

Which file contains the score capping logic?

The core capping logic resides in score.py, specifically within the print_evaluation_results function. Lines 88-121 handle per-category capping, while lines 66-69 manage the overall aggregate cap. Supporting data structures are defined in models.py.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}