# Conversation quality scoring Human Guide

## What This Is For
When the user wants to build or improve a sales bot's ability to rate conversation quality. It gives the agent a clearer input/output frame for conversation quality scoring: what context to ask for, what decisions to make, and what usable artifact to return.

Use this as a human-readable version of the Conversation quality scoring agent skill. It is meant for marketers, operators, founders, and other non-coders who want the workflow without reading agent-specific implementation instructions.

## When To Use This
- Use this when you need a repeatable process for conversation quality scoring.
- Use this when the task needs judgment, examples, constraints, or a clear output format rather than a one-off prompt.
- Use this when you want to hand an AI assistant enough context to produce a usable marketing artifact.

## When Not To Use This
- Do not use this when you only need a quick factual answer.
- Do not use this when the work depends on private data you cannot share with the assistant.
- Do not use this as a replacement for legal, compliance, financial, or medical review.

## What You Need Before Starting
- The goal or business outcome you want.
- The audience, customer segment, or market context.
- Any source material the assistant should respect, such as notes, briefs, examples, URLs, or brand guidance.
- Constraints such as tone, length, channel, deadline, region, or approval requirements.
- A clear definition of what a good final answer should look like.

## Step-By-Step Workflow
1. State the job clearly: "Use the Conversation quality scoring guide to help me with..."
2. Add context: audience, goal, offer, channel, source material, and constraints.
3. Ask the assistant to identify missing inputs before producing the final output.
4. Have the assistant follow the skill-specific guidance below.
5. Review the result against the final checklist and ask for revisions where needed.

## Skill-Specific Guidance
- Frame the work around conversation quality scoring and ask for the context needed to do it well.
- Turn vague preferences into explicit choices before drafting the final output.
- Ask for a concrete deliverable, not just general advice.

## Decision Points And Nuance
The original skill emphasizes: Why Quality Scoring Matters, The Blind Spot Problem, With Quality Scoring, Scoring Dimensions, Engagement Quality, Discovery Quality, Value Communication, Objection Handling Quality, Progression Quality, Aggregate Scoring.

Use these questions to steer the work:
- What is the intended audience or buyer?
- What source material must be preserved?
- What should the assistant optimize for: clarity, persuasion, accuracy, speed, creativity, or conversion?
- What examples represent the desired quality bar?
- What should the assistant avoid?

## Common Mistakes
- Don't know what "good" looks like

## Copy-And-Paste Prompt
```text
Use the Conversation quality scoring human guide.

My goal:
[Describe the business outcome]

Audience:
[Describe who this is for]

Context and source material:
[Paste notes, examples, links, or existing copy]

Constraints:
[Tone, length, channel, timeline, must-include items, must-avoid items]

Before producing the final output, ask me for any missing information that would materially improve the result.
```

## Final Checklist
- [ ] The output matches the original goal.
- [ ] The audience and context are reflected in the answer.
- [ ] Important constraints and source material were preserved.
- [ ] The assistant made the relevant decisions explicit.
- [ ] The final artifact is ready to use, review, or hand to the next person.

## Source
This guide was generated from the louisblythe/sales-skills skill entry for `conversation-quality-scoring`.

## Source Skill Notes
These notes preserve the nuance from the original skill. Use them as supporting reference when the workflow above feels too generic.

# Conversation Quality Scoring

You are an expert in building sales bots that rate conversation quality to identify top performers. Your goal is to help developers create systems that score conversations to find what works and coach improvement.

## Why Quality Scoring Matters

### The Blind Spot Problem
```
Without quality scoring:
- All conversations treated equal
- Don't know what "good" looks like
- Can't identify best practices
- No basis for improvement

Rep A: 10% conversion
Rep B: 15% conversion
Bot C: 12% conversion
Why? Unknown.
```

### With Quality Scoring
```
Quality-aware system:
- Score each conversation dimension
- Identify high-quality patterns
- Spot areas for improvement
- Benchmark and compare

Rep A: Score 65 (weak on discovery)
Rep B: Score 82 (strong qualification)
Bot C: Score 71 (needs better objection handling)
Now we know where to focus.
```

## Scoring Dimensions

### Engagement Quality
```python
def score_engagement(conversation):
    score = 0

    # Response rate
    response_rate = calculate_response_rate(conversation)
    if response_rate >= 0.8:
        score += 30
    elif response_rate >= 0.5:
        score += 20
    elif response_rate >= 0.3:
        score += 10

    # Response depth
    avg_response_length = calculate_avg_response_length(conversation)
    if avg_response_length > 50:
        score += 20
    elif avg_response_length > 25:
        score += 10

    # Response sentiment
    sentiment_trend = analyze_sentiment_trend(conversation)
    if sentiment_trend > 0:
        score += 20
    elif sentiment_trend == 0:
        score += 10

    # Engagement velocity
    avg_response_time = calculate_avg_response_time(conversation)
    if avg_response_time < timedelta(hours=4):
        score += 15

    return min(score, 100)
```

### Discovery Quality
```python
def score_discovery(conversation):
    score = 0

    # Information gathered
    info_gathered = {
        "pain_points": 25,