tone-matching
When the user wants to build or improve a sales bot's ability to adapt formality based on how the prospect communicates. Also use when the user mentions "matching tone," "adapting formality," "mirroring communication style," "voice adaptation," or "matching prospect style."
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/louisblythe/sales-skills --skill tone-matching- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads tone-matching automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Tone Matching for Sales Bots
You are an expert in building adaptive communication systems for sales bots. Your goal is to help design bots that adjust their tone and formality to match how prospects communicate.
Initial Assessment
Before providing guidance, understand:
-
Context
- What channels does your bot operate on?
- What's your brand voice?
- What audience do you serve?
-
Current State
- How does your bot currently communicate?
- Does it sound robotic or natural?
- Is the tone appropriate for your audience?
-
Goals
- What would better tone matching achieve?
- What communication style should be the baseline?
Core Principles
1. Mirror Their Energy
- Formal prospect → More formal response
- Casual prospect → More casual response
- Match, don't clash
2. Stay Within Brand Bounds
- Adaptable doesn't mean chameleon
- Maintain brand voice fundamentals
- Shift within acceptable range
3. Authenticity Over Mimicry
- Adapt tone, not personality
- Don't force a style that doesn't fit
- Natural over artificial
4. Read Continuously
- Tone can change within conversation
- Stay attuned throughout
- Adjust as they shift
Tone Dimensions
Formality Spectrum
Formal: "Good afternoon. Thank you for your inquiry. I would be delighted to assist you with..."
Professional: "Hi there. Thanks for reaching out. Happy to help you with..."
Casual: "Hey! Thanks for getting in touch. Let me help you with..."
Very Casual: "Hey there! What's up? Let's figure this out..."
Energy Spectrum
High energy: "That's awesome! I love that idea. Let's make it happen!"
Medium energy: "Great choice. I think that'll work well for you."
Low energy: "That makes sense. Here's how we can proceed."
Detail Spectrum
Detail-oriented: "Here's a comprehensive overview of the three options, including pricing tiers, features included in each, and implementation timelines..."
Summary-focused: "Here are your options: A, B, or C. I'd recommend B based on what you've shared."
Detecting Communication Style
Linguistic Signals
Formal indicators:
- Complete sentences
- Professional greetings ("Dear," "Good morning")
- No contractions
- Full words (not abbreviations)
- Technical language
- Longer messages
Casual indicators:
- Sentence fragments
- Casual greetings ("Hey," "Hi!")
- Contractions ("I'm," "won't")
- Abbreviations ("pls," "thx")
- Emojis
- Shorter messages
Analysis Approach
function analyzeTone(message) {
signals = {
formality: 0, // -1 (casual) to 1 (formal)
energy: 0, // -1 (low) to 1 (high)
detail: 0 // -1 (brief) to 1 (detailed)
}
// Formality signals
if (hasGreeting(message, ["dear", "good morning", "good afternoon"])) {
signals.formality += 0.3
}
if (hasGreeting(message, ["hey", "hi!", "yo"])) {
signals.formality -= 0.3
}
if (hasEmoji(message)) {
signals.formality -= 0.2
}
if (hasContractions(message)) {
signals.formality -= 0.1
}
if (averageWordLength(message) > 5) {
signals.formality += 0.1
}
// Energy signals
if (hasExclamationMarks(message)) {
signals.energy += 0.2 * countExclamations(message)
}
if (hasPositiveEmoji(message)) {
signals.energy += 0.2
}
if (hasEnthusiasticWords(message)) {
signals.energy += 0.2
}
// Detail signals
if (message.length > 200) {
signals.detail += 0.3
}
if (message.length < 50) {
signals.detail -= 0.3
}
if (hasNumbersOrLists(message)) {
signals.detail += 0.2
}
return signals
}
Context Signals
Consider:
- Channel (SMS more casual, email varies)
- Industry (finance formal, tech casual)
- Role (C-suite often more direct)
- Time of day (early morning more brief)
- Previous interactions
Adapting Response Tone
Response Variations
For the same information, different tones:
Formal: "Thank you for your inquiry. I would be pleased to schedule a demonstration at your convenience. Please indicate your availability, and I will coordinate accordingly."
Professional: "Thanks for reaching out! I'd be happy to set up a demo. What times work for you this week?"
Casual: "Hey, love to show you a demo! When works for you?"
Tone Templates
templates = {
"schedule_demo": {
"formal": "I would be delighted to arrange a demonstration. Please share your availability at your earliest convenience.",
"professional": "Happy to set up a demo. What times work for you?",
"casual": "Let's get you a demo! When's good for you?"
},
"answer_pricing": {
"formal": "Regarding pricing, our solutions begin at [X]. I can provide a detailed proposal tailored to your requirements.",
"professional": "Pricing starts at [X]. Want me to put together a custom quote based on your needs?",
"casual": "We start at [X], but it depends on what you need. Want me to work up something specific for you?"
}
}
Dynamic Tone Selection
function selectTone(detected_signals, brand_limits) {
// Map detected tone to response tone
if (detected_signals.formality > 0.5) {
base_tone = "formal"
} else if (detected_signals.formality < -0.3) {
base_tone = "casual"
} else {
base_tone = "professional"
}
// Apply brand limits
if (brand_limits.min_formality == "professional" && base_tone == "casual") {
base_tone = "professional"
}
return base_tone
}
LLM-Based Tone Matching
Prompt Engineering
system_prompt = """
You are a sales assistant. Adapt your tone to match the prospect's communication style:
- If they're formal (complete sentences, professional language), respond formally
- If they're casual (abbreviations, emojis, fragments), respond casually
- Match their energy level
Brand guidelines:
- Always professional and helpful
- Can use humor if they do
- Never use profanity
- Stay within [formal - professional - friendly] range
"""
function generateResponse(message, context) {
tone_analysis = analyzeTone(message)
prompt = `
Prospect's tone: ${describeTone(tone_analysis)}
Prospect's message: ${message}
Context: ${context}
Respond in a matching tone while staying professional.
`
return llm.generate(system_prompt, prompt)
}
Few-Shot Examples
examples = [
{
input: "Hey! Super interested in your product. What's the price?",
tone: "casual, high energy",
output: "Hey! Glad to hear it! 😊 Pricing depends on your team size—usually ranges from $X-$Y/month. What's your setup like?"
},
{
input: "Good morning. I would like to inquire about your enterprise pricing structure.",
tone: "formal, low energy",
output: "Good morning. Thank you for your inquiry. Our enterprise pricing is customized based on several factors. I would be happy to discuss your specific requirements. Would you have time for a brief call this week?"
},
{
input: "Quick q - do you integrate with Salesforce?",
tone: "casual, brief",
output: "Yep! Full Salesforce integration. Want details?"
}
]
Channel-Specific Tone
SMS
Default: Casual to professional Adjustments:
- Shorter regardless of their length
- Emojis acceptable if they use them
- Less formal greetings
Default: Professional Adjustments:
- Match their formality level
- Longer is acceptable if they write longer
- Professional signature
Chat
Default: Friendly professional Adjustments:
- More casual acceptable
- Real-time feel
- Quicker, shorter messages
Voice
Default: Professional conversational Adjustments:
- Match their pace
- Mirror energy level
- Adapt formality
Maintaining Brand Voice
Brand Guardrails
Define:
- Minimum formality level
- Maximum casualness
- Acceptable/unacceptable words
- Emoji policy
- Humor boundaries
Example:
brand_guidelines = {
min_formality: "friendly_professional",
max_casualness: "casual", // Never "very_casual"
emoji_allowed: true,
emoji_max: 1, // Per message
humor_allowed: true,
humor_type: ["light", "friendly"], // Not sarcasm
banned_words: ["..."certain terms..."],
always_include: ["professional signature on email"]
}
Voice Consistency
Core elements to maintain:
- Helpfulness
- Professionalism
- Brand personality
- Value-focused
Elements to adapt:
- Formality
- Energy
- Detail level
- Greetings/closings
Measuring Tone Matching
Metrics
Engagement:
- Response rate by tone match
- Conversation length by tone match
- Sentiment changes through conversation
Quality:
- Human review of tone appropriateness
- Customer feedback
- Brand guideline compliance
A/B Testing
Test:
- Matching tone vs. fixed tone
- Different matching algorithms
- Tone ranges for different segments
Measure:
- Engagement
- Conversion
- Satisfaction
Common Mistakes
1. Over-Matching
Problem: Mirroring everything, losing brand identity Fix: Adapt within brand boundaries
2. Under-Matching
Problem: Fixed tone regardless of prospect Fix: Detect and respond to their style
3. Jarring Shifts
Problem: Sudden tone changes feel unnatural Fix: Gradual adaptation, consistent conversation
4. Matching Negativity
Problem: Prospect is angry, bot matches anger Fix: Match formality, not negativity
5. Forced Casualness
Problem: Casual tone where inappropriate Fix: Industry/context awareness
Implementation Checklist
Phase 1: Basic Tone Detection
- Detect formality level
- Detect energy level
- Categorize messages
Phase 2: Response Adaptation
- Create tone-varied templates
- Select templates by detected tone
- Test and refine
Phase 3: Dynamic Generation
- LLM-based tone matching
- Brand guideline enforcement
- Continuous learning
Questions to Ask
If you need more context:
- What's your brand voice today?
- What audience do you serve?
- What channels does your bot operate on?
- What tone problems have you observed?
- What are your brand's communication boundaries?
Related Skills
- sentiment-analysis: Detecting emotional state
- adaptability: Human adaptability principles
- conversational-flow-management: Natural conversation
- personalization-at-scale: Personalizing communication
FAQ
What does the tone-matching skill do?
When the user wants to build or improve a sales bot's ability to adapt formality based on how the prospect communicates. Also use when the user mentions "matching tone," "adapting formality," "mirroring communication style," "voice adaptation," or "matching prospect style."
How do I install tone-matching?
Run npx skills add https://github.com/louisblythe/sales-skills --skill tone-matching in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/tone-matching/ (personal) or .claude/skills/tone-matching/ (project).
Which AI agents can use tone-matching?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is tone-matching safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains tone-matching?
It is published by louisblythe in louisblythe/sales-skills. It has 422 recorded installs.
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