tov-guidelines

name	tov-guidelines
version	2.1
author	genesys-growth
last_updated	"2026-01-21T00:00:00.000Z"
description	Analyzes existing client content (website, blog, social, docs) to extract voice patterns, vocabulary preferences, frequency scores, and do/don't usage rules. Produces a two-phase output: Phase 1 TOV analysis (patterns extracted from source material) and Phase 2 TOV guidelines (codified voice rules for content production). Upstream: recommended company-context. Downstream: feeds linkedin-content, landing-page-copy, product-messaging, outreach-emails, and all content skills. NOT for visual brand tokens (use /brand-kit).

TOV guidelines

Extract actionable editorial tone of voice guidelines from a company's existing content. Two-phase workflow ensures guidelines are grounded in evidence, not assumptions.

---

Process Flowchart

┌──────────────────────────────────────────────────────────────┐
│                  TOV GUIDELINES PROCESS                       │
│              (Two-Phase Workflow with User Gate)              │
└──────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌──────────────────────────────────────────────────────────────┐
│ PHASE 1: ANALYSIS                                             │
│ Step 1.1: Discover site structure → page list                 │
│ Step 1.2: Scrape 15-20 pages by priority                      │
│ Step 1.3-1.6: Extract patterns (sentence, paragraph, word,    │
│               structural)                                     │
│ Step 1.7: Score frequency (High 80%+ / Med 40-79% / Low <40%)│
│ Step 1.8: Build content-type voice mapping                    │
│ Step 1.9: Generate voice-in-action examples                   │
│ Step 1.10: Identify inconsistencies                           │
│ Step 1.11: Document gaps                                      │
│ ✓ Output: tov-analysis.md                                     │
└──────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌──────────────────────────────────────────────────────────────┐
│ REVIEW GATE: Level 2 (User Review Required)                   │
│ Present: Pattern findings, inconsistencies, gaps              │
│ User Actions: [Confirm] [Correct patterns] [Answer gap Qs]    │
│ → MUST have user review before proceeding to Phase 2          │
└──────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌──────────────────────────────────────────────────────────────┐
│ PHASE 2: GENERATION                                           │
│ Step 2.1: Incorporate user feedback                           │
│ Step 2.2: Generate guidelines (reader, tone, patterns,        │
│           vocabulary, structure, anti-patterns)               │
│ Step 2.3: Add source attribution to every guideline           │
│ ✓ Output: tov-guidelines.md                                   │
└──────────────────────────────────────────────────────────────┘

---

Claude Code Triggers

Invoke this skill when user says:

• "Create TOV guidelines for [URL/company]"
• "Extract voice from website"
• "Build editorial guidelines"
• "Tone of voice document"
• "Brand voice guidelines"
• "Writing style guide for [company]"
• "What's [company]'s voice?"
• "Analyze voice from [URL]"

Do NOT invoke when:

• User wants brand identity guidelines (visual) → Use brand-kit skill
• User wants product messaging → Use product-messaging skill
• User wants to write content → Use appropriate content skill with TOV as input
• User just wants quick voice description → Answer directly

---

Input Requirements

Required Inputs

Input	Description	Source
Company URL	Primary website URL	User provides

Optional Inputs (improve quality)

Input	How It Helps
Founder context	Interview transcript, founder notes, voice preferences
Existing guidelines	Current brand guidelines to incorporate or update
Target audience	Known audience details to validate against content

---

Process (Step-by-Step)

Phase 1: Analysis

Purpose: Extract evidence-based voice findings from website content.

Output: tov-analysis.md

Steps:

1. Step 1.1: Discover site structure

   • Fetch homepage and extract all internal links
   • Filter: same domain only (no third-party links)
   • Prioritize: /about, /manifesto, /values, /blog/, /case-studies/, /pricing, /faq
   • Output: Page list for analysis

2. Step 1.2: Scrape comprehensively

   • Fetch pages in priority order (stop at ~15-20 pages)
   • Priority 1: Homepage (core positioning, primary CTA style)
   • Priority 2: About/Manifesto/Values (origin story, beliefs, personality)
   • Priority 3: Blog posts (3-5) (long-form voice, educational tone)
   • Priority 4: Case studies (2-3) (proof presentation, client voice)
   • Priority 5: Pricing/Services (commercial tone, specificity)
   • Priority 6: FAQ (objection handling, conversational tone)
   • Priority 7: Landing pages (persuasion patterns)
   • Output: Page content collected

3. Step 1.3: Extract sentence-level patterns

   • Average sentence length
   • First-person vs. third-person usage
   • Question frequency
   • Command/imperative usage
   • Output: Sentence-level analysis

4. Step 1.4: Extract paragraph-level patterns

   • Average paragraph length
   • Opening patterns
   • Transition patterns
   • Evidence placement
   • Output: Paragraph-level analysis

5. Step 1.5: Extract word-level patterns

   • Recurring terms (company vocabulary)
   • Industry terms (customer vocabulary)
   • Banned/avoided words
   • Modifier frequency (very, really, extremely)
   • Output: Word-level analysis

6. Step 1.6: Extract structural patterns

   • Header style (sentence case vs. title case)
   • CTA placement and phrasing
   • Proof stacking patterns
   • Section organization
   • Output: Structural analysis

7. Step 1.7: Score frequency

   • High (80%+): Pattern across most page types
   • Medium (40-79%): Pattern on some pages
   • Low (<40%): Pattern appears rarely
   • Conflict: Contradictory patterns found
   • Output: Frequency scores per finding

8. Step 1.8: Build content-type mapping

   • Document how voice varies by content type
   • Note person, formality, CTA style per type
   • Output: Voice variation table

9. Step 1.9: Generate voice-in-action examples

   • Create before/after pairs (generic → on-brand)
   • Output: Transformation examples

10. Step 1.10: Identify inconsistencies

    • Flag conflicts (e.g., homepage uses "I", pricing uses "we")
    • Note as issues to resolve in Phase 2
    • Output: Inconsistency list

11. Step 1.11: Document gaps

    • What couldn't be determined from scraping
    • Suggested founder interview questions
    • Output: Gap inventory

Phase 1 Checkpoint:

• 15+ pages analyzed
• All pattern categories extracted
• Frequency scores assigned
• Inconsistencies flagged
• Gaps documented

User Review Point: Present Phase 1 analysis to user for review before Phase 2.

Phase 2: Generation

Purpose: Create actionable guidelines from validated findings.

Output: tov-guidelines.md

Steps:

1. Step 2.1: Incorporate feedback

   • User reviews Phase 1 analysis
   • Apply corrections to misidentified patterns
   • Resolve inconsistencies
   • Fill gaps with founder answers
   • Output: Validated findings

2. Step 2.2: Generate guidelines document

   • Primary reader definition
   • Tone attributes with examples
   • Pattern library with LLM guidance
   • Vocabulary lists
   • Structure templates
   • Anti-patterns
   • Output: Complete guidelines

3. Step 2.3: Add source attribution

   • Every guideline traces to source URL
   • Frequency scores carried forward
   • Unresolved gaps marked "TBD - requires founder input"
   • Output: Sourced guidelines

Phase 2 Checkpoint:

• All guidelines have source attribution
• Inconsistencies resolved
• Gaps marked or filled
• Document follows template

---

Core Frameworks

Six Critical Questions

Every TOV guidelines document answers:

#	Question	What It Contains
1	Who's actually reading?	Primary reader definition with job title, challenges, goals
2	What does tone sound like?	Demonstrable before/after examples, not adjectives
3	What patterns repeat vs. vary?	Pattern library with LLM-specific guidance
4	What words actually used?	Vocabulary with company terms, customer terms, banned words
5	What structure fits?	Opening patterns, body structure, CTA style
6	What to refuse?	Anti-patterns with specific phrases, claims, tones to avoid

Frequency Scoring

Score	Criteria	Example
High (80%+)	Pattern appears across most page types	"First-person 'I' used on 5 of 6 pages (83%)"
Medium (40-79%)	Pattern appears on some pages	"'I' used on homepage and about, but not blog posts (40%)"
Low (<40%)	Pattern appears rarely	"'We' found once on pricing page (17%)"
Conflict	Contradictory patterns found	"Homepage uses 'I', pricing uses 'we'"

Pattern Categories

Category	What to Extract
Sentence-level	Length, person, questions, imperatives
Paragraph-level	Length, openings, transitions, evidence
Word-level	Company vocabulary, customer vocabulary, banned words
Structural	Headers, CTAs, proof stacking, sections
Content-type	Voice variations by page type
Emotional register	Tone by content moment

---

Output Format

Phase 1 Output (Analysis)

# TOV Analysis: [Company Name]

**Website:** [URL]
**Pages analyzed:** [Count]
**Analysis date:** [Date]

---

## Pages Scraped

| Page | URL | Voice Facets Revealed |
|------|-----|----------------------|
| Homepage | [URL] | Core positioning, primary CTA |
| About | [URL] | Origin story, beliefs |

---

## Pattern Findings

### Sentence-Level Patterns

| Pattern | Finding | Frequency | Source Pages |
|---------|---------|-----------|--------------|
| Average sentence length | [X words] | High/Med/Low | [Pages] |
| Person usage | [First/Third] | High/Med/Low | [Pages] |
| Question frequency | [X per page] | High/Med/Low | [Pages] |

### Word-Level Patterns

**Company vocabulary:**
- [Term 1] — [Frequency] — [Source pages]

**Banned/avoided words:**
- [Word] — [Evidence it's avoided]

---

## Content-Type Voice Mapping

| Content Type | Person | Formality | CTA Style |
|--------------|--------|-----------|-----------|
| Homepage | [I/We/Brand] | [Conversational/Professional] | [Style] |
| Blog posts | [I/We/Brand] | [Conversational/Professional] | [Style] |

---

## Voice-in-Action Examples

| Generic | On-Brand | Principle |
|---------|----------|-----------|
| "[Generic phrasing]" | "[Actual phrasing from site]" | [What makes it distinctive] |

---

## Inconsistencies Found

### [Inconsistency 1]
- **Page A:** "[Finding]" ([URL])
- **Page B:** "[Conflicting finding]" ([URL])
- **Recommendation:** [How to resolve]

---

## Gaps Identified

| Gap | Why It Matters | How to Fill |
|-----|----------------|-------------|
| [Gap] | [Impact] | [Method] |

Phase 2 Output (Guidelines)

# TOV Guidelines: [Company Name]

**Based on analysis of:** [URL]
**Pages analyzed:** [Count]
**Generated:** [Date]

---

## 1. Primary Reader

**Job title:** [Title]
**Challenges:** [Key challenges]
**Goals:** [What they want to achieve]
**How they read:** [Skimming? Deep reading? Mobile?]

---

## 2. Tone Attributes

### [Attribute 1]

**What it means:** [Description]

**Before (generic):**
> "[Generic example]"

**After (on-brand):**
> "[On-brand example from site]"

**Source:** [URL where this pattern appears]

---

## 3. Pattern Library

### Sentence Patterns

| Pattern | Guideline | Source |
|---------|-----------|--------|
| Person | Use "[I/We/Brand]" consistently | [Frequency: X%] |
| Length | Target [X] words average | [Analysis finding] |

### LLM-Specific Guidance

When using this TOV in AI prompts:
- [Specific instruction 1]
- [Specific instruction 2]

---

## 4. Vocabulary

### Always Use

| Term | Context | Source |
|------|---------|--------|
| [Term] | [When to use] | [URL] |

### Never Use

| Term | Say Instead | Source |
|------|-------------|--------|
| [Term] | [Alternative] | [Evidence] |

---

## 5. Structure Templates

### Opening Patterns

**For [content type]:**
[Template structure]
**Source:** [URL]

---

## 6. Anti-Patterns

### Phrases to Avoid

| Don't Say | Why | Say Instead |
|-----------|-----|-------------|
| "[Phrase]" | [Reason] | "[Alternative]" |

### AI-Speak Structural Patterns to Flag

| Pattern | Example | Guideline |
|---------|---------|-----------|
| **False contrast reframe ("X isn't Y. It's Z.")** | "The problem isn't effort. It's coverage." | THE number one AI-speak tell. Rewrite to just state the point directly. |
| **Wrapped bow ending** | "That's the real competitive advantage." | End on questions, open thoughts, or mid-thought instead. |
| **AI transition phrases** | "Here's the thing:" / "Here's why:" | Verbal tics of LLMs. Cut entirely. |
| **Uniform paragraph rhythm** | Every paragraph is 2-3 sentences | Mix lengths wildly. One-word paragraphs. Long ones. |

---

What Good Looks Like

Example: Genesys Growth TOV Analysis

Input context:

URL: https://genesysgrowth.com
Type: B2B SaaS GTM consulting

Expected output excerpt:

### Sentence-Level Patterns

| Pattern | Finding | Frequency | Source Pages |
|---------|---------|-----------|--------------|
| Average sentence length | 12 words | High (87%) | All pages |
| Person usage | First-person "I" | High (83%) | Homepage, About, Services |
| Question frequency | 2-3 per page | Medium (60%) | Homepage, Services |
| Imperative usage | Moderate (CTAs only) | High (90%) | All pages |

### Voice-in-Action Examples

| Generic | On-Brand | Principle |
|---------|----------|-----------|
| "We help companies improve their GTM" | "I help B2B SaaS teams accelerate their GTM without the bloat" | Operator authority + specificity |
| "Contact us to learn more" | "Book a call with Matteo" | Personal name > generic CTA |
| "Our solutions leverage AI" | "I use Clay, Claude, and n8n to build systems" | Name specific tools > buzzwords |

Why this is good:

• Every finding has frequency score
• Source pages cited
• Before/after examples are real, not invented
• Principles extracted from evidence

---

Anti-Hallucination Guardrails

1. Every finding needs source. Include page URL where pattern was found.
2. Frequency scores based on counts. "5 of 6 pages (83%)" not "usually"
3. Examples from actual content. Never invent illustrative examples.
4. Flag inconsistencies. Don't paper over contradictory patterns.
5. Document gaps explicitly. Don't fill gaps with assumptions.

---

Quality Checklist (Pre-Delivery)

Phase 1 Quality

• 15+ pages scraped and analyzed
• All pattern categories have findings
• Frequency scores calculated (not estimated)
• Inconsistencies explicitly flagged
• Gaps documented with founder questions

Phase 2 Quality

• All guidelines trace to Phase 1 findings
• Inconsistencies resolved (or marked pending)
• Examples are from actual scraped content
• Source URLs included for verification

---

MCP Data Integration

Pulls fresh

Source	What to pull	Tool	When
Firecrawl	Content pages (blog, about, docs) for voice analysis	firecrawl_scrape	Always
YouTube	Founder video transcripts for spoken voice	get_transcript	If founder videos exist
Exa	Brand content across external channels	web_search_exa	Always

Fallback (no MCP)

• WebFetch for content page samples
• Manual transcript analysis
• WebSearch for external brand content

---

Reference Files

File	Purpose	Status
references/content-analysis-guide.md	Pattern extraction framework	Core
references/analysis-template.md	Phase 1 output structure	Core
references/tov-template.md	Phase 2 output structure	Core
references/founder-interview-questions.md	Questions to fill gaps	Core
references/quality-checklist.md	Pre-delivery quality checks	Core
examples/genesys-growth-analysis-example.md	Complete Phase 1 example	Example
examples/genesys-growth-guidelines-example.md	Complete Phase 2 example	Example

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Changelog

Version	Date	Changes
2.1	2026-01-21	Agentic enhancements: visual flowchart, self-evaluation protocol, upstream/downstream integration map
2.0	2026-01-16	Refactored to v2.0 template: Claude Code triggers, iteration prompts, auto-update rules. Two-phase workflow preserved.
1.0	Previous	Initial skill creation with two-phase workflow