tov-guidelines

name	tov-guidelines
version	2.1
last_updated	"2026-01-21T00:00:00.000Z"
author	marketing-team
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). Triggers: "tone of voice", "brand voice", "voice guidelines", "writing guidelines", "TOV audit", or starting a new content program. Upstream: recommended company-context, win-loss-analysis, competitor-research. Downstream: feeds linkedin-content, landing-page-copy, product-messaging, outreach-emails, and all content skills. NOT for visual brand tokens (use /brand-kit) TOV doc).
goal	Analyzes existing client content (website, blog, social, docs) to extract voice patterns, vocabulary preferences, frequency scores, and do/don't usage rules.
outcome	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...
primitive	research
ontology_type	tone-of-voice
review_gate	2
inputs	{"required":[],"recommended":["company-context"]}
outputs	[{"type":"tov-analysis","feeds_into":[]},{"type":"tov-guidelines","feeds_into":["website-copy","linkedin-weekly-content","product-messaging","sales-enablement","outreach-emails"]}]
depends_on	[]
feeds_into	["website-copy","linkedin-weekly-content","outreach-emails","product-messaging","sales-enablement"]
owned_by_agent	researcher
mcps_used	["exa","firecrawl"]
push_targets	["gdrive","notion"]
triggers	{"slash_commands":["/tov-guidelines"],"natural_language":[]}
status	draft
locked_by	null
locked_date	null
lock_version	null
sources_count	0
context	fork
effort	high

TOV guidelines

Extract actionable editorial tone of voice guidelines from a company's existing content. Two-phase workflow: Phase 1 analyzes website content to surface evidence-based voice patterns; Phase 2 codifies those patterns into guidelines that downstream content skills consume. User review gate sits between the two phases.

When to run

• User says "tone of voice", "brand voice", "voice guidelines", "writing style guide", or asks to extract voice from a URL.
• Starting a new content program for a client and downstream skills (linkedin-content, landing-page-copy, product-messaging, outreach-emails) need a voice contract.
• Existing TOV is stale (>6 months) or content has drifted from documented voice.

Don't run when: user wants visual brand identity (use brand-kit), product messaging (use product-messaging), to write content directly (use the content skill with TOV as input), or just a quick voice description (answer directly).

Inputs

Input	Required	Source
Company URL	yes	User
Founder context (transcript, notes, voice preferences)	optional	User / company-context
Existing brand guidelines (to update)	optional	User
Target audience	optional	icp-research / user

Validate before proceeding: URL is accessible, site has homepage + 5-10 pages of content. If thin, flag sample-size limitation in output.

Research substrate (Exa): primary tool web_search_exa for founder voice harvesting via site:linkedin.com / site:twitter.com filters. Per .claude/rules/exa-protocol.md. Citation: [VERIFIED: exa_search, {url}, accessed {YYYY-MM-DD}]. Quality gate: ≥3 sources per major claim, ≥50% [VERIFIED].

Steps

Two phases. Phase 1 outputs tov-analysis.md. User reviews. Phase 2 outputs tov-guidelines.md.

Phase 1 — Analysis

1. Discover site structure — fetch homepage, extract internal links, filter same-domain only, prioritize /about, /manifesto, /values, /blog/*, /case-studies/*, /pricing, /faq.
2. Scrape 15-20 pages in priority order: homepage → about/values → blog (3-5) → case studies (2-3) → pricing → FAQ → landing pages.
3. Extract sentence-level patterns — average length, person (1st/3rd), question frequency, imperative usage.
4. Extract paragraph-level patterns — average length, openings, transitions, evidence placement.
5. Extract word-level patterns — company vocabulary, customer/industry vocabulary, banned/avoided words, modifier frequency.
6. Extract structural patterns — headers (sentence vs title case), CTA placement and phrasing, proof stacking, section organization.
7. Score frequency — High (80%+ of pages), Medium (40-79%), Low (<40%), Conflict (contradictory).
8. Build content-type voice mapping — table of person/formality/CTA per page type (homepage, blog, case study, pricing).
9. Generate voice-in-action examples — generic → on-brand transformations, drawn from actual scraped text (never invented).
10. Identify inconsistencies — flag conflicts (e.g., homepage uses "I", pricing uses "we") for Phase 2 resolution.
11. Document gaps — what couldn't be determined; suggest founder interview questions.
12. Write tov-analysis.md using references/analysis-template.md (44-section canonical scaffold; compact alternative in same file).
13. Present to user — Review gate. User must confirm patterns, correct misidentifications, and answer gap questions before Phase 2.

Phase 2 — Generation

14. Incorporate user feedback — apply corrections, resolve inconsistencies, fill gaps with founder answers.
15. Generate tov-guidelines.md using references/tov-template.md — primary reader, tone attributes with before/after, pattern library with LLM guidance, vocabulary lists, structure templates, anti-patterns. Always include the AI-speak anti-patterns table from references/ai-speak-anti-patterns.md.
16. Add source attribution — every guideline traces to a source URL; frequency scores carried forward; unresolved gaps marked "TBD — requires founder input".
17. Self-evaluate per references/self-evaluation.md (completeness, evidence, guardrails, self-roast). Surface improvement prompts to user when checks fail.
18. Save approved output as reference example if user explicitly approves — see references/auto-update-protocol.md.

What good looks like

References

• references/process-flowchart.md — full two-phase visual flowchart with review gate
• references/content-analysis-guide.md — pattern extraction framework (site discovery, scraping, scoring)
• references/analysis-template.md — Phase 1 output structure (44-section canonical + compact alternative scaffold)
• references/tov-template.md — Phase 2 output structure (canonical + compact alternative scaffold)
• references/founder-interview-questions.md — questions to fill gaps from scraping
• references/quality-checklist.md — pre-delivery verification (Phase 1 + Phase 2)
• references/ai-speak-anti-patterns.md — universal AI-speak table to include in every TOV output
• references/self-evaluation.md — completeness + evidence + guardrail checks, self-roast questions, improvement prompts
• references/auto-update-protocol.md — feedback signal detection, save-as-reference flow, pattern detection rules
• references/integration-map.md — feeds-into / receives-from skill graph + chain sequences

Examples

• references/examples/ — user-approved TOV outputs from prior runs (auto-populated by approval flow)

Evaluations

A TOV output passes when:

• 15+ pages scraped, all pattern categories (sentence, paragraph, word, structural) have findings.
• Frequency scores based on actual counts (e.g., "5 of 6 pages, 83%"), not "usually" or "often".
• Every guideline traces to a source URL.
• All examples are from actual scraped text (no invented illustrative examples).
• Inconsistencies explicitly flagged, not papered over; gaps documented with founder questions.
• Phase 2 includes the AI-speak anti-patterns table (false contrast, wrapped bow, "Here's the thing", uniform rhythm, max-1 negation setup).
• No adjective-only descriptions ("friendly", "professional"); no vague guidelines ("use conversational language").
• All 6 critical questions answered: who reads / what tone sounds like / patterns repeat vs vary / words used / structure / what to refuse.

Push

After delivery, offer downstream chains:

• "Want me to apply this TOV to landing-page-copy?"
• "Ready to generate linkedin-content with this voice?"
• "Should I run product-messaging with these guidelines as voice contract?"
• "Should I analyze competitor voice for differentiation contrast?"

If user signals approval ("great guidelines", "this captures our voice"): offer to save as reference example per references/auto-update-protocol.md.

If user corrects patterns: log the correction. After 3+ similar corrections across runs, propose a SKILL.md update.

Reads from upstream: company-context (positioning), win-loss-analysis (how team actually speaks), competitor-research (differentiation contrast). Feeds into: landing-page-copy, linkedin-content, product-messaging, sales-enablement, outreach-emails, aeo-content, thought-leadership, newsletter. Full graph: references/integration-map.md.

MCP substrate: Firecrawl (firecrawl_scrape) for page content; YouTube get_transcript for founder spoken voice if videos exist; Exa web_search_exa for external brand content. Fallbacks: WebFetch, manual transcript analysis, WebSearch (flag as data gap).