Brand Voice Enforcement
AnthropicOfficialCreated by Tribe AIDistributed by Anthropic
- Skill
- Content generator
- Business and productivity
- MIT
- No issues found
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
Brand Voice Enforcement acts as a quality control layer for content creation. It ensures that every piece of writing—from cold emails to executive proposals—aligns with your established brand personality. By utilizing a 'voice constant, tone flexes' model, the skill maintains your core identity while adapting formality, energy, and technical depth to suit specific audiences and content types. It integrates with your existing guidelines to provide consistent, on-brand messaging while offering explanations for its stylistic choices.
What it does
- Loads brand guidelines from session context or local project files.
- Applies 'We Are / We Are Not' personality attributes to ensure consistency.
- Adjusts tone dimensions (formality, energy, technical depth) based on content type.
- Validates content against approved terminology and messaging frameworks.
- Provides clear explanations for brand-aligned editorial decisions.
- Handles conflicts by recommending adaptations or providing alternative options.
When to use it
- When drafting sales emails, pitch decks, or marketing copy.
- When you need to ensure content sounds like your brand identity.
- When you want to refine existing drafts to better match your established tone.
- When you need to resolve conflicts between a specific request and brand standards.
Advantages
- Maintains a unified brand personality across all communication channels.
- Reduces the time spent manually editing content for tone and style.
- Provides data-driven and context-aware adjustments rather than generic rewriting.
- Offers transparency by explaining why specific brand choices were made.
Usage examples
- “Write a cold outreach email to a VP of Sales using our brand voice.”
- The agent generates an email that leads with a bold, data-driven claim, uses high-energy active verbs, and maintains a professional yet direct tone consistent with your brand guidelines.
- “Rewrite this LinkedIn post to be more on-brand.”
- The agent analyzes the post, identifies where it deviates from your 'We Are' attributes, and revises it to be more confident and approachable while removing prohibited terminology.
- “Does this proposal sound like us?”
- The agent reviews the proposal against your guidelines, highlights specific sections that align or conflict with your voice, and suggests edits to better reflect your brand's personality.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/anthropics/brand-voice-enforcement/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip brand-voice-enforcement.zip -d .agents/skills
Documentation by Tribe AI
Brand Voice Enforcement
Apply existing brand guidelines to all sales and marketing content generation. Load the user's brand guidelines, apply voice constants and tone flexes to the content request, validate output, and explain brand choices.
Loading Brand Guidelines
Find the user's brand guidelines using this sequence. Stop as soon as you find them:
Session context — Check if brand guidelines were generated earlier in this session (via
/brand-voice:generate-guidelines). If so, they are already in the conversation. Use them directly. Session-generated guidelines are the freshest and reflect the user's most recent intent.Local guidelines file — Check for
.claude/brand-voice-guidelines.mdinside the user's working folder. Do NOT use a relative path from the agent's current working directory — in Cowork, the agent runs from a plugin cache directory, not the user's project. Resolve the path relative to the user's working folder. If no working folder is set, skip this step.Ask the user — If none of the above found guidelines, tell the user: "I couldn't find your brand guidelines. You can:
Run
/brand-voice:discover-brandto find brand materials across your platformsRun
/brand-voice:generate-guidelinesto create guidelines from documents or transcriptsPaste guidelines directly into this chat or point me to a file"
Wait for the user to provide guidelines before proceeding.
Also read .claude/brand-voice.local.md for enforcement settings (even if guidelines came from another source):
strictness: strict | balanced | flexiblealways-explain: whether to always explain brand choices
Enforcement Workflow
1. Analyze the Content Request
Before writing, identify:
Content type: email, presentation, proposal, social post, message, etc.
Target audience: role, seniority, industry, company stage
Key messages needed: which message pillars apply
Specific requirements: length, format, tone overrides
2. Apply Voice Constants
Voice is the brand's personality — it stays constant across all content:
Apply "We Are / We Are Not" attributes from guidelines
Use brand personality consistently
Incorporate approved terminology; reject prohibited terms
Follow messaging framework and value propositions
Refer to references/voice-constant-tone-flexes.md for the "voice constant, tone flexes" model.
3. Flex Tone for Context
Tone adapts by content type and audience. Use the tone-by-context matrix from guidelines to set:
Formality: How formal or casual should this be?
Energy: How much urgency or enthusiasm?
Technical depth: How detailed or accessible?
4. Generate Content
Create content that:
Matches brand voice attributes throughout
Follows tone guidelines for this specific content type
Incorporates key messages naturally (not forced)
Uses preferred terminology
Mirrors the quality and style of guideline examples
For complex or long-form content, delegate to the content-generation agent (defined in agents/content-generation.md).
For high-stakes content, delegate to the quality-assurance agent (defined in agents/quality-assurance.md) for validation.
5. Validate and Explain
After generating content:
Briefly highlight which brand guidelines were applied
Explain key voice and tone decisions
Note any areas where guidelines were adapted for context
Offer to refine based on feedback
When always-explain is true in settings, include brand application notes with every response.
Handling Conflicts
When the user's request conflicts with brand guidelines:
Explain the conflict clearly
Provide a recommendation
Offer options: follow guidelines strictly, adapt for context, or override
Default to adapting guidelines with an explanation of the tradeoff.
Open Questions Awareness
Open questions are unresolved brand positioning decisions flagged during guideline generation, stored in the guidelines under an "Open Questions" section. When generating content, check if the brand guidelines contain open questions:
If content touches an unresolved open question, note it
Apply the agent's recommendation from the open question unless the user specifies otherwise
Suggest resolving the question if it significantly impacts the content
Reference Files
references/voice-constant-tone-flexes.md— The "voice constant, tone flexes" mental model, "We Are / We Are Not" table structure, and tone-by-context matrix explanationreferences/before-after-examples.md— Before/after content examples per content type showing enforcement in practice
Files
- ATTRIBUTION.mdAdded by AutoGineer1.1 KiB
- LICENSELicence file added by AutoGineer1.0 KiB
- SKILL.md5.3 KiB
- references/before-after-examples.md7.3 KiB
- references/voice-constant-tone-flexes.md5.7 KiB
references/before-after-examples.md
Before/After Content Examples
Concrete examples showing how brand voice enforcement transforms generic content into on-brand output. Each example shows the generic version, the enforced version, and annotations explaining what changed and why.
These examples use a fictional B2B SaaS company for illustration. During enforcement, replace all specifics (data points, product names, metrics) with data from the user's actual brand guidelines and context.
Cold Outreach Email
Before (Generic)
Subject: Quick question about your sales process
Hi [Name],
I wanted to reach out because I think our product could be a great fit
for your team. We offer an AI-powered platform that helps sales teams
be more productive.
Would you be open to a quick 15-minute call to learn more?
Best,
[Sender]After (Brand Voice Enforced)
Subject: [Company] is leaving pipeline on the table
Hi [Name],
Your team closed $12M last quarter — impressive. But our data shows
mid-market SaaS teams like yours typically lose 23% of qualified pipeline
to slow follow-ups.
[Product] fixes that. We help sales teams respond to buying signals in
real time, not hours later. [Customer X] saw their win rate jump 18%
in the first quarter.
Worth 15 minutes to see if the numbers apply to [Company]?
[Sender]What Changed
Voice: Confident — leads with a bold claim and specific data (not "I think our product could be")
Voice: Data-driven — concrete numbers (23%, 18%, $12M) replace vague "more productive"
Voice: Direct — gets to the point immediately, no "I wanted to reach out"
Tone: Energy HIGH — active language, short sentences, urgency
Tone: Formality MEDIUM — professional but conversational
Tone: Technical depth LOW — outcomes and impact, not feature descriptions
Terminology: "buying signals" and "pipeline" (industry-specific but accessible to VP Sales)
Follow-Up Email
Before (Generic)
Hi [Name],
Just following up on my previous email. I'd love to schedule a call
to discuss how we can help your team.
Let me know if you're available this week.
Thanks,
[Sender]After (Brand Voice Enforced)
Hi [Name],
Quick update: we just published our Q4 benchmark report on enterprise
sales velocity. Three insights jumped out that are relevant to [Company]:
1. Teams using real-time signals close 31% faster than the industry average
2. The #1 pipeline killer isn't competition — it's response time
3. Mid-market wins are trending 15% larger when reps personalize within 2 hours
Happy to walk through how these benchmarks compare to your team's numbers.
The report is attached if you'd rather dig in yourself first.
[Sender]What Changed
Voice: Data-driven — new value in every touch (benchmark data), not just "following up"
Voice: Approachable — offers two paths (call or self-serve) without pressure
Voice: Confident — shares proprietary insights, positions as expert
Tone: Energy MEDIUM — informative, not pushy
Tone: Formality MEDIUM — professional but not stiff
Key principle applied: "Add new value each touch" — no empty follow-ups
Enterprise Proposal Executive Summary
Before (Generic)
We are pleased to submit this proposal for your consideration. Our
company offers a comprehensive AI solution that can help improve your
sales team's performance. We believe our platform is the best choice
for organizations looking to modernize their sales operations.After (Brand Voice Enforced)
[Company] processes 2,400 leads per quarter with a 12-person sales team.
Current pipeline-to-close conversion sits at 18% — below the 24% benchmark
for enterprise SaaS at your ARR stage.
This proposal outlines how [Product] closes that gap. Our platform
analyzes buying signals across your existing tech stack and surfaces
the 3-5 daily actions most likely to advance each deal. Typical results:
+31% win rate, -40% time to close, 2.1x pipeline per rep.
Implementation takes 3 weeks. ROI breaks even at week 6.What Changed
Voice: Data-driven — opens with the client's own metrics, not "we are pleased"
Voice: Direct — no filler language, every sentence carries information
Voice: Confident — specific outcomes with numbers, clear implementation timeline
Tone: Formality HIGH — structured, precise language
Tone: Energy MEDIUM — measured confidence, not hype
Tone: Technical depth HIGH — specific features (buying signals, tech stack analysis), ROI math
LinkedIn Post
Before (Generic)
Excited to announce our new feature! We've been working hard on this
and we think it's going to change the way sales teams work. Check out
the link below to learn more! #sales #AI #innovationAfter (Brand Voice Enforced)
The average sales rep spends 4.2 hours/day on tasks that don't close deals.
We analyzed 50,000 sales activities across 200 teams and found the pattern:
top performers aren't working harder — they're responding faster.
Today we're launching Signal Score: real-time prioritization that tells
your reps exactly which deal to work next, and why.
Early results from beta: 23% fewer lost deals from delayed follow-up.
Link in comments.What Changed
Voice: Data-driven — opens with a statistic, not "excited to announce"
Voice: Confident — specific research (50,000 activities, 200 teams)
Voice: Innovative — describes what's new without hype
Voice: NOT hype-driven — no "change the way sales teams work", replaced with measurable outcomes
Tone: Formality LOW-MEDIUM — casual enough for LinkedIn, professional enough for B2B
Tone: Energy HIGH — short sentences, bold opening stat
Tone: Technical depth LOW — outcomes-focused, accessible to any reader
Terminology: Avoided "revolutionary" and "game-changing" (prohibited terms)
Internal Slack Message
Before (Generic)
Hey team, just a reminder that we should all be using the approved
messaging when talking to prospects. Please refer to the brand
guidelines document for more information.After (Brand Voice Enforced)
Heads up — we updated the competitive positioning section in the brand
guidelines. Two big changes:
1. Against [Competitor]: lead with implementation speed (3 weeks vs. their 3 months)
2. Against Status Quo: new stat — teams using manual processes lose 23% of pipeline to timing
Updated doc is pinned in #sales-enablement. Questions? Tag me.What Changed
Voice: Direct — specific changes, not vague "please refer to"
Voice: Data-driven — concrete differentiators with numbers
Voice: Approachable — casual, helpful tone appropriate for Slack
Tone: Formality LOW — contractions, short sentences, "tag me"
Tone: Energy MEDIUM — informative without being overly enthusiastic
Key principle: Internal comms can be less polished, but voice attributes still apply
Using These Examples
When enforcing brand voice, reference the relevant content type example to calibrate:
Match the content type to the closest example above
Note which voice attributes are most active for that type
Apply the tone settings (formality, energy, technical depth)
Check "We Are Not" boundaries — make sure content doesn't cross them
Verify terminology compliance (preferred terms used, prohibited terms absent)
Requirements and compatibility
Written for: Claude Code, Claude apps
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Security
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Attribution and licence
- Created by
- Tribe AI
- Publisher
- Anthropic (Official)
- Distributed by
- Anthropic
- Source
- anthropics/knowledge-work-plugins · partner-built/brand-voice/skills/brand-voice-enforcement · commit e050c2e
- Licence
- MIT License (redistribution allowed)
- LICENSE (added by AutoGineer from partner-built/brand-voice/LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
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