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Guideline Generation

AnthropicOfficialCreated by Tribe AIDistributed by Anthropic

  • Skill
  • Content generator
  • Documents and content
  • MIT
  • No issues found

From the author

This skill generates, creates, or builds brand voice guidelines from source materials. It should be used when the user asks to "generate brand guidelines", "create a style guide", "extract brand voice", "create guidelines from calls", "consolidate brand materials", "analyze my sales calls for brand voice", "build a brand playbook from documents", "synthesize a voice and tone guide", or uploads brand documents, transcripts, or meeting recordings for brand analysis. Also triggers when the user has a discovery report and wants to convert it into actionable guidelines.
— the description by Tribe AI

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

Guideline Generation transforms fragmented brand assets—such as documents, sales transcripts, and discovery reports—into a unified, LLM-ready brand playbook. It systematically extracts voice attributes, messaging pillars, and tone-by-context matrices while assigning confidence scores to ensure transparency. The skill produces a structured markdown document that serves as a single source of truth for brand identity, complete with 'We Are/We Are Not' anchors and actionable recommendations for team alignment.

What it does

  • Analyzes documents and transcripts to extract consistent brand voice attributes.
  • Creates a structured 'We Are / We Are Not' identity table with supporting evidence.
  • Builds a tone-by-context matrix to guide communication across different channels.
  • Assigns confidence scores to each section based on source corroboration.
  • Identifies ambiguities and generates actionable questions for team resolution.
  • Archives previous versions and saves the final guidelines to a project-specific path.

When to use it

  • When you need to formalize a brand voice from scattered documents and meeting notes.
  • After conducting a brand discovery process to convert findings into a usable playbook.
  • When you want to ensure consistent messaging across sales, marketing, and support teams.
  • When you need to resolve conflicts between different brand materials or communication styles.

Advantages

  • Reduces ambiguity by providing clear, evidence-based brand guidelines.
  • Ensures consistency by distinguishing between fixed voice constants and flexible tone.
  • Provides transparency through confidence scoring for every guideline section.
  • Facilitates team alignment by highlighting open questions that require human decision-making.
  • Integrates directly with other agent tools for immediate enforcement of the generated voice.

Usage examples

“Generate brand guidelines from my uploaded sales transcripts and company mission statement.”
The agent analyzes the provided files, identifies recurring language patterns and core values, and outputs a structured brand playbook with a tone matrix and voice attributes.
“I have a discovery report from our recent branding workshop. Can you turn this into actionable guidelines?”
The agent processes the discovery report as the primary source, synthesizes the findings into the standard template, and flags any areas requiring further team confirmation.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/anthropics/guideline-generation/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip guideline-generation.zip -d .agents/skills

Documentation by Tribe AI

Guideline Generation

Generate comprehensive, LLM-ready brand voice guidelines from any combination of sources — brand documents, sales call transcripts, discovery reports, or direct user input. Transform raw materials into structured, enforceable guidelines with confidence scoring and open questions.

Inputs

Accept any combination of:

  • Discovery report from the discover-brand skill (structured, pre-triaged)

  • Brand documents uploaded or from connected platforms (PDF, PPTX, DOCX, MD, TXT)

  • Conversation transcripts from Gong, Granola, manual uploads, or Notion meeting notes

  • Direct user input about their brand voice and values

When a discovery report is provided, use it as the primary input — sources are already triaged and ranked. Supplement with additional analysis as needed.

Generation Workflow

1. Identify and Classify Sources

Determine what the user has provided. If no sources are available:

  • Check if a discovery report exists from a previous /brand-voice:discover-brand run

  • Check .claude/brand-voice.local.md for known brand material locations

  • Suggest running discovery first: /brand-voice:discover-brand

2. Process Sources

For documents: Delegate to the document-analysis agent for heavy parsing. Extract voice attributes, messaging themes, terminology, tone guidance, and examples.

For transcripts: Delegate to the conversation-analysis agent for pattern recognition. Extract implicit voice attributes, successful language patterns, tone by context, and anti-patterns.

For discovery reports: Extract pre-triaged sources, conflicts, and gaps. Use the ranked sources directly.

3. Synthesize Into Guidelines

Merge all findings into a unified guideline document following the template in references/guideline-template.md. Key sections:

"We Are / We Are Not" Table — The core brand identity anchor:

We AreWe Are Not
[Attribute — e.g., "Confident"][Counter — e.g., "Arrogant"]
[Attribute — e.g., "Approachable"][Counter — e.g., "Casual or sloppy"]

Derive attributes from the most consistent patterns across sources. Each row should have supporting evidence.

Voice Constants vs. Tone Flexes — Clarify what stays fixed and what adapts:

  • Voice = personality, values, "We Are / We Are Not" — constant across all content

  • Tone = formality, energy, technical depth — flexes by context

Tone-by-Context Matrix:

ContextFormalityEnergyTechnical DepthExample
Cold outreachMediumHighLow"[example phrase]"
Enterprise proposalHighMediumHigh"[example phrase]"
Social mediaLowHighLow"[example phrase]"
4. Assign Confidence Scores

Score each section using the methodology in references/confidence-scoring.md:

  • High confidence: 3+ corroborating sources, explicit guidance found

  • Medium confidence: 1-2 sources, or inferred from patterns

  • Low confidence: Single source, inferred, or conflicting data

5. Surface Open Questions

Generate open questions for any ambiguity that cannot be resolved:

## Open Questions for Team Discussion

### High Priority (blocks guideline completion)
1. **[Question Title]**
   - What was found: [conflicting or incomplete info]
   - Agent recommendation: [suggested resolution with reasoning]
   - Need from you: [specific decision or confirmation needed]

Every open question MUST include an agent recommendation. Turn ambiguity into "confirm or override" — never a dead end.

6. Quality Check

Before presenting, verify via the quality-assurance agent (defined in agents/quality-assurance.md):

  • All major sections populated (including Brand Personality and Content Examples if sources support them)

  • At least 3 voice attributes with evidence

  • "We Are / We Are Not" table has 4+ rows

  • Tone matrix covers at least 3 contexts

  • Confidence scores assigned per section

  • Source attribution for all extracted elements

  • No PII exposed

  • Open questions include recommendations

7. Present and Offer Next Steps

Summarize key findings:

  • Total sections generated with confidence breakdown

  • Strongest voice attribute and most effective message

  • Number of open questions (if any)

8. Save for Future Sessions

The default save location is .claude/brand-voice-guidelines.md inside the user's working folder.

Important: The agent's working directory may not be the user's project root (especially in Cowork, where plugins run from a plugin cache directory). Always resolve the path relative to the user's working folder, not the current working directory. If no working folder is set, skip the file save and tell the user guidelines will only be available in this conversation.

  1. Resolve the save path. The file MUST be saved to .claude/brand-voice-guidelines.md inside the user's working folder. Confirm the working folder path before writing.

  2. Check if guidelines already exist at that path

  3. If they exist, archive the previous version: Rename the existing file to brand-voice-guidelines-YYYY-MM-DD.md in the same directory (using today's date)

  4. Save new guidelines to .claude/brand-voice-guidelines.md inside the working folder

  5. Confirm to the user with the full absolute path: "Guidelines saved to <full-path>. /brand-voice:enforce-voice will find them automatically in future sessions."

The guidelines are also present in this conversation, so /brand-voice:enforce-voice can use them immediately without loading from file.

After saving, offer:

  1. Walk through the guidelines section by section

  2. Start creating content with /brand-voice:enforce-voice

  3. Resolve open questions

Privacy and Security

Enforce these privacy constraints throughout the entire generation workflow, not only at output time:

  • Redact customer names and contact information from all examples

  • Anonymize company names in transcript excerpts if requested

  • Flag any sensitive information detected during processing

Reference Files

  • references/guideline-template.md — Complete output template with all sections, field definitions, and formatting guidance

  • references/confidence-scoring.md — Confidence scoring methodology, thresholds, and examples

Files

references/guideline-template.md

Brand Guidelines Output Template

The complete structure for generated brand voice guidelines. Fill each section based on discovered and analyzed sources. The following is a raw markdown template — copy and fill the sections below. Omit sections with no data rather than including empty placeholders.

[Company Name] Brand Voice Guidelines

Generation Metadata

  • Created: [date]

  • Version: [N] (incremented from previous if updating)

  • Replaces: [previous version date, if applicable]

  • Sources: [list of documents and/or conversations analyzed]

  • Documents processed: [N]

  • Conversations analyzed: [N]

  • Discovery report used: [Yes/No]

  • Overall confidence: [High/Medium/Low]


Executive Summary

[2-3 paragraph overview of the brand voice. What makes this brand distinctive? What is the core personality? How should content feel to the reader?]


We Are / We Are Not

The foundational brand identity anchor. Voice is constant — it doesn't change by channel or audience.

We AreWe Are Not
[Attribute 1] — [brief description][Counter 1] — [what to avoid]
[Attribute 2] — [brief description][Counter 2] — [what to avoid]
[Attribute 3] — [brief description][Counter 3] — [what to avoid]
[Attribute 4] — [brief description][Counter 4] — [what to avoid]
[Attribute 5] — [brief description][Counter 5] — [what to avoid]
Voice Attributes Detail

For each "We Are" attribute:

[Attribute 1]: [Name]
  • What it means: [detailed description]

  • How it shows up: [specific behaviors in content]

  • What to avoid: [the "We Are Not" counterpart, expanded]

  • Evidence: [source quote or pattern, with citation]

  • Confidence: [High/Medium/Low]

[Repeat for each attribute]


Brand Personality

  • Archetype: [e.g., "The Expert Friend" — technically deep but never condescending]

  • If our brand were a person: [brief personality sketch]

  • Core values expressed in voice: [list]


Messaging Framework

Primary Value Proposition

[Core value statement — one sentence]

Variations observed:

  • "[Variation 1]" (Source: [doc])

  • "[Variation 2]" (Source: [doc])

Key Message Pillars
  1. [Pillar Name]

    • Core idea: [one sentence]

    • When to use: [contexts]

    • Example phrasing: "[quote]"

    • Frequency in conversations: [X]% (if transcript data available)

    • Effectiveness: [High/Medium/Low] (if outcome data available)

  2. [Pillar Name] [same format]

  3. [Pillar Name] [same format]

Competitive Positioning
  • vs. [Competitor A]: [how we differentiate]

  • vs. [Competitor B]: [how we differentiate]

  • vs. Status Quo: [why change is worth it]


Tone-by-Context Matrix

Voice is constant. Tone flexes by context. These three dimensions adjust:

ContextFormalityEnergyTechnical DepthKey Principle
Cold outreachMediumHighLowHook fast, earn attention
Discovery callsMediumMedium-HighMediumAsk more than tell
Demo / presentationMedium-HighHighHighShow, don't just describe
Enterprise proposalHighMediumHighROI and precision
Follow-up emailMediumMediumLow-MediumAdd new value each touch
Social mediaLow-MediumHighLowBrevity and personality
Customer successMediumWarmMediumEmpathy and competence
Internal commsLowMediumVariesAuthentic, less polished
Context-Specific Guidelines
Cold Outreach
  • Overall tone: [description]

  • Opening approach: [what works]

  • Do's: [list]

  • Don'ts: [list]

  • Example: "[sample opening line]"

Discovery Calls

[same structure]

Proposals & RFPs

[same structure]

Social Media

[same structure]


Terminology Guide

Must-Use Terms
TermUsageInstead OfExample
[term][when/how to use][what it replaces]"[in a sentence]"
Preferred Terms
TermUsageExample
[term][guidance]"[in a sentence]"
Avoid These Terms
TermReasonAlternative
[term][why to avoid][what to use instead]
Never-Use Terms
TermReason
[term][why this is prohibited]

Language That Works

Ranked by effectiveness (from conversation analysis, if available):

Top Phrases
  1. "[Phrase]" — Context: [when to use], Why it works: [analysis]

  2. "[Phrase]" — Context: [when to use], Why it works: [analysis]

Questions That Engage
  1. "[Question]" — Leads to: [typical outcome]

  2. "[Question]" — Leads to: [typical outcome]

Objection Handling Patterns
  1. Objection: "[common objection]" Response pattern: "[how top performers respond]" Why it works: [analysis]


Language to Avoid

Anti-Patterns
  1. "[Phrase/pattern]" — Problem: [what happens], Better: "[alternative]"

  2. "[Phrase/pattern]" — Problem: [what happens], Better: "[alternative]"


Content Examples

Excellent Examples

[Full example with annotation explaining what makes it on-brand]

Examples to Avoid

[Full example with annotation explaining what's off-brand and how to fix it]


Confidence Scores

SectionConfidenceBasisSources
Voice Attributes[H/M/L][why][N sources]
Messaging Framework[H/M/L][why][N sources]
Tone Matrix[H/M/L][why][N sources]
Terminology[H/M/L][why][N sources]
Language Patterns[H/M/L][why][N sources]

Open Questions for Team Discussion

High Priority (blocks guideline completion)
  1. [Question Title]

    • What was found: [description of ambiguity]

    • Agent recommendation: [suggested resolution]

    • Need from you: [specific decision]

Medium Priority (improves quality)

[same format]

Low Priority (nice to have)

[same format]


Data Gaps & Recommendations

[What's missing and specific actions to fill gaps]

  • [ ] [Gap 1]: [recommendation to fill it]

  • [ ] [Gap 2]: [recommendation to fill it]


Appendix: Sources

#SourcePlatformTypeDateKey Sections UsedConfidence
1[title][platform][AUTHORITATIVE/OPERATIONAL/etc.][date][sections][H/M/L]

Template Usage Notes

  • Omit sections with no data rather than including empty placeholders

  • If transcript data is unavailable, omit the "Language That Works" and "Language to Avoid" sections

  • If only documents are available (no transcripts), note this in metadata

  • "We Are / We Are Not" table should have minimum 4 rows, ideally 5-7

  • Tone matrix should cover at minimum: cold outreach, proposals, social media

  • All examples must have source attribution

  • Every open question must include an agent recommendation

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

No issues found by our static scan· No issues found

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
Tribe AI
Publisher
Anthropic (Official)
Distributed by
Anthropic
Source
anthropics/knowledge-work-plugins · partner-built/brand-voice/skills/guideline-generation · 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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