Guideline Generation

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:

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:

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