harsh040506/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:

| We Are | We 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:

| Context | Formality | Energy | Technical Depth | Example | |---------|-----------|--------|-----------------|---------| | Cold outreach | Medium | High | Low | "[example phrase]" | | Enterprise proposal | High | Medium | High | "[example phrase]" | | Social media | Low | High | Low | "[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