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skill Claude Code CX Codex Cursor GitHub Copilot official · Anthropic

guideline-generation — Claude Code skill: what it does, how to install

Builds brand voice guidelines and tone matrices from source documents, transcripts, and discovery reports. Useful for codifying brand rules into LLM-ready markdown files.

Install into Claude Code · Skills CLI (Vercel) All methods and platforms ↓
npx skills add anthropics/knowledge-work-plugins --skill guideline-generation -a claude-code
Open on GitHub

What guideline-generation does

The skill transforms unstructured source files into a unified brand style guide. It processes documents in PDF, PPTX, DOCX, or markdown, as well as conversation transcripts from Gong, Granola, or Notion. To extract insights reliably, it routes source parsing to specialized sub-agents: document-analysis for explicit messaging themes and conversation-analysis for speech patterns.

Generated outputs follow the structure in references/guideline-template.md. This includes a core "We Are / We Are Not" table, a tone matrix across communication contexts, and confidence scores per section. If source data reveals unresolved ambiguities, the skill formulates open questions with specific agent recommendations instead of leaving blank sections.

Output files default to .claude/brand-voice-guidelines.md in the user's working directory, archiving older versions by appending the current date. If no working directory is configured in the environment, the skill skips writing to disk and keeps the rules accessible only within the active chat session.

What it helps with

  • extract brand voice traits from recorded customer calls
  • build a tone-by-context matrix for proposals, outreach, and social posts
  • convert discovery reports into structured markdown style guides
  • highlight brand messaging conflicts and propose concrete resolutions

Example prompts for the agent

  • generate brand guidelines from uploaded sales transcripts and documents
  • synthesize a voice and tone guide from our latest discovery report
  • analyze my sales calls for brand voice and build a brand playbook

How to install guideline-generation

1Skills CLI (Vercel)

npx skills add anthropics/knowledge-work-plugins --skill guideline-generation -a claude-code

2Manually: put the files in place

~/.claude/skills/guideline-generation/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/guideline-generation/

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Questions about guideline-generation

How to install guideline-generation in Claude Code?

Run npx skills add anthropics/knowledge-work-plugins --skill guideline-generation -a claude-code. The skill will be registered directly in your workspace.

Where does the skill store generated guidelines?

It writes to .claude/brand-voice-guidelines.md inside your working folder. If an existing guide is detected, it archives the old file as brand-voice-guidelines-YYYY-MM-DD.md before saving.

How are confidence scores calculated for each section?

High confidence requires explicit guidance corroborated by three or more sources. One or two sources yield medium confidence, while single-source inferences and unresolved contradictions receive low confidence ratings.

Source text

SKILL.md · Apache-2.0
---
name: guideline-generation
description: >
  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.
---

# 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:

```markdown
## 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

© anthropics · Apache-2.0 · anthropics/knowledge-work-plugins

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