Guideline Generation
AnthropicOfficialCreated by Tribe AIDistributed by Anthropic
- Skill
- Content generator
- Documents and content
- MIT
- No issues found
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
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/anthropics/guideline-generation/downloadUnzip 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-brandrunCheck
.claude/brand-voice.local.mdfor known brand material locationsSuggest 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.
Resolve the save path. The file MUST be saved to
.claude/brand-voice-guidelines.mdinside the user's working folder. Confirm the working folder path before writing.Check if guidelines already exist at that path
If they exist, archive the previous version: Rename the existing file to
brand-voice-guidelines-YYYY-MM-DD.mdin the same directory (using today's date)Save new guidelines to
.claude/brand-voice-guidelines.mdinside the working folderConfirm to the user with the full absolute path: "Guidelines saved to
<full-path>./brand-voice:enforce-voicewill 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:
Walk through the guidelines section by section
Start creating content with
/brand-voice:enforce-voiceResolve 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 guidancereferences/confidence-scoring.md— Confidence scoring methodology, thresholds, and examples
Files
- ATTRIBUTION.mdAdded by AutoGineer1.1 KiB
- LICENSELicence file added by AutoGineer1.0 KiB
- SKILL.md6.9 KiB
- references/confidence-scoring.md4.9 KiB
- references/guideline-template.md7.0 KiB
references/confidence-scoring.md
Confidence Scoring Methodology
How to assign and interpret confidence scores for generated brand guidelines.
Scoring Levels
High Confidence
The guideline section is well-supported and actionable.
Criteria (must meet at least 3):
3+ corroborating sources
Explicit guidance found in at least one AUTHORITATIVE source
Consistent across document and conversation analysis
Specific, actionable instructions (not just vague principles)
No unresolved conflicts
Example: Voice attribute "Confident but not arrogant" appears in the official style guide, is demonstrated in email templates, and matches patterns in top performer calls.
Medium Confidence
The section is reasonable but could benefit from more data or team confirmation.
Criteria (must meet at least 2):
1-2 corroborating sources
Inferred from patterns rather than explicit instruction
Minor inconsistencies resolved via recency or authority
Actionable but some interpretation was required
May have one unresolved conflict
Example: Tone for social media inferred from email templates and one Slack thread, but no official social media guidelines exist.
Low Confidence
The section is a best-effort recommendation. Team review strongly recommended.
Criteria (must meet at least 2):
Single source only
Primarily inferred from indirect evidence
Significant interpretation required
Unresolved conflicts between sources
Limited specificity
Example: Competitive positioning derived from a single sales call where a competitor was discussed, with no supporting documentation.
Section-Level Scoring Guide
Voice Attributes
High: Attributes appear in official brand guide AND are demonstrated in templates or calls
Medium: Attributes appear in one document type only, or are inferred from multiple conversations
Low: Attributes inferred from a single source or from indirect evidence
Messaging Framework
High: Value propositions documented in official materials AND used consistently in sales conversations
Medium: Documented but not observed in practice, OR observed but not documented
Low: Extracted from a single pitch deck or single call
Tone Matrix
High: Explicit tone guidance exists for the context AND matches observed behavior
Medium: Tone inferred from 3+ examples of content in that context
Low: Tone inferred from 1-2 examples, or extrapolated from similar contexts
Terminology
High: Terms explicitly listed in a style guide or glossary
Medium: Terms consistently used in templates and calls (pattern-based)
Low: Terms observed in a single document or inferred from brand personality
Language Patterns (from transcripts)
High: Pattern observed in 5+ calls across multiple speakers
Medium: Pattern observed in 3-4 calls or from a single top performer
Low: Pattern observed in 1-2 calls only
Transcript-Primary Scenarios
When guidelines are generated primarily from conversational sources (no AUTHORITATIVE documents available):
Voice Attributes derived from 5+ transcripts = Medium (not Low)
Messaging Framework from consistent patterns across 5+ calls = Medium
Language Patterns weight increases from 10% to 20% in aggregate calculation (subtract 10% from Voice Attributes)
Note this in the guideline metadata: "Guidelines generated primarily from conversational sources — team review recommended to formalize."
Aggregate Confidence
Calculate overall guideline confidence as the weighted average of section scores:
| Section | Weight |
|---|---|
| Voice Attributes | 30% |
| Messaging Framework | 25% |
| Tone Matrix | 20% |
| Terminology | 15% |
| Language Patterns | 10% |
Convert scores: High = 1.0, Medium = 0.6, Low = 0.3
Example:
Voice Attributes: High (1.0 x 0.30 = 0.30)
Messaging: Medium (0.6 x 0.25 = 0.15)
Tone: Medium (0.6 x 0.20 = 0.12)
Terminology: High (1.0 x 0.15 = 0.15)
Language: Low (0.3 x 0.10 = 0.03)
Overall: 0.75 = Medium-High confidence
Aggregate score thresholds:
0.85–1.0 = High
0.60–0.84 = Medium
Below 0.60 = Low
Presentation
Present confidence alongside each section header:
## Voice Attributes (Confidence: High)
[content]
## Tone Matrix (Confidence: Medium)
[content — note: no official social media guidelines found, tone inferred from email patterns]For Medium and Low confidence sections, include a brief note explaining why confidence is limited and what would raise it.
Relationship to Open Questions
Low confidence sections should generate corresponding open questions:
Low confidence + conflict = High Priority open question
Low confidence + gap = Medium Priority open question
Medium confidence + minor inconsistency = Low Priority open question
Every open question includes a recommendation that, if confirmed, would raise the section's confidence score.
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
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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/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
AutoGineer is not affiliated with or endorsed by Tribe AI or Anthropic.
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