guideline-generation▌
anthropics/knowledge-work-plugins · updated Apr 8, 2026
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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.
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-brandrun - Check
.claude/brand-voice.local.mdfor 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.
- 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 folder - Confirm 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-voice - 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 guidancereferences/confidence-scoring.md— Confidence scoring methodology, thresholds, and examples
How to use guideline-generation on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add guideline-generation
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches guideline-generation from GitHub repository anthropics/knowledge-work-plugins and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate guideline-generation. Access the skill through slash commands (e.g., /guideline-generation) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ Use When
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.5★★★★★26 reviews- ★★★★★Chaitanya Patil· Dec 28, 2024
guideline-generation is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Isabella Garcia· Dec 16, 2024
guideline-generation has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Nikhil Liu· Dec 8, 2024
guideline-generation fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Sakura Agarwal· Nov 27, 2024
guideline-generation is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Piyush G· Nov 19, 2024
guideline-generation fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Valentina Bhatia· Nov 7, 2024
Solid pick for teams standardizing on skills: guideline-generation is focused, and the summary matches what you get after install.
- ★★★★★Luis Sharma· Oct 26, 2024
guideline-generation is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Michael Mehta· Oct 18, 2024
Solid pick for teams standardizing on skills: guideline-generation is focused, and the summary matches what you get after install.
- ★★★★★Shikha Mishra· Oct 10, 2024
guideline-generation has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Hiroshi Park· Sep 25, 2024
I recommend guideline-generation for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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