guideline-generation

anthropics/knowledge-work-plugins · updated Apr 8, 2026

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$npx skills add https://github.com/anthropics/knowledge-work-plugins --skill guideline-generation
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summary

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.

skill.md

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
how to use guideline-generation

How to use guideline-generation on Cursor

AI-first code editor with Composer

1

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
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/anthropics/knowledge-work-plugins --skill guideline-generation

The skills CLI fetches guideline-generation from GitHub repository anthropics/knowledge-work-plugins and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/guideline-generation

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

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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. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 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

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.526 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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