Apply existing brand guidelines to all sales and marketing content generation. Load the user's brand guidelines, apply voice constants and tone flexes to the content request, validate output, and explain brand choices.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionbrand-voice-enforcementExecute the skills CLI command in your project's root directory to begin installation:
Fetches brand-voice-enforcement from anthropics/knowledge-work-plugins and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate brand-voice-enforcement. Access via /brand-voice-enforcement in your agent's command palette.
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.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Apply existing brand guidelines to all sales and marketing content generation. Load the user's brand guidelines, apply voice constants and tone flexes to the content request, validate output, and explain brand choices.
Find the user's brand guidelines using this sequence. Stop as soon as you find them:
Session context — Check if brand guidelines were generated earlier in this session (via /brand-voice:generate-guidelines). If so, they are already in the conversation. Use them directly. Session-generated guidelines are the freshest and reflect the user's most recent intent.
Local guidelines file — Check for .claude/brand-voice-guidelines.md inside the user's working folder. Do NOT use a relative path from the agent's current working directory — in Cowork, the agent runs from a plugin cache directory, not the user's project. Resolve the path relative to the user's working folder. If no working folder is set, skip this step.
Ask the user — If none of the above found guidelines, tell the user: "I couldn't find your brand guidelines. You can:
/brand-voice:discover-brand to find brand materials across your platforms/brand-voice:generate-guidelines to create guidelines from documents or transcriptsWait for the user to provide guidelines before proceeding.
Also read .claude/brand-voice.local.md for enforcement settings (even if guidelines came from another source):
strictness: strict | balanced | flexiblealways-explain: whether to always explain brand choicesBefore writing, identify:
Voice is the brand's personality — it stays constant across all content:
Refer to references/voice-constant-tone-flexes.md for the "voice constant, tone flexes" model.
Tone adapts by content type and audience. Use the tone-by-context matrix from guidelines to set:
Create content that:
For complex or long-form content, delegate to the content-generation agent (defined in agents/content-generation.md).
For high-stakes content, delegate to the quality-assurance agent (defined in agents/quality-assurance.md) for validation.
After generating content:
When always-explain is true in settings, include brand application notes with every response.
When the user's request conflicts with brand guidelines:
Default to adapting guidelines with an explanation of the tradeoff.
Open questions are unresolved brand positioning decisions flagged during guideline generation, stored in the guidelines under an "Open Questions" section. When generating content, check if the brand guidelines contain open questions:
references/voice-constant-tone-flexes.md — The "voice constant, tone flexes" mental model, "We Are / We Are Not" table structure, and tone-by-context matrix explanationreferences/before-after-examples.md — Before/after content examples per content type showing enforcement in practiceMake data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
brand-voice-enforcement fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
brand-voice-enforcement is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
I recommend brand-voice-enforcement for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Useful defaults in brand-voice-enforcement — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
brand-voice-enforcement is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
brand-voice-enforcement fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Solid pick for teams standardizing on skills: brand-voice-enforcement is focused, and the summary matches what you get after install.
brand-voice-enforcement has been reliable in day-to-day use. Documentation quality is above average for community skills.
Registry listing for brand-voice-enforcement matched our evaluation — installs cleanly and behaves as described in the markdown.
brand-voice-enforcement reduced setup friction for our internal harness; good balance of opinion and flexibility.
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