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- 🎨 Brand Consistency Linter
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🎨 Brand Consistency Linter
It eliminates the "drift" that happens when taglines are updated or social links change, but old documentation isn't refreshed.
$12
🎨 Brand Consistency Linter
Example session with this skill installed
Audit my project's README.md and footer.txt files against our brand spec to ensure consistency.
- Read your context and instructions
- Compiled the brand consistency linter
Brand Audit Results
Scope: README.md, footer.txt
Findings:
- [High] Rule
v1-tagline: Inconsistent tagline inREADME.md:12. Found "Fastest AI tool", expected "The World's Pro-Grade AI Orchestrator". - [Med] Rule
link-check: Outdated Twitter URL infooter.txt:2.
Remediation: Update tagline using snippetSNIP-04.
Connects securely to your tools. The creator never sees your data.
About this skill
Automated Brand Compliance for Developers
The Brand Consistency Linter is a specialized audit tool designed to ensure your project's public-facing surfaces—like GitHub bios, website footers, and profile READMEs—perfectly align with your official brand guidelines. It eliminates the "drift" that happens when taglines are updated or social links change, but old documentation isn't refreshed.
What it does
- Scans local Markdown and text files against a centralized
brand-spec.json. - Identifies inconsistent taglines, outdated brand names, and broken link patterns.
- Detects forgotten "INSERT LINK HERE" or "BRAND_NAME" placeholders.
- Provides structured findings including severity levels, line numbers, and rule IDs.
Why use this skill?
Unlike a general LLM prompt which might hallucinate or miss subtle inconsistencies, this skill uses a deterministic Python-based scanner backed by a formal specification. It acts as a linting layer for your brand identity, ensuring 100% adherence to your source of truth without the risk of an AI "improving" your copy in unauthorized ways. It provides actionable remediation snippets instead of just vague advice.
Output details
You receive a comprehensive audit report detailing the inspected scope, the specific rules applied, a categorized list of violations, and recommended copy fixes based on your approved remediation library.
How to use
- 1
Connect your agent to the Agensi MCP
Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup
- 2
Sign in to Agensi
Your agent shows a sign-in link. Sign in and click Allow.
- 3
Ask for what you need
Your agent loads this skill and uses it on your task. It always asks before any payment.
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