Brand Voice transforms scattered brand materials into enforceable AI guardrails — automatically. It searches across Notion, Google Drive, Confluence, Gong, Slack, and meeting transcripts to distill your strongest brand signals into a single source of truth, then applies them to every piece of AI-generated content. The more your team creates with Claude, the more consistent your brand becomes.
Search connected platforms for brand materials and produce a discovery report
Apply brand guidelines to content creation
Generate brand voice guidelines from documents, transcripts, discovery reports, or any combination
Generates brand-aligned sales and marketing content by applying brand guidelines to specific content requests. Use this agent for long-form content, batch generation, or when multiple brand constraints must be balanced simultaneously. <example> Context: The brand-voice-enforcement skill needs to generate a detailed enterprise proposal. It delegates to the content-generation agent for long-form, multi-constraint content creation. user: "Write a 5-page proposal for our AI platform at a Fortune 500" assistant: "I'll generate a brand-aligned proposal applying all guidelines..." <commentary> Long-form content requiring simultaneous application of multiple brand constraints. The content-generation agent handles complex generation with thorough validation. </commentary> </example> <example> Context: User needs a batch of personalized outreach emails for different personas. user: "Create 5 cold emails for different buyer personas using our brand voice" assistant: "I'll generate brand-aligned emails tailored to each persona..." <commentary> Batch content generation requiring brand consistency across multiple variations. The content-generation agent balances brand constraints with persona-specific adaptation. </commentary> </example>
Analyzes sales call transcripts to extract brand voice patterns, messaging effectiveness, and tone variations. Use this agent when processing multiple transcripts or performing deep pattern recognition across conversations. <example> Context: The guideline-generation skill has 10 sales call transcripts to analyze. user: "Generate brand guidelines from my last 10 sales calls" assistant: "I'll analyze the transcripts for voice patterns and messaging..." <commentary> Multiple transcripts need deep pattern recognition across conversations. The conversation-analysis agent handles this heavy analysis. </commentary> </example> <example> Context: Gong transcripts were found during brand discovery and need analysis. user: "Analyze the Gong calls found during discovery" assistant: "I'll pull the transcripts from Gong and analyze voice patterns..." <commentary> Discovery identified relevant Gong recordings. The conversation-analysis agent fetches transcripts via MCP and performs deep pattern analysis. </commentary> </example>
Autonomously searches enterprise platforms to discover brand-related documents, transcripts, and design assets. Use when the user wants to build brand guidelines but doesn't know where materials are, or wants a comprehensive brand content audit. <example> Context: User wants to create brand guidelines but doesn't know what materials exist. user: "I need brand guidelines but our stuff is scattered everywhere — Notion, Confluence, Google Drive, Box..." assistant: "I'll search across your connected platforms to find all brand-related materials." <commentary> User has scattered brand materials across multiple platforms. The discover-brand agent autonomously searches all connected MCP platforms to find and triage brand content. </commentary> </example> <example> Context: User wants a brand content audit before generating guidelines. user: "What brand materials do we actually have? Can you find everything?" assistant: "I'll run a comprehensive brand discovery across your connected platforms." <commentary> User wants to understand what brand materials exist. The discover-brand agent searches, categorizes, ranks, and reports on all discovered brand content. </commentary> </example> <example> Context: The discover-brand skill delegates deep platform search to this agent. user: "Discover our brand voice" assistant: "I'll search your connected platforms for brand materials..." <commentary> The discover-brand skill orchestrates this agent for the heavy search and triage work. </commentary> </example>
Analyzes brand documents to extract voice attributes, messaging, terminology, and examples. Use this agent when processing multiple brand documents or performing cross-document pattern recognition. <example> Context: The guideline-generation skill has received 5 brand documents to process. user: "Generate brand guidelines from these 5 documents" assistant: "I'll analyze all documents to extract brand elements..." <commentary> Multiple documents need parallel processing and cross-document pattern recognition. The document-analysis agent handles heavy parsing efficiently. </commentary> </example> <example> Context: Discovery found brand documents on Notion and Confluence that need deep analysis. user: "Analyze the brand materials found during discovery" assistant: "I'll do a deep analysis of each discovered document..." <commentary> Discovery report identified key documents. The document-analysis agent fetches full content from connected platforms and extracts structured brand elements. </commentary> </example>
Validates content and brand guidelines against brand standards. Use this agent to check compliance, consistency, completeness, and open question coverage before finalizing output. <example> Context: The brand-voice-enforcement skill has generated a cold email and wants to validate it against guidelines before presenting to the user. user: "Check this email against our brand guidelines" assistant: "Let me validate this against your brand guidelines..." <commentary> Content needs validation against brand standards before delivery. The quality-assurance agent performs a fast, structured compliance check. </commentary> </example> <example> Context: Brand guidelines were just generated and need validation before presenting. user: "Validate these brand guidelines for completeness and quality" assistant: "Let me check the guidelines for completeness, consistency, and open questions..." <commentary> Generated guidelines need quality validation before presenting to the user. The quality-assurance agent checks completeness, open questions coverage, and PII. </commentary> </example>
This skill applies brand guidelines to content creation. It should be used when the user asks to "write an email", "draft a proposal", "create a pitch deck", "write a LinkedIn post", "draft a presentation", "write a Slack message", "draft sales content", or any content creation request where brand voice should be applied. Also triggers on "on-brand", "brand voice", "enforce voice", "apply brand guidelines", "brand-aligned content", "write in our voice", "use our brand tone", "make this sound like us", "rewrite this in our tone", or "this doesn't sound on-brand". Not for generating guidelines from scratch (use guideline-generation) or discovering brand materials (use discover-brand).
This skill orchestrates autonomous discovery of brand materials across enterprise platforms (Notion, Confluence, Google Drive, Box, SharePoint, Figma, Gong, Granola, Slack). It should be used when the user asks to "discover brand materials", "find brand documents", "search for brand guidelines", "audit brand content", "what brand materials do we have", "find our style guide", "where are our brand docs", "do we have a style guide", "discover brand voice", "brand content audit", or "find brand assets".
This skill generates, creates, or builds brand voice guidelines from source materials. It should be used when the user asks to "generate brand guidelines", "create a style guide", "extract brand voice", "create guidelines from calls", "consolidate brand materials", "analyze my sales calls for brand voice", "build a brand playbook from documents", "synthesize a voice and tone guide", or uploads brand documents, transcripts, or meeting recordings for brand analysis. Also triggers when the user has a discovery report and wants to convert it into actionable guidelines.
External network access
Connects to servers outside your machine
Uses power tools
Uses Bash, Write, or Edit tools
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AI specialists for every role at your company.
Sales preps calls. Engineers ship PRs. Finance closes the month. Marketing ships campaigns. Each role gets Claude tuned to their work and connected to their real tools (HubSpot, Slack, Snowflake, Notion, Linear).
pace.tools · Cookbook · Docs · Catalog
Pace is a curated bundle of AI agents you install into Cowork (the Claude desktop app) or Claude Code (the CLI). One agent per role, each one fluent in that role's vocabulary and connected to the SaaS tools the team already uses.
/pace router for the commands that are specific to your
company. /pace teach captures how your team actually works once;
everything else builds on it.For front-end code-level design, Pace defers to impeccable; the installer offers to bring it.
Open Cowork → Plugins → Add marketplace → paste:
GoldenBerry-SO/Pace
Then install whichever plugin matches your role: sales, marketing,
engineering, data, finance, ...
claude plugin marketplace add GoldenBerry-SO/Pace
claude plugin install sales@pace
Or use the guided picker:
npx pace-tools install
Just describe what you need. The skills auto-trigger from natural language; you don't need to remember commands.
Prep me for the Acme call tomorrow.
> sales / call-prep fires, pulls HubSpot history + recent news, drafts the brief.
Write a SQL query for monthly active users by plan tier, last 6 months.
> data / write-query takes over, drafts the query, runs it against Snowflake if connected.
Review this PR. Look for real bugs first.
> engineering / code-review takes the PR, reads the diff, posts comments.
For your team's own commands, use the /pace router:
/pace teach # one-time: capture how your company works
/pace standup # then: anything you've authored as a /pace:* command
The engineering plugin includes 34 engineering skills. Beyond the obvious
review/test/diagnose ones, there's a disciplined stacked-PR
workflow: spec → implement → review → topr → next. See
the engineering cookbook
for the deep-dive.
pace.tools/
├── skill/ ← the /pace router (your team's commands)
├── plugins/ ← the 50-plugin catalog
│ ├── sales/ ← Anthropic-authored, imported verbatim
│ ├── engineering/ ← Anthropic + PiXeL16/skills + authored
│ ├── ...
│ └── partner-built/ ← Apollo, Common Room, Slack, etc.
├── .claude-plugin/
│ └── marketplace.json ← registers all 50 plugins
├── site/ ← pace.tools (Astro)
├── cli/ ← npx pace-tools CLI
└── NOTICE.md ← full attribution
Pace is open source, fork it, rename the router, write commands specific to your operation. We also help companies adopt Pace end-to-end: install + connector setup, command authoring tuned to your team's workflows, training, and ongoing enablement.
Talk to GoldenBerry · About GoldenBerry
See NOTICE.md for full attribution. Apache 2.0 throughout.
Company-wide skills for AI coding agents. One router (/pace), many commands across engineering, product, ops, marketing. Built on the same install tech as impeccable.
Search across all of your company's tools in one place. Find anything across email, chat, documents, and wikis without switching between apps.
Manage tasks, plan your day, and build up memory of important context about your work. Syncs with your calendar, email, and chat to keep everything organized and on track.
Create, customize, and manage plugins tailored to your organization's tools and workflows. Configure MCP servers, adjust plugin behavior, and adapt templates to match how your team works.
Company-wide skills for AI coding agents. One router (/pace), many commands across engineering, product, ops, marketing. Built on the same install tech as impeccable.
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Standalone image generation plugin using Nano Banana MCP server. Generates and edits images, icons, diagrams, patterns, and visual assets via Gemini image models. No Gemini CLI dependency required.
Multi-model consensus engine integrating OpenAI Codex CLI, Gemini CLI, and Claude CLI for collaborative code review and problem-solving.
Write feature specs, plan roadmaps, and synthesize user research faster. Keep stakeholders updated and stay ahead of the competitive landscape.
Ultra-compressed communication mode. Cuts ~75% of tokens while keeping full technical accuracy by speaking like a caveman.
Memory compression system for Claude Code - persist context across sessions