By larkcowork
Brand Voice transforms scattered brand materials into enforceable AI guardrails — automatically. It searches across Lark Wiki, Lark Drive, Lark Wiki, Gong, Lark IM, 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 — Lark Wiki, Lark Wiki, Lark 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 Lark Wiki and Lark Wiki 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 Lark IM 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 the org's Lark collaboration layer (Lark Wiki, Lark Drive, Lark IM) plus specialty connectors (Figma design tokens, Gong and Granola call transcripts). 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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Biến Claude Cowork (trong Claude Desktop) thành một chuyên gia làm việc thuần Lark cho mọi
vai trò — chat, mail, lịch, tài liệu/wiki, công việc, Base, drive, biên bản họp — tất cả đều chạy
qua cầu nối MCP lark-cli.
📘 Bạn là người dùng nghiệp vụ (không chuyên kỹ thuật)? Bắt đầu tại đây →
docs/— bộ tài liệu tiếng Việt đầy đủ: giới thiệu, hướng dẫn bắt đầu, danh mục theo phòng ban, giới thiệu chi tiết tất cả plugin (trang 8), cách vận hành (trang 9), cách tùy biến/custom (trang 10), best practice — nên dùng & custom sao (trang 11), kịch bản thực tế, an toàn dữ liệu, FAQ và thuật ngữ.
Tùy biến từ bộ knowledge-work-plugins mã nguồn mở của Anthropic. Bộ gốc nhắm tới Slack / Notion / Google / Jira. Bản fork này đấu lại toàn bộ lớp cộng tác sang Lark (larksuite.com), trong khi vẫn giữ các công cụ chuyên ngành thực sự (kho dữ liệu, CRM, thanh toán, cơ sở dữ liệu khoa học) dưới dạng connector ngoài tùy chọn.
Lark là một super-app: Chat (IM), Mail, Lịch, Tài liệu (Docs), Wiki, Base (bảng dữ liệu thông minh), Sheets, Drive, Minutes (biên bản họp AI), họp video, Phê duyệt, OKR… gói gọn trong một nền tảng. Vì mọi mặt công việc đã nằm trong Lark, một trợ lý AI đặt ngay tại đây có thể chạm tới toàn bộ vòng đời công việc — điều mà việc ghép nối rời rạc Slack + Notion + Jira + Gmail không làm được.
Bộ plugin này dạy Claude nói "tiếng Lark": bạn ra lệnh bằng tiếng Việt như nói với đồng nghiệp, Claude tự thao tác trên dữ liệu Lark của bạn rồi trả kết quả về — ngay tại chỗ, không cần rời Lark, không cần biết code.
Mỗi plugin chỉ gồm markdown + JSON. Các kỹ năng (skill) đều không phụ thuộc công cụ cụ thể —
chúng tham chiếu tới các "ô giữ chỗ" dạng ~~category, được phân giải qua file CONNECTORS.md của
từng plugin. So với bản gốc, mỗi plugin chỉ thay đổi đúng hai thứ:
.mcp.json — các server giao tiếp chung (Slack, Gmail, Google Calendar, Notion, Asana,
Linear, Atlassian, Guru, Fireflies, Box…) được thay bằng một server MCP lark duy nhất
(lark-cli mcp serve). Các server chuyên ngành được giữ nguyên.CONNECTORS.md — mỗi nhóm danh mục chung nay được ánh xạ tới các công cụ lark_* cụ thể;
các danh mục chuyên ngành vẫn giữ kết nối ngoài.Một server lark duy nhất hỗ trợ mọi danh mục cộng tác. Bất kỳ thao tác nào không có công cụ chuyên
biệt sẽ rơi xuống lark_api (cửa thoát hiểm tới Lark OpenAPI). Xem bản ánh xạ tổng tại
connectors/CONNECTORS.lark.md.
Sơ đồ luồng:
Bạn ra lệnh Trợ lý AI Thao tác trên Lark Trả kết quả
trong Lark → dùng kỹ năng → (mail, lịch, Base, → về Lark
(tiếng Việt) chuyên Lark tài liệu, duyệt…) (thẻ, tin nhắn, file)
Write feature specs, plan roadmaps, and synthesize user research faster. Keep stakeholders updated and stay ahead of the competitive landscape.
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.
Deploy a production Lark Base end-to-end for your team — an 8-phase orchestrator (discovery → design → build → wire-up → import → dashboards → automation → verified handover) that fans out parallel sub-agents at build and viz.
Search across all of your company's tools in one place. Find anything across email, chat, documents, and wikis without switching between apps.
Connect to preclinical research tools and databases (literature search, genomics analysis, target prioritization) to accelerate early-stage life sciences R&D.
npx claudepluginhub larkcowork/lark-cowork-plugins --plugin brand-voiceUpstash Context7 MCP server for up-to-date documentation lookup. Pull version-specific documentation and code examples directly from source repositories into your LLM context.
Make your AI agent code with your project's architecture, rules, and decisions.
AI-powered wiki generator for code repositories. Generates comprehensive, Mermaid-rich documentation with dark-mode VitePress sites, onboarding guides, deep research, and source citations. Inspired by OpenDeepWiki and deepwiki-open.
Claude + Obsidian knowledge companion. Sets up a persistent, compounding wiki vault (Karpathy's LLM Wiki pattern). v1.7 "Compound Vault" + v1.8 methodology modes close 5 of 5 priority gaps from the May 2026 compass artifact. Ships: substrate alignment with kepano/obsidian-skills, default Obsidian CLI transport, hybrid retrieval (contextual prefix + BM25 + cosine rerank per Anthropic's Sept 2024 research), per-file advisory locking for multi-writer safety, pre-commit verifier agent, AND methodology modes (LYT / PARA / Zettelkasten / Generic) for first-class organizational support no other Claude+Obsidian competitor offers. v1.7.x audit closure: every BLOCKER + HIGH + MEDIUM + LOW finding from the v1.7.0 audit is CLOSED or DEFERRED-with-rationale. Optional DragonScale Memory extension (log folds, deterministic addresses, semantic tiling lint, boundary-first autoresearch).
Complete AI coding workflow system. Self-correcting memory + persistent FTS5-indexed research wikis + auto-research loop + multi-LLM council on a single SQLite store. 33 skills, 8 agents, 22 commands, 37 hook scripts across 24 events. Cross-agent via SkillKit.
Comprehensive C4 architecture documentation workflow with bottom-up code analysis, component synthesis, container mapping, and context diagram generation