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lobu-ai

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• 8 skills
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• 19 hours ago last updated

1. ▌
   Lobu · lobu-ai

   Set up new Lobu agents end to end and operate existing Lobu projects and memory: interview, scaffold, validate, authenticate, connect feeds, execute operations, and test Automations.
   0 installs
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   Lobu Operator · lobu-ai

   Lobu Operator — Repo Guide
   0 installs
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   Lobu 2 · lobu-ai

   Use Lobu MCP for shared, permission-aware company context, durable organizational memory, and governed actions. Trigger when the user asks what the organization knows, needs context from connected company systems, wants to preserve a durable fact or decision, or needs to discover and use Lobu SDK capabilities.
   0 installs
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   Lobu Builder · lobu-ai bundle

   Use when working inside a Lobu project generated by @lobu/cli or any repository centered on lobu.config.ts, AGENTS.md, agent prompt files, local skills, and evals. This skill helps a coding agent inspect the right files, make Lobu-native changes, keep the stack runnable, and validate semantics with chat tests and evals.
   0 installs
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   Crm Ops · lobu-ai

   How to operate the Lobu funnel CRM — create and enrich leads, log interactions, advance funnel stages, open and update pilots, and produce the weekly digest. Use whenever the task touches the pipeline.
   0 installs
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   Account Brief · lobu-ai

   Build a pre-renewal brief for a tracked account from its recent public news and announcements. Use before a renewal call or QBR, after the account-health Automation flags a risk. Reading public news is allowed; do not log in, submit forms, or touch any CRM write endpoint.
   0 installs
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   Deliveroo Order · lobu-ai

   Turn collected lunch orders into a clean per-person order list for a human to place on Deliveroo. Use in step 2 of the lunch run, after orders are collected. The live menu is fetched automatically by the lobu-team-lunch-finalize reaction (via the Owletto Chrome extension) — this skill never places an order or touches payment.
   0 installs
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   Company Agent Infrastructure · lobu-ai

   Use when designing or building internal/company AI agents that need shared context across company systems, durable organizational memory across sessions or agents, user-scoped permissions, approvals, audit, or governed actions. Also use when deciding whether ordinary MCP tools, RAG, or local agent memory are enough versus a shared company context and control layer.
   0 installs