Use to migrate a Genudo pipeline built with an old instructions approach — legacy persona, global instructions, stage instructions, or stage structure — to the current authoring approach. Takes the old artifacts (pasted, from files, or pulled live from the account) and rebuilds or updates the pipeline against today's…
Create a new Genudo pipeline in the account from the staged local spec — creating the pipeline, its stages, variables, and actions via the connector. Use when the user has approved a drafted pipeline and says build it, push it live, provision it, or create it. Confirms before the first write and reports the created…
Use to analyze a Genudo workspace — funnel health, opportunity distribution, conversation quality, AI cost and performance, and operations reporting. Read-focused; proposes actions but confirms before any change. Use proactively when the user asks how things are performing, for the numbers, or for a funnel or…
Wire an end-to-end automation into a Genudo stage — collect the required data, fire an action when it's ready, and tell the customer the outcome. Use when the user describes a rule like "when name, phone, and course are collected in the closing stage, register the lead to Google Sheets and tell them someone will reach…
Produce an operations report for a Genudo workspace — account totals, AI cost and response time, and messaging volume by provider and time window. Use when the user asks for the numbers, a weekly or monthly report, AI spend, cost per deal, or overall performance across pipelines. Read-only.
Genudo — the enterprise operating platform for autonomous AI Employees: Aaref (Sales), Adnan (Customer Support), ROZ (Quality Control & Operations), and Sara (WhatsApp Campaign Sender) across WhatsApp, Instagram, Messenger, and Web. Smart onboarding with connected tool detection (Meta Ads, Google Sheets, Lovable, Zoho.
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Build & manage Genudo AI agents for any messaging channel — pipelines, integrations, knowledge. Runs locally from the genudo-mcp-client npm package. Needs 3 environment variables to run.
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Create and deploy a 100% live, interactive web dashboard for GenuDo CRM and AI pipelines with zero Lovable credit consumption. Uses Lovable strictly as a free hosting platform via GitHub sync. Adheres strictly to the GenuDo Design System (Inter, JetBrains Mono.
Comprehensive reference for all public GenuDo REST API endpoints and remote Streamable HTTP MCP tools. Use when building custom dashboards, integrations, webhooks, or when looking up endpoint URLs, HTTP methods, headers, schemas, and payload examples.
Canonical visual styling, color tokens, typography rules, component geometry, and dashboard hierarchy for GenuDo web dashboards, UI widgets, and mobile surfaces (based on the GenuDo Handover Specification). Use whenever designing, generating, or restyling any GenuDo dashboard, frontend component, or report.
The essential first-step onboarding experience for GenuDo. Welcomes new and returning users, audits their active GenuDo workspace (deals, messages, pipelines), auto-detects other active tools and plugins in their environment (Meta Ads, Google Sheets, Lovable, HubSpot, Slack, Zoho Desk, Zapier), introduces the 4 named…
Inspect, create, edit, and troubleshoot GenuDo automation pipelines, stages, and agent action tool bindings. Trigger whenever the user asks about GenuDo pipelines, journeys, or stage workflows.
Design, schedule, and update per-stage follow-up sequences for re-engaging silent leads in GenuDo pipelines. Trigger when configuring lead nurture intervals, follow-up messages, or stage expiration actions.
Grade a Genudo agent's replies against business facts and rubrics — deterministic checks first, then you act as the LLM judge to confirm real failures and clear false alarms (dialect spellings, polite negations, equivalent numbers). Works on a test run's results.json or on real conversations. Use when the user asks…
Author a multi-turn synthetic-customer test suite for a Genudo agent — realistic 4–12 step journeys with fact, action, and stage assertions, global guardrail rules, and the business facts the judge grades against — as JSON for the Genudo testing engine. Use when the user wants test cases, a test suite, regression…
Mine a Genudo pipeline's real customer messages to find what buyers actually ask, where the agent fails them, and which facts are missing from the knowledge base. Use when onboarding or auditing an agent, when the user asks what customers ask most, wants buyer intents or archetypes, wants knowledge-base gaps found…
Turn agent test failures, chat-mining gaps, and requested agent changes into a prioritized 2-week sprint backlog in Excel — user stories, Fibonacci points, acceptance criteria tied to test cases, P0 blockers first. Use when the user wants a sprint plan, backlog, roadmap, or status sheet for improving a Genudo agent…
Turn failed test steps or mined conversation defects into the smallest fix that removes them — a knowledge row, a stage instruction, an enter condition, or a global rule — then prove it with a targeted re-run and a P0 regression. Use after run-agent-tests or evaluate-agent-replies reports failures, or after…
Run a synthetic-customer test suite against a live Genudo agent with the Genudo testing engine — isolated test leads, multi-turn messages, wait-for-reply polling, action and stage tracking, automatic archive-and-pause cleanup — and summarize the results. Use when the user wants to test an agent, run the suite, run…
Install and verify the Genudo agent testing engine on the user's machine (macOS, Linux, or Windows) — Python, the engine repo, its virtual environment, the API token, pipeline mapping, the Archived cleanup stage, and the subscription CLI used as LLM judge. Use before the first automated test run, when run-agent-tests…
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originalMIT
At most 3 mods per repository are shown here, and a mod shipped inside a plugin is left to that plugin's page — the rest are on their repository pages: