Update to Rippit

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        "Upload and export CSVs"
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      "long_description": "Rippit gives ChatGPT a data platform to work on. Connect business tools like Gong, Salesforce, Zendesk, Intercom, Snowflake and more, upload CSVs and files, or pull in targeted web content, then ask ChatGPT questions that span all of it.\n\nRippit reads every row, not the few records that fit in a chat. The heavy analysis runs in Rippit, so large jobs stay fast and affordable. It turns unstructured text like tickets, calls and documents into structured tables, queries millions of records in seconds, and saves results so you can pick up where you left off.\n\nSales, marketing, product and CX teams use Rippit with ChatGPT to find an edge and keep it: catch risk early, identify product bugs impacting key customers, influence roadmap decisions, track competitor moves and understand what customers are saying in their own words.",
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        "description": "Build an SEO- and AEO-optimized content program grounded in a company's own conversation data (support chats, sales calls, etc.) via the Rippit MCP. Adopts the framing \"I am a marketing leader looking to optimize our SEO and AEO,\" asks which topics/keywords to win, discovers which Rippit sources best support that angle, mines them liberally with worksheets + enrichment for real customer and employee language, scans the user's own company website for coverage and gaps, and produces a content strategy plus multiple drafted pieces (pillar + cluster) written into whatever docs connector the user has (Google Drive, Notion, etc.). Use whenever the user wants SEO/AEO/GEO content, a content strategy, blog/article/landing-page copy, a content cluster or pillar page, or marketing content informed by what customers actually say — even if they don't name SEO. Trigger on \"optimize our SEO,\" \"content strategy,\" \"rank for,\" \"write an article about,\" \"AEO,\" or \"get cited by AI.\" Runs as a multi-sub-agent pipeline.",
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        "description": "Analyze support conversations to find where an AI chatbot underperforms and human agents excel, turn those gaps into a prioritized knowledge-base build list, and (when a help center is connected) reconcile the gaps against existing documentation. Works with any helpdesk, any chatbot, and any help center. Use when the user asks to: analyze chatbot / bot gaps, compare bot vs agent (human) performance, run a deflection analysis, understand why the bot escalates or hands off, decide what the bot should be able to handle, find knowledge-base or documentation gaps, or improve / train the chatbot.",
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          "short_description": "Analyze support conversations to find where an AI chatbot underperforms and human agents excel, turn those gaps into a prioritized knowledge-base build list, and (when a help center is connected) reconcile the gaps against existing documentation. Works with any helpdesk, any chatbot, and any help center. Use when the user asks to: analyze chatbot / bot gaps, compare bot vs agent (human) performance, run a deflection analysis, understand why the bot escalates or hands off, decide what the bot should be able to handle, find knowledge-base or documentation gaps, or improve / train the chatbot."
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        "description": "Go progressively DEEPER on a specific customer question with the Rippit MCP by repeatedly narrowing into a SUBSET of the SAME tickets. Enrich a worksheet, filter to the interesting subset, build a child worksheet over just those rows, and enrich it more richly; repeat. The population only NARROWS each step (vs. rippit-iterative-learning, which moves to different tickets). Use to drill into one already-identified segment. Only use when explicitly invoked (e.g. via its slash command); do not auto-trigger from topic detection.",
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        "description": "Detect brewing escalations in a Rippit customer's support tickets — conversations at risk of turning into legal action, a public/social-media blow-up, or an exec/board complaint — and deliver the top examples as a ready-to-post Slack update. Use this whenever the user asks to find, surface, monitor, or get ahead of escalations, \"brewing\" or \"at-risk\" or \"about-to-blow-up\" tickets, churn threats, legal/PR/exec risk in support, or wants a recurring escalation digest posted to Slack. Trigger even if the user only says something like \"what support issues are about to blow up on us?\" or \"set up an escalation watch for a specific account\". This runs against the Rippit MCP toolchain (list_data_sources, describe_table, create_worksheet, enrich_worksheet, get_enrich_status, then aggregate/read), so prefer it over ad-hoc querying for any escalation-detection request.",
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