{"id":12133,"external_id":"plugin_asdk_app_6a860a7e55888191a2186b19e03fe4b1","name":"app-6a860a7e55888191a2186b19e03fe4b1","display_name":"Rippit","developer":"Rippit","category":"Business & Operations","listing_language":"en","listing_language_details":{"method":"cld-0.13.0/listing-v1","reliable":true,"detected_at":"2026-11-11T00:06:12Z","input_sha256":"919655429ebfb272f045d1d57afb0a934532adc248a9de2de937379dbc0b7e92","source_fields":["release.description","release.interface.short_description","release.interface.long_description"]},"version":"1.0.1","skill_count":7,"first_seen_at":"2026-11-10T15:12:07.056Z","last_seen_at":"2026-11-14T06:00:03.021Z","last_changed_at":"2026-11-11T00:08:58.691Z","install_count":null,"search_matches":[],"research_summary":null,"research_reviewed_at":null,"metadata":{"canonical_app_id":"asdk_app_6a860a7e55888191a2186b19e03fe4b1","connector_id":"asdk_app_6a860a7e55888191a2186b19e03fe4b1","created_at":"2026-08-19T19:56:46.577633Z","discoverability":"LISTED","id":"plugin_asdk_app_6a860a7e55888191a2186b19e03fe4b1","is_template":false,"is_template_instance":false,"name":"app-6a860a7e55888191a2186b19e03fe4b1","release":{"app_ids":["asdk_app_6a860a7e55888191a2186b19e03fe4b1"],"app_manifest":{"apps":{"rippit":{"id":"asdk_app_6a860a7e55888191a2186b19e03fe4b1","required":true}}},"app_templates":[],"description":"Load, store and run bulk AI analysis on large datasets, with every row read.","display_name":"Rippit","id":"pluginrel_de614bfa98048191b22fde5f6499f702","interface":{"brand_color":"#046244","capabilities":["Analyze customer conversations","Classify conversations with AI","Aggregate and pivot results","Find verbatim quotes","Upload and export CSVs"],"category":"Business & Operations","composer_icon_dark_url":null,"composer_icon_url":"https://files.openai.com/content?id=file_00000000054481f584680f96ef2da5fd","default_prompt":"What are customers complaining about most this month?","default_prompts":["What are customers complaining about most this month?","Tag every lost deal in this CSV by the reason given.","Which accounts mentioned a competitor recently?"],"developer_name":"Rippit","logo_url":"https://files.openai.com/content?id=file_0000000066ac82309aa91bc7b45cd8b2","logo_url_dark":null,"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.","plugin_category_id":"business & operations","privacy_policy_url":"https://www.rippit.com/privacy-policy","screenshot_urls":[],"short_description":"AI that reads all your data","terms_of_service_url":"https://www.rippit.com/terms-of-service","website_url":"https://www.rippit.com"},"keywords":["customer conversations","conversation analytics","support tickets","voice of customer","customer feedback"],"onboarding_skill_name":null,"requires_local_executor":false,"skills":[{"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.","interface":{"brand_color":null,"default_prompt":null,"display_name":"rippit-aeo-seo-optimization","icon_large_url":null,"icon_small_url":null,"iconography":"search","short_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."},"name":"rippit-aeo-seo-optimization","plugin_release_skill_id":"pluginrsk_6af3a0631d008191b4e958595a9325fe"},{"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.","interface":{"brand_color":null,"default_prompt":null,"display_name":"rippit-chatbot-analysis","icon_large_url":null,"icon_small_url":null,"iconography":"chart","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."},"name":"rippit-chatbot-analysis","plugin_release_skill_id":"pluginrsk_6af3a0631f008191aa2cff2da0b654ab"},{"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.","interface":{"brand_color":null,"default_prompt":null,"display_name":"rippit-double-click","icon_large_url":null,"icon_small_url":null,"iconography":"search","short_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."},"name":"rippit-double-click","plugin_release_skill_id":"pluginrsk_6af3a0631e848191845b55b6574553a0"},{"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.","interface":{"brand_color":null,"default_prompt":null,"display_name":"rippit-escalation-finder","icon_large_url":null,"icon_small_url":null,"iconography":"radar","short_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."},"name":"rippit-escalation-finder","plugin_release_skill_id":"pluginrsk_6af3a0632e408191af1425842972103e"},{"description":"Build and run a repeatable \"ticket-insights\" QA program that measures something meaningful on EVERY support ticket — contact reason, root cause, avoidability, resolution quality, product signal, and churn/at-risk — instead of just scoring agents. Use this whenever the user wants to redesign, rebuild, or stand up a QA or support-analytics program; shift QA from agent scorecards to ticket/insight focus; systematically tag, classify, or score every ticket; find contact drivers, avoidable volume, product feedback, or at-risk accounts from support conversations; or set up a recurring support-insights readout. Trigger even if the user doesn't say \"QA\" — e.g. \"what should we measure on every ticket\", \"turn our support tickets into insights\", \"find what's driving contacts\", \"which tickets put revenue at risk\". Works with ANY helpdesk or conversation data source and ANY set of connectors; do not assume a specific integration.","interface":{"brand_color":null,"default_prompt":null,"display_name":"rippit-qa-program-revamp","icon_large_url":null,"icon_small_url":null,"iconography":"chart","short_description":"Build and run a repeatable \"ticket-insights\" QA program that measures something meaningful on EVERY support ticket — contact reason, root cause, avoidability, resolution quality, product signal, and churn/at-risk — instead of just scoring agents. Use this whenever the user wants to redesign, rebuild, or stand up a QA or support-analytics program; shift QA from agent scorecards to ticket/insight focus; systematically tag, classify, or score every ticket; find contact drivers, avoidable volume, product feedback, or at-risk accounts from support conversations; or set up a recurring support-insights readout. Trigger even if the user doesn't say \"QA\" — e.g. \"what should we measure on every ticket\", \"turn our support tickets into insights\", \"find what's driving contacts\", \"which tickets put revenue at risk\". Works with ANY helpdesk or conversation data source and ANY set of connectors; do not assume a specific integration."},"name":"rippit-qa-program-revamp","plugin_release_skill_id":"pluginrsk_6af3a064106081918fd02201f878ee9b"},{"description":"Find the best \"above and beyond\" customer-support conversations — where an agent went the extra mile and the customer was visibly grateful — and turn them into a polished recognition message for Slack (or another chat channel). Use this whenever someone wants to celebrate, recognize, or shout out support agents; surface \"great support moments,\" \"wins,\" \"kudos,\" or \"appreciation\" stories from support tickets/conversations; build a team recognition post; or schedule a recurring support-wins digest. Trigger it even if the person only says something like \"find me some great support interactions to highlight\" or \"put together a kudos post for the support team\" — this skill defines the full find → rank → draft → test-in-DM → confirm-channel → (optionally) schedule workflow, and it never posts to a shared channel without explicit confirmation.","interface":{"brand_color":null,"default_prompt":null,"display_name":"rippit-support-shoutouts","icon_large_url":null,"icon_small_url":null,"iconography":"heart","short_description":"Find the best \"above and beyond\" customer-support conversations — where an agent went the extra mile and the customer was visibly grateful — and turn them into a polished recognition message for Slack (or another chat channel). Use this whenever someone wants to celebrate, recognize, or shout out support agents; surface \"great support moments,\" \"wins,\" \"kudos,\" or \"appreciation\" stories from support tickets/conversations; build a team recognition post; or schedule a recurring support-wins digest. Trigger it even if the person only says something like \"find me some great support interactions to highlight\" or \"put together a kudos post for the support team\" — this skill defines the full find → rank → draft → test-in-DM → confirm-channel → (optionally) schedule workflow, and it never posts to a shared channel without explicit confirmation."},"name":"rippit-support-shoutouts","plugin_release_skill_id":"pluginrsk_6af3a06439f48191bdb4bc5f4e96dbce"},{"description":"Run a controlled head-to-head (\"bake-off\") between two or more analysis tools (e.g. the Rippit MCP vs. a Snowflake/SQL MCP, or any data/connector MCP) on the SAME deep-dive question, using isolated Cowork sub-agents, then present each tool's output and the process behind it side by side in the chat so the user can judge for themselves. Use when the user wants to compare tools/MCPs, benchmark connectors, run a tool bake-off, see how different tools answer the same question, or A/B two analysis engines on real data. Triggers: \"compare X MCP and Y MCP\", \"bake-off\", \"which tool is better at\", \"run the same analysis with both tools\", \"head-to-head\", \"benchmark these connectors\".","interface":{"brand_color":null,"default_prompt":null,"display_name":"rippit-tool-bakeoff","icon_large_url":null,"icon_small_url":null,"iconography":"chart","short_description":"Run a controlled head-to-head (\"bake-off\") between two or more analysis tools (e.g. the Rippit MCP vs. a Snowflake/SQL MCP, or any data/connector MCP) on the SAME deep-dive question, using isolated Cowork sub-agents, then present each tool's output and the process behind it side by side in the chat so the user can judge for themselves. Use when the user wants to compare tools/MCPs, benchmark connectors, run a tool bake-off, see how different tools answer the same question, or A/B two analysis engines on real data. Triggers: \"compare X MCP and Y MCP\", \"bake-off\", \"which tool is better at\", \"run the same analysis with both tools\", \"head-to-head\", \"benchmark these connectors\"."},"name":"rippit-tool-bakeoff","plugin_release_skill_id":"pluginrsk_6af3a063fdac81919e22adfa03158a45"}],"version":"1.0.1"},"scope":"GLOBAL","status":"ENABLED"},"research":null,"package_metadata":{"name":"app-6a860a7e55888191a2186b19e03fe4b1","author":{"url":"https://www.rippit.com","name":"Rippit"},"sources":[{"path":".codex-plugin/plugin.json","sha256":"70cb12887196bb3e35a359ced525276c6bcd50d0cbcdcd933b9b3dd782e287d7"}],"version":"1.0.1","commerce":false,"homepage":"https://docs.rippit.com/mcp-server/overview","keywords":["customer conversations","conversation analytics","support tickets","voice of customer","customer feedback"],"artifact_id":228006,"observed_at":"2026-11-13T12:14:07Z","support_url":"https://docs.rippit.com/troubleshooting","capabilities":["Analyze customer conversations","Classify conversations with AI","Aggregate and pivot results","Find verbatim quotes","Upload and export CSVs"],"field_sources":{"name":0,"author":0,"version":0,"commerce":0,"homepage":0,"keywords":0,"support_url":0,"capabilities":0,"release_notes":0,"review_case_counts":0,"commerce_description":0},"release_notes":"Resubmission addressing all five findings from the September 8, 2026 review. Test 1: the review account now has conversation data covering the test window, and the prompt asks for the most recent 30 days of data so it does not depend on the review date. Tests 2 and 4: the churn analysis and support analysis worksheets are now in the review account. Test 3: conversations 10482 and 10491 now contain the billing scenario. Test 5: fixed the broken Rippit mascot image on result cards; each card resource now declares openai/widgetCSP with the image host in resource_domains. Package migrated to the plugin format.","extraction_version":1,"review_case_counts":{"negative":3,"positive":5},"conflicts_or_errors":[],"commerce_description":"Rippit does not sell products or process payments in ChatGPT."}}