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Analyze customer recommendation and satisfaction surveys, NPS and CSAT, from Google Sheets. Group comments into themes, explain weekly score changes and low-score drivers, and post a Slack digest with representative quotes without modifying survey data.
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Customer feedback analysis — sentiment detection, NPS/CSAT frameworks, feature request clustering, support ticket triage, churn signal detection, and feedback-to-roadmap translation
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Analyze CSAT, NPS comments, reviews, and support feedback for sentiment, recurring themes, customer pain, and service improvement actions. Use for customer support and CX feedback reviews.
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Send CSAT follow-up emails to customers after ticket resolution via [Gmail](https://composio.dev/toolkits/gmail)
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Personal App-in-Skill customer-support desk (help desk) that triages incoming support tickets from email, WhatsApp, web chat, contact forms, and WeChat, drafts KB-grounded replies, and proposes actions, then holds every send behind a human approval queue with a pre-send quality gate (support-qa) that outputs SHIP/FIX/BLOCK, plus SLA and CSAT tracking. Use when the user invokes $kelly-support or /kelly-support, mentions 客服 / 工单 / 支持台, a support desk, help desk, customer service, support tickets, ticket triage, a knowledge base or canned macros, SLA / first-response time, CSAT, escalations, or refund requests, wants incoming tickets triaged into a needs_review → approved → done pipeline, agent-drafted replies grounded in a knowledge base and reviewed before sending, refunds and commitments gated behind human approval, or breaching-SLA tickets surfaced. This is the OPERATOR desk (agent + human), distinct from a visitor-facing chat bubble.
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Turn interviews, surveys, usability notes, and customer feedback into shared themes, user groups, and prioritized recommendations. Findings include supporting quotes and participant counts, helping teams connect research evidence to concrete product improvements.
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Turn interviews, surveys, usability notes, and customer feedback into shared themes, user groups, and prioritized recommendations. Findings include supporting quotes and participant counts, helping teams connect research evidence to concrete product improvements.
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Builds the loop from what customers say to what gets changed — collecting feedback, distinguishing signal from noise, routing it to owners, and closing the loop back to the customer. Use this to set up a feedback program, design or interpret CSAT/NPS, decide what customer feedback deserves action, get product to act on recurring issues, or diagnose why feedback is collected but nothing changes.
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Weekdays, file work only, no browser at all. Reads every ledger, every queue file, and every run record since its cursor, turns the member's ticks into replied lines and closed cards, computes the first response and time to resolution clocks the Friday report is built from, folds the card inbox, re-renders the desk board, and writes the short morning brief the member opens first. It holds every outbound action unless you released the channel, and it never touches a credential.
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Monthly on the last weekday, light browser lane. Re-tests every theme and every severity rule against a month of real ticket evidence and rewrites strategy/themes.md wherever the evidence disagrees with the assumption. It merges two themes the evidence shows are one, splits a theme whose tickets have two different answers, retires a theme with no volume, creates a theme for a cluster that kept landing unclassified, and rewrites a severity rule wherever the outcomes contradict it. Theme ids are never reused and never renumbered. It holds every outbound action unless you released the channel, and it never touches a credential.
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Weekdays, heavy browser lane. Reads every support channel named in strategy/channels.md, meaning the mailbox, the helpdesk queue, the review and rating listings, the marketplace pages, and the forums where the member's products are discussed, and captures every new or changed item as one dated, sourced, severity graded line on the ticket ledger. It replies to nothing, reacts to nothing, marks nothing read or resolved, and takes no action of any kind on LinkedIn. It holds every outbound action unless you released the channel, and it never touches a credential.
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Runs once by hand on the first day and once a month after that, light browser lane. On the first run it researches the business from its own published surfaces before asking anything, writes the strategy folder, creates the ledgers, seeds the opening cards, builds the single page dashboard, reconciles the schedule table, and registers the eight recurring jobs. On every monthly run it re-reads a month of the kit's own evidence, applies what changed, adds a tab for a channel that gained volume, rebuilds, and reconciles every registered job against the table. It holds every outbound action unless you released the channel, and it never enters a credential.
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Weekdays, conditional browser lane. Reads the ticket ledger and the account, billing, and usage surfaces the member named, and decides which paying customers are about to leave. Every flag is a dossier carrying the trip wires that fired, the exact evidence behind each one, that account's whole history, and one suggested save with its cost and the screen it happens on. It reads billing screens and touches no control on them: a refund, a credit, a plan change, and a cancellation are named and never made.
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Weekdays, conditional browser lane. Selects from the ticket ledger hardest first, meaning by severity and then by age within severity, and writes each customer a reply into a dated queue file the member reads, edits, and sends by hand. Where the right answer is a refund, a credit, a plan change, or a cancellation, it names the remedy, the exact amount, and the exact screen, and leaves the granting to the member. It sends only where you released the channel, never posts, never resolves a ticket, spends only where you released it, and never touches a credential.
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Weekly on a Friday, conditional browser lane. Scores the week from the ledgers with a source beside every number and an estimate nowhere, reads the review and rating movement off the member's own listing screens, replays the browser flows the weekday routines depend on, and names the one product change that would have removed the most tickets this week. It sends only where you released the channel, never posts, never resolves anything, spends only where you released it, and never touches a credential.
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Weekly on a Wednesday, light browser lane. Turns the questions that keep coming back into the answer that only has to be written once. It reads the ticket ledger over the recurrence window, finds every theme that crossed its threshold, checks the member's own help center and the existing macros for an answer that already covers it, and writes a reusable macro and a help article draft for every theme that has neither. It publishes only where you released the channel an article, never edits a live help center page, sends only where you released the channel, spends only where you released it, and never touches a credential.
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Analyze NPS, CSAT, and qualitative customer feedback to extract themes, identify trends, and generate actionable insight reports. Use when the user requests customer feedback analysis or provides relevant inputs for this workflow.
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Master the AI tools that transform customer support from cost center to competitive advantage. Automate routine queries, empower agents, and delight customers at scale. Use when "customer support, helpdesk, ticketing system, chatbot, knowledge base, support automation, CSAT, customer service, support, helpdesk, chatbot, knowledge-base, customer-service, automation" mentioned.
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Support operations reconnaissance -- audit current ticket volume, SLA compliance, knowledge base coverage, escalation paths, and CSAT to understand where support is the constraint. Use when asked to "audit our support", "why is our response time bad", "how healthy is our support operation", or "before designing a support system".
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Design support metrics dashboard -- CSAT, FRT, TTR, ticket deflection rate, volume trends, and agent efficiency. Use when asked to "what metrics should support track", "build our support dashboard", "measure support quality", or "audit our support performance".
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Expert support operations guidance for customer service excellence. Use when designing ticket management systems, creating SLA policies, building support tier structures (L1/L2/L3), optimizing knowledge bases, defining severity levels and escalation procedures, implementing support metrics (CSAT, FRT, TTR, FCR), configuring support tool stacks, or building support-to-CS feedback loops. Covers Zendesk, Intercom, Freshdesk, and help desk best practices.
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Strategic Customer Success leadership guidance for CS org design, customer segmentation and tiering (tech touch, low touch, high touch), success metrics and KPIs (NRR, GRR, NPS, CSAT, CES), playbook development, executive stakeholder management, CS technology stack strategy, value realization frameworks, and customer journey mapping. Use when building CS teams, defining customer segments, designing playbooks, measuring success outcomes, or implementing CS platforms.
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"Measure and optimize customer service performance using CSAT, NPS, CES, First Contact Resolution, and text mining on support tickets. Use this skill when the user needs to evaluate CS team performance, identify top complaint drivers, optimize staffing, or build CS dashboards — even if they say 'is our CS team doing well', 'what are customers complaining about', 'how many agents do we need', or 'build a CS dashboard'.".
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Analyze survey and feedback data with statistical distributions, cross-tabulations, significance testing, and open-ended theme extraction. Produces executive-ready survey reports with segment comparisons and action recommendations. Use when processing NPS, CSAT, employee engagement, or market research surveys.
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