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Found 18 Skills
Analyze collections of user feedback to identify patterns and themes. Use when you have user feedback from multiple sources that needs synthesis.
Analyze user/customer feedback and produce a User Feedback Analysis Pack (source inventory, normalized feedback table, taxonomy/codebook, themes + evidence, recommendations, and feedback loop). Use for voice of customer, feature request analysis, support ticket synthesis, churn reason synthesis, and survey open-ends.
Use when users don't notice feedback, miss state changes, or can't tell if their action worked
Help users synthesize and act on customer feedback. Use when someone is analyzing NPS responses, processing support tickets, reviewing user research, synthesizing feedback from multiple channels, or trying to identify patterns in customer input.
Builds feedback collection systems using Superhuman's PMF framework and YC's "talk to users" methodology. Use when implementing NPS surveys, scheduling user interviews, or measuring product-market fit.
Report user activity and submit feedback to Glean. Use when logging user interactions or providing relevance feedback on search results.
Capture user corrections and feedback after any skill runs, persist them as learned instructions, and silently apply them on future invocations. TRIGGER when: user gives feedback or corrections after a skill runs — e.g. "next time only show top 5", "always use bullet points", "don't include X", "from now on...", "remember to...". Also: "What have you learned about {skill}?", "Show skill tuning", "Clear skill tuning for {skill}"
Implements the Syncfusion WPF SfRating control for star-based rating input. Use when adding user feedback or review ratings, configuring rating precision (standard/half/exact), or presenting read-only rating displays.
Asks for user feedback after each task or cron job completion and runs a recursive learning flow. If output is good, asks what was good until 10 approvals; if needs improvement, asks why/how/what via multiple choice plus optional examples, uses web search and iterative thinking to resolve, and caps iterations by severity (slight 5, medium 10, severe 20). Keeps feedback non-intrusive. Use when completing discrete tasks or cron jobs for the user.
Integrates Kelet into AI applications end-to-end: instruments agentic flows with OTEL tracing, maps session boundaries, adds user feedback signals (VoteFeedback, edit tracking, coded behavioral hooks), generates synthetic signal evaluator deeplinks, and verifies the integration. Kelet is an AI agent that performs Root Cause Analysis on AI app failures — it ingests traces and signals, clusters failure patterns, and suggests fixes. Use when the developer mentions Kelet or asks to integrate, set up, instrument, or add tracing/signals/feedback to their AI app. Triggers on: "integrate Kelet", "set up Kelet", "add Kelet", "instrument my agent", "connect Kelet", "use Kelet".
Toast notifications, alerts, feedback messages, and their timing. Use when adding user feedback, success messages, or alerts.
Pendo platform help — product analytics, in-app guides, session replay, NPS/CSAT surveys, feature adoption tracking, Leo AI. Use when Pendo guides aren't showing, feature tagging is tedious, analytics data looks wrong, users aren't completing onboarding, NPS scores are flat, need help with Pendo API or aggregation queries, setting up Pendo for the first time, or comparing Pendo to Appcues or WalkMe. Do NOT use for in-app messaging strategy across platforms (use /sales-in-app-messaging) or general customer feedback strategy (use /sales-customer-feedback).