feedback-analysis

Prepare for a customer call by synthesizing engagement, sentiment, and feedback from Pendo analytics. Use this skill whenever someone asks about account health, wants to prepare for a customer call, needs an account health report, asks about engagement or usage for a specific customer, or wants a summary of how an account is doing. Also trigger when users mention preparing for a QBR, checking on a customer, or reviewing an account before a meeting — even if they don't say "account health" explicitly.

Generate a shareable health report for one application, summarizing engagement, acquisition, retention, average time, stickiness, frustration signals, and survey scores into a verdict and next steps. Use this skill whenever someone asks how healthy an app is, wants an app health report, wants an overall picture of how an application is doing, or asks about an app's engagement, retention, stickiness, or friction at the application level — even if they don't say "app health" explicitly. This is app-level (a whole application), not account-level — for a single customer account use account-health, and for a single feature use feature-adoption.

Use when planning a product feature, fix, or prioritization task where Pendo usage data, signals, issues, or customer feedback could ground the plan in real product behavior — e.g. "plan a fix for the pricing-page dropoff", "what should I build next", "improve the welcome flow". Skip for pure code refactors, infra, or tooling work.

Analyze feature adoption rates, identify power users vs laggards, and track adoption trends using Pendo analytics. Use this skill whenever someone asks about feature adoption, feature usage, who's using a feature, adoption rates, feature rollout progress, or wants to understand how a specific feature is performing. Also trigger when users mention tracking a feature launch, finding champions or power users of a feature, identifying accounts that haven't adopted a feature, comparing adoption across segments, or analyzing usage trends over time — even if they don't say "adoption" explicitly. If someone asks "who's using X" or "how is feature Y doing", this is the right skill.

Triage issues captured in a Pendo session replay by fetching devlogs, mapping errors to the local codebase, proposing fixes, and prompting for a pull request. Accepts either a replay URL directly or a ticket reference (e.g. Jira key or URL, GitHub issue URL, etc.) and will pull the replay link from the ticket. Use whenever someone hands off a user-reported bug — via a replay link or a ticket that contains one — and asks to investigate, diagnose, debug, triage, or fix it. Requires Pendo connector; uses whatever ticketing MCP or CLI is available (Atlassian, `gh`, etc.) when a ticket is supplied.

Run a guide-review pass over the Guide Signals Pendo has already produced — list the archive, deactivate, and anomaly signals for an app, show each with its evidence and how old it is, then let the user mute a signal or deactivate/archive the guide. Use this skill whenever someone wants to review their guides, clean up stale or declining guides, see which guides Pendo recommends archiving or deactivating, triage guide signals or anomalies, or snooze a signal — even if they don't say "signals" explicitly. Nothing is computed here; the skill reads what the weekly job produced and never changes a guide without an explicit choice.

Creates production-ready Pendo in-app guides as HTML/CSS/JS files, drawing on guide type best practices, org-specific content guidelines, and user-provided context. Trigger this skill whenever a user types /pendo-guides:pendo-guide-creator or uses natural language like "create a guide", "build a Pendo guide", "write a walkthrough", "make an announcement guide", "create an alert", "build a poll", or "write an in-app message". Also trigger when the user describes a guide use case — e.g. "I want to onboard new users", "we're launching a feature and want to notify users in-app", or "I need to collect feedback in the product." Always use this skill proactively when any Pendo guide creation is requested, even if the request is casual or incomplete — the skill handles gathering missing context from the user.

Answer questions about how Orchestrate journeys and emails are doing, read-only. Use when someone asks whether a journey is set up or working as intended, why fewer people got an email than expected or who was missed, whether a journey changed what people did, what Orchestrate achieved over a period (including a QBR, renewal or exec slide), or whether people are getting too many messages. Not for creating or editing journeys (use orchestrate-journeys).

Rigor Explore compatible skill slug for meaningful and potentially novel deep learning research candidates. Use when the researcher has chosen the task family, dataset, benchmark, evaluation method, provided SOTA references, and wants candidate-only exploration on top of `current_research` with auditable repo understanding, idea gating, fair comparison, and governed experiments written to `explore_outputs/`. Do not use for README-first trusted reproduction, open-ended direction finding, narrow code-only or run-only exploration, passive repo analysis, verified novelty claims, or implicit experimentation.

Rigor Reproduce compatible skill slug for README-first deep learning repository reproduction. Use when the user wants an end-to-end, minimal-trustworthy flow that reads the repository first, selects the smallest documented inference or evaluation target, coordinates intake, setup, trusted execution, optional trusted training, optional repository analysis, and optional paper-gap resolution, enforces conservative patch rules, records evidence assumptions deviations and human decision points, and writes the standardized `repro_outputs/` bundle. Do not use for paper summary, generic environment setup, isolated repo scanning, standalone command execution, silent protocol changes, score chasing, or broad research assistance outside repository-grounded reproduction.

Rigor Train skill for deep learning research repositories. Use when a documented or selected training command should be run conservatively for startup verification, short-run verification, full kickoff, or resume, with command, config, seed, log, checkpoint, status, and metric evidence written to standardized `train_outputs/`. Do not use for environment setup, exploratory sweeps, speculative idea implementation, or end-to-end orchestration.

Rigor Improve / Rigor Explore run leaf skill for bounded exploratory evidence in deep learning research repositories. Use when the researcher explicitly authorizes exploratory runs such as small-subset validation, short-cycle guess-and-check, batch sweeps, idle-GPU search, or quick transfer-learning trials, with fair-comparison caveats and no-overclaim summaries in `explore_outputs/`. Do not use for end-to-end exploration orchestration on top of `current_research`, trusted baseline execution, conservative training verification, default routing, verified SOTA claims, or implicit experimentation.

Search, scrape, crawl, extract structured data, and monitor web pages via the ScrapeGraph AI CLI. Use when the user asks to search the web, scrape a webpage, grab content from a URL, extract JSON from a site, crawl documentation or site sections, monitor a page for changes, inspect request history, check ScrapeGraph credits, or validate API setup.

When the user wants to create sales collateral, pitch decks, one-pagers, objection handling docs, or demo scripts. Also use when the user mentions 'sales deck,' 'pitch deck,' 'one-pager,' 'leave-behind,' 'objection handling,' 'deal-specific ROI analysis,' 'demo script,' 'talk track,' 'sales playbook,' 'proposal template,' 'buyer persona card,' 'battle card,' 'battlecard,' 'competitive one-pager,' 'win-loss analysis,' 'why are we losing deals,' 'loss reasons,' 'how do I respond to this objection,' 'prospect just said,' 'help my sales team,' 'sales materials,' or 'what should I give my sales reps,' 'cold call script,' or 'voicemail script.' Use this for any document or asset that helps a sales team close deals. For public competitor comparison pages, see competitors. For running buyer interviews or churn research, see customer-research. For marketing website copy, see copywriting. For cold outreach emails, see cold-email. For the offer being sold (bonuses, guarantees, pricing structure), see offers.

Web search and content extraction with Tavily and Exa via inference.sh CLI. Apps: Tavily Search, Tavily Extract, Exa Search, Exa Answer, Exa Extract. Capabilities: AI-powered search, content extraction, direct answers, research. Use for: research, RAG pipelines, fact-checking, content aggregation, agents. Triggers: web search, tavily, exa, search api, content extraction, research, internet search, ai search, search assistant, web scraping, rag, perplexity alternative

When the user wants to conduct, analyze, or synthesize customer research. Use when the user mentions "customer research," "ICP research," "talk to customers," "analyze transcripts," "customer interviews," "survey analysis," "support ticket analysis," "voice of customer," "VOC," "build personas," "customer personas," "jobs to be done," "JTBD," "what do customers say," "what are customers struggling with," "Reddit mining," "G2 reviews," "review mining," "digital watering holes," "community research," "forum research," "competitor reviews," "customer sentiment," "PMF survey," "product/market fit survey," "customer interview questions," "interview outreach," "Sales Safari," or "find out why customers churn/convert/buy." Use for analyzing existing research assets, mining online sources, AND running primary research (interviews and surveys). For writing copy informed by research, see copywriting. For acting on research to improve pages, see cro.