segment-analysis

Find, build and launch A/B tests on a Noibu-monitored store: ranked test ideas from 30 days of Noibu data and the live site, flag-gated variation code, a pull request or Shopify preview theme, automated QA, and a started test. Use for "what should we A/B test", CRO or experiment ideas, building a specific test, implementing a test already found, or taking a Noibu draft test live. Requires the Noibu MCP connector.

Audit a website for WCAG 2.1 AA accessibility violations using automated scanning and Noibu traffic data, and optionally draft the fixes. Use when you want to know what's inaccessible across the store, check WCAG or ADA compliance, find contrast or heading errors, audit for screen reader support, generate an accessibility report, or fix accessibility violations with pull requests or tickets.

Builds a rich business-context Markdown file (a CLAUDE.md / context.md) so Noibu skills and Claude stop hallucinating and reason with real context about the business. Use this skill whenever someone wants to create, set up, or improve their business context file, onboard a new store to Noibu, 'build my MD file', 'make a context file', 'set up my CLAUDE.md', capture company background for the AI, or asks why Claude keeps getting their business wrong. Also trigger on looser phrasings like 'help Claude understand my business', 'document my store for the AI', 'what should I tell Claude about my company', or when a user is starting with Noibu and has no context file yet. Even if the user just says 'build the MD file' or 'set up context', use this skill. It does deep web research on the business (always via a subagent), interviews the user about goals, money leaks, deployment process, and team setup, writes a clean Markdown file, then persists the context to the Noibu backend for the domain.

Analyze checkout performance and health using Noibu data. Use when you want to know where shoppers drop off in the checkout funnel, what payment or delivery methods customers are using, why completion rates are low, what priority errors are hurting checkout, or how cart and order values benchmark.

Analyze product and collection performance using Noibu data. Use when you want to know which products are underperforming, what your best-selling product type is, why a product isn't converting, how your collections are performing, which products get views but no sales, or where shoppers drop off in the product funnel.

  Required reference for calling `noibu_*` tools — field and measure semantics,   query constraints (the `orderBy` requirement, row caps,   unique-measure rule), the call-metadata argument convention (`rationale`, `skill`), and domain   resolution. Also covers parsing of pasted `console.noibu.com` URLs.   Loads alongside topical Noibu skills   (e.g., `checkout-analysis`, `tech-diagnosis`), not instead of them — they   own routing for their respective domains.

An instant, at-a-glance read on your store's health from the last 24 hours — with the option to save it as a live Cowork dashboard artifact or schedule a recurring report. Use to check on how the overall store is doing, save a live dashboard, or schedule a recurring summary.

Diagnose technical issues and Core Web Vital performance problems using Noibu data, and recommend a fix. Use when you want to know what's broken across the store, why an error is happening, what's causing errors, the root cause of poor page performance, how to fix a technical issue, why an error rate spiked, or where errors are concentrated.

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.