zia-investigate-sandbox

Analyze Zscaler Microsegmentation (ZMS) policy rules for optimization opportunities. Reviews custom and default policy rules, identifies stale or unused rules, detects overly permissive rules, maps cross-zone communication patterns, and assesses default security posture. Use when an administrator asks: 'Are there unused policy rules?', 'Which rules are too broad?', 'Optimize our segmentation policies', 'Review our default deny posture', 'Show me policy rule coverage', or 'Analyze our ZMS policies.'

End-to-end onboarding of a new Browser Access (BA) application in Zscaler Private Access. Walks through the full dependency chain — App connector group, server group, segment group, BA TLS certificate, BA application segment with apps_config, and access policy rule — for web apps that should be reachable through the browser without Zscaler Client Connector. Use only when the admin explicitly asks for Browser Access. DO NOT USE WHEN: the admin wants a regular client-routed application segment (use zpa-application_segment-onboard instead).

Troubleshoot Zscaler Microsegmentation (ZMS) agent deployment and connectivity issues. Investigates agent fleet health, connection status, version compliance, agent group configuration, provisioning keys, and TOTP secrets. Use when an administrator reports: 'Agents are disconnected', 'Agent enrollment failing', 'How do I provision new agents?', 'Check agent versions', or 'Agent not connecting.'

Review Zscaler Microsegmentation (ZMS) tag classification, application discovery, and tag-to-resource-group mapping. Investigates tag namespaces (CUSTOM, EXTERNAL, ML), tag keys and values, application catalog entries, and how tags drive managed resource group membership. Use when an administrator asks: 'Show me our tag structure', 'What cloud tags are imported?', 'How are resource groups using tags?', 'What applications were discovered?', 'Review tag classification', or 'Are ML tags being used?'

Audit the overall Zscaler Microsegmentation (ZMS) deployment posture. Reviews agent fleet health, workload protection coverage, resource group structure, policy rules, app zones, application catalog, and tag-based classification. Use when an administrator asks: 'What is our microsegmentation coverage?', 'How many workloads are protected?', 'Show me our ZMS policies', 'Review our microsegmentation deployment', or 'Audit our ZMS posture.'

Assess Zscaler Microsegmentation (ZMS) workload protection coverage and identify protection gaps. Investigates resource protection status, resource group membership, unprotected workloads by cloud and region, and resource group coverage gaps. Use when an administrator asks: 'Which workloads are unprotected?', 'What is our microsegmentation coverage?', 'Find protection gaps', 'Which resource groups have no policies?', 'Show me unprotected resources', or 'What is our workload coverage percentage?'

Audit shadow IT and SaaS application usage using Zscaler Analytics (Z-Insights). Discovers unsanctioned applications, assesses risk scores, monitors CASB-protected SaaS usage, tracks data transfers to shadow apps, and reviews IoT device inventory. Use when a security team asks: 'What shadow IT apps are being used?', 'Show me unsanctioned SaaS usage', 'What is our SaaS risk exposure?', 'How many IoT devices are on our network?', or 'Generate a shadow IT report.'

Analyze web traffic patterns using Zscaler Analytics (Z-Insights). Examines traffic distribution by location, protocol breakdown (HTTP vs HTTPS), threat categories, DLP violations, and volume trends over time. Use when an administrator asks: 'Show me web traffic by location', 'What protocols are in use?', 'Are there any DLP violations?', 'What does our traffic look like?', or 'Show traffic trends.'

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.