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Installs, configures, and tunes Snort 3 intrusion detection system to monitor network traffic for malicious activity using custom and community rulesets, preprocessors, and alert output plugins on authorized network segments.
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Configuring Zscaler Private Access (ZPA) to replace traditional VPN with zero trust network access by deploying App Connectors, defining application segments, configuring access policies based on user identity and device posture, and integrating with IdPs.
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Conducts authorized wireless network penetration tests to assess the security of WiFi infrastructure by testing for weak encryption protocols, captive portal bypasses, evil twin attacks, WPA2/WPA3 handshake capture, rogue access point detection, and client-side attacks. The tester evaluates wireless authentication, network segmentation, and the effectiveness of wireless intrusion detection systems. Activates for requests involving wireless pentest, WiFi security assessment, WPA2/WPA3 testing, or rogue access point detection.
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Captures and analyzes network packet data using Wireshark and tshark to identify malicious traffic patterns, diagnose protocol issues, extract artifacts, and support incident response investigations on authorized network segments.
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Remote-sensing literature search, citation verification, sensor/platform/dataset query strategy, citation file management (.ris/.bib/.enw conversion), and reference management (BibTeX, related articles, DOI/arXiv lookup) across CrossRef, arXiv, Scopus, ScienceDirect, Semantic Scholar, and domain web sources. Use when the user needs coordinated multi-step literature workflows for remote sensing, Earth observation, geospatial AI, GIScience, photogrammetry, SAR, hyperspectral imaging, LiDAR, satellite missions, benchmark datasets, land-cover mapping, change detection, object detection, segmentation, retrieval, or multimodal remote-sensing foundation models. Also trigger on general literature-search needs during academic writing even without the word "Nature", such as searching for papers/literature, doing a literature review, verifying a citation, converting citation files, and Chinese phrasings like 文献检索、查文献、找文献、 文献综述检索、查论文、引文核对、参考文献管理、文献去重.
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Create a look-alike audience from a seed audience and a candidate population dataset. Classifies Rosetta Stone attributes, generates the same materialized-view scoring pipeline Lookalike Studio emits (Naive-Bayes categorical weights + Gaussian continuous similarity), gates on approval, submits via `narrative_workflows_create`, and monitors the build to completion. Use when: "create a lookalike audience", "find more users like this segment", "expand my seed audience to 500k similar users", "score the population against my customers", "build a look-alike of dataset X". (narrative-audience)
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Dynamic path segments ($paramName), splat routes ($ / _splat), optional params ({-$paramName}), prefix/suffix patterns ({$param}.ext), useParams, params.parse/stringify, pathParamsAllowedCharacters, i18n locale patterns.
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複数事業セグメントを持つ企業の、セグメント別の収益性・成長性・資産配分を深掘りする時に使う。「〇〇のセグメント別採算を教えて」「どの事業が稼いでいるか」のような、事業構成に着目した指示で発火する。
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Use the WooCommerce REST API v4 (namespace wc/v4, since WC 10.2) alongside or in place of v3 — verified route catalog (Customers, Orders, Refunds, Products, ShippingZones with DELETE, ShippingZoneMethod, Fulfillments, segmented Settings under /wc/v4/settings/<group>), the hook prefix pattern woocommerce_rest_api_v4_<route>_*, shared error codes, and the RestApiCache trait (WC 10.5+) for endpoint response caching. The v4 AbstractController lives under the Internal namespace and is NOT a public extension surface; plugin-defined REST routes use WP_REST_Controller directly. Use when calling WC REST endpoints, when picking v3 vs v4, or when reviewing /wc/v3/ URLs that now have v4 equivalents. Triggers on wc/v4, /wc/v4/, woocommerce_rest_api_v4_*, RestApiCache, customer-owned Woo endpoints, REST API v4 in WooCommerce context.
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Use when the user asks to "build my brand-safety exclusion lists", "set placement / topic / content exclusions before launch", "add network and audience exclusions", or "prep the A1 brand-safety evidence for the auditor"; produces a placement/network exclusion list, a content-suitability & sensitive-topic block list, an audience/negative-audience exclusion set, and a packaged A1 brand/placement-safety evidence file for the gate. Not for building the audiences you target — use audience-segment-builder; not for computing the RQS or issuing the A1 verdict — use ad-account-auditor. 品牌安全/排除位置/否定受众列表
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Use when the user asks to "watch my list health over time", "flag decaying / unengaged subscribers on a schedule", "why is my open rate drifting down / bounces creeping up", or "build me a re-permission and prune worklist"; runs the scheduled SEND list-decay + suppression-drift watch — an engagement-recency cohort read (30/90/180/365-day), hard-bounce and spam-complaint trend vs benchmark, suppression-list growth/leakage check, and a segmented re-permission / sunset / prune worklist tied to SEND S (list hygiene) and E (engagement-decay) sub-items. Not for the one-time pre-send authentication pre-flight — use deliverability-qa; not for the consent/suppression record itself — use consent-registry; not for computing the EQS or enforcing vetoes — use email-quality-auditor. 邮件列表健康度监控/退订漂移/沉睡用户清理
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Plan, analyze and synthesize customer research into pains, jobs, triggers, objections, language, segments and evidence-backed marketing implications.
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Design lifecycle email programs and sequences with segmentation, deliverability, personalization, testing and measurement.
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Designs and reviews production observability for mobile apps on every stack — Flutter, native Android, native iOS, React Native and Kotlin Multiplatform. Covers crash and error reporting with usable context, breadcrumbs, structured logs, traces and custom metrics, release and cohort segmentation, correlation with backend requests, alerting thresholds, sampling and cost, and what must never be collected. Use when production problems cannot be explained from the available data, when setting up crash reporting or telemetry, or when defining release health alerts.
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SOTA Computer Vision Expert (2026). Specialized in YOLO26, Segment Anything 3 (SAM 3), Vision Language Models, and real-time spatial analysis.
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Use when working with Devcycle — devCycle feature flag management, targeting rules, environments, variable management, and usage metrics. Covers feature creation, variation configuration, targeting rules, audience segments, and API usage tracking. Use when managing feature flags, configuring targeting rules, reviewing variable usage, or analyzing feature adoption in DevCycle.
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Use when working with Aws Xray — aWS X-Ray trace analysis, service map generation, fault and error analysis, sampling rule management, and latency investigation. Covers trace summaries, segment analysis, annotation-based filtering, and group configuration.
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Use when working with Druid — apache Druid datasource analysis, ingestion task management, supervisor status, segment management, and query performance.
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scikit-learn compatible Python toolkit for time series ML: classify, cluster, regress, segment, transform with 30+ algorithms (ROCKET, InceptionTime, KNN-DTW, HIVE-COTE, WEASEL). Handles panel, multivariate, and unequal-length series. Maintained successor to sktime. Alternatives: sktime (larger ecosystem), tslearn (fewer algorithms), catch22 (features only).
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Collect, download, and verify App Store Connect Analytics reports with asc. Use when users need to discover analytics report requests, select report instances by processing date or granularity, download every segment, or verify downloaded files against Apple's size and MD5 metadata before analysis.
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Implement computer vision features including text recognition (OCR), face detection, barcode scanning, image segmentation, object tracking, and document scanning in iOS apps. Covers both the modern Swift-native Vision API (iOS 16+) and legacy VNRequest patterns, VisionKit DataScannerViewController for live camera scanning, and VNCoreMLRequest for custom model inference. Use when adding OCR, barcode scanning, face detection, or custom Core ML model inference with Vision.
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Busabase App-in-Skill dashboard over a fixed, deterministic mock user-behavior funnel dataset (browse → search → compare → booking attempt/abandon → complete) for a generic consumer booking product. Use when the user invokes $kelly-behavior-predict or /kelly-behavior-predict, wants to review funnel drop-off, per-segment predicted next actions, or backtest a rule-based "predicted next action" heuristic against a mock historical sample. Fully deterministic mock data and a hand-recomputable rule — never a real ML/LLM model, never a live system.
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Generates the monthly revenue-accountable marketing report that B2B SaaS marketing leaders deliver to CEOs, CFOs, executive teams, and boards. Use when preparing the monthly CEO report, drafting the marketing section of a board deck, defending budget decisions, diagnosing pipeline gaps, or transitioning marketing from MQL/SQO reporting to revenue, CAC payback, and forward pipeline coverage. Designed for B2B SaaS demand generation leaders who own pipeline targets across segments, geos, and motions, and need to explain marketing's revenue contribution in executive language.
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Generates standard operating procedures for B2B SaaS marketing operations: campaign naming conventions, UTM governance, tool administration, workflow QA, data hygiene, lead routing, and incident response playbooks. Use when setting up or auditing marketing ops processes, onboarding a new marketing ops hire, standardizing campaign tracking, fixing broken lead routing, cleaning up HubSpot or CRM data, building QA checklists for campaign launches, or creating incident response procedures for marketing system failures. Also use when asked to "fix our UTM mess," "standardize campaign naming," "build a QA checklist," "audit our marketing ops," or "create an ops runbook." Designed for B2B SaaS marketing teams running HubSpot with multi-geo, multi-segment operations.
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