「trial」の検索結果

This skill covers deploying anomaly detection systems for industrial control environments using machine learning models trained on OT network baselines, physics-based process models, and behavioral analysis of industrial protocol communications. It addresses building normal behavior profiles for SCADA polling patterns, detecting deviations in Modbus/DNP3/OPC UA traffic, identifying rogue devices, and correlating network anomalies with physical process data from historians.

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This skill covers deploying anomaly detection systems for industrial control environments using machine learning models trained on OT network baselines, physics-based process models, and behavioral analysis of industrial protocol communications. It addresses building normal behavior profiles for SCADA polling patterns, detecting deviations in Modbus/DNP3/OPC UA traffic, identifying rogue devices, and correlating network anomalies with physical process data from historians.

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Strategic clinical trial design feasibility assessment. Analyzes 6 dimensions (endpoint, population, comparator, effect size, duration, regulatory pathway) using precedent trials and FDA guidance. Produces enrollment projections, endpoint recommendations, and approval-pathway analysis. Use for trial-protocol design, power/sample-size estimation, comparator selection, and FDA submission strategy. Driven by precedent-based reasoning rather than first-principles math.

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Prepare the internal case for hosting an Otameshi Tenshoku trial: total what it costs the company with candidate pay, staff hours and platform or incidental fees kept apart, compare it with the budget, state what remains if no hire follows, and lay out the risks, stop conditions, decision owners and deadlines. Use when a hiring manager needs approval or budget from a decision maker, a recruiter has to explain the plan to the team that will host, or a small-business owner wants to see the full cost including their own time before posting; do not use to design the work (trial-work-design), to write the listing (trial-listing-draft), to predict whether a hire will follow, to state platform fees, agency fees or market rates from memory, or to submit the request or post the listing on the user's behalf.

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Help job seekers use Otameshi Tenshoku to prepare profiles and applications, clarify trial conditions, plan learning visits or paid trial work, and reflect on the experience. Use when a candidate wants to apply for, prepare for, negotiate the scope of, or review an otameshi work experience; do not use to predict whether they will be hired or to judge their aptitude, to state fees, contract terms, or service conditions from memory, or to submit an application, update a profile, or send a message on their behalf. Employer-side requests belong to the employer skills (trial-work-design, trial-listing-draft, applicant-reply and others).

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Generate a single-file industrial-archive product landing page (Nordic museum aesthetic, hash-routed detail pages). Takes a natural-language description of one or more "exhibit-style" products (objects, tools, hardware, watches, kitchenware, lighting, audio, instruments, anything with a designed/forged/restrained quality); outputs ONE working .html file. Visitors see a black-framed cover that flips between exhibits, sidebar index, side meta column (material/origin/year/dimension), and click any exhibit → full detail page (giant italic title, spec table, hero image, lead + 3 prose paragraphs, gallery, prev/next). Runs in BOTH Claude Code (writes to ~/Desktop) and GLM / online chat (outputs HTML inline). Trigger: "做个工业风产品落地页", "做克制之物风格的展品页", "industrial-archive-gen ...", or when user drags this skill folder into chat asking for a product page in the Nordic-industrial archive style.

日本語の概要は準備中です。原文の説明を表示しています。

This skill covers deploying anomaly detection systems for industrial control environments using machine learning models trained on OT network baselines, physics-based process models, and behavioral analysis of industrial protocol communications. It addresses building normal behavior profiles for SCADA polling patterns, detecting deviations in Modbus/DNP3/OPC UA traffic, identifying rogue devices, and correlating network anomalies with physical process data from historians.

日本語の概要は準備中です。原文の説明を表示しています。

When the user wants to optimize signup, registration, account creation, or trial activation flows. Also use when the user mentions "signup conversions," "registration friction," "signup form optimization," "free trial signup," "reduce signup dropoff," "account creation flow," "people aren't signing up," "signup abandonment," "trial conversion rate," "nobody completes registration," "too many steps to sign up," or "simplify our signup." Use this whenever the user has a signup or registration flow that isn't performing. For post-signup onboarding, see onboarding. For lead capture forms (not account creation), see cro.

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This skill covers detecting cyber attacks targeting Supervisory Control and Data Acquisition (SCADA) systems including man-in-the-middle attacks on industrial protocols, unauthorized command injection into PLCs, HMI compromise, historian data manipulation, and denial-of-service against control system communications. It leverages OT-specific intrusion detection systems, industrial protocol anomaly detection, and process data analytics to identify attacks that traditional IT security tools miss.

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Submit compact ClinicalTrials.gov API v2 requests for study search, metadata, enums, search areas, and field statistics. Use when a user wants concise ClinicalTrials.gov summaries

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Help users install and configure the mirrord Operator for team/enterprise environments. Use when users ask about operator setup, Helm installation, cloud API key or license configuration, air-gapped/offline licensing, enabling features (queue splitting, DB branching, preview environments, multi-cluster), internal registries, OpenShift/GKE Autopilot, RBAC, or multi-user mirrord deployments. Also use when an AI agent hits a Team or Enterprise feature on a cluster with no license and needs to offer the user a trial (agent-started trial; the trial is Enterprise tier).

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When the user wants to design, test, or optimize their app's paywall — layout, copy, pricing display, trial offers, plan structure, hard vs soft paywall, paywall placement, or paywall A/B tests. Use when the user mentions "paywall", "paywall design", "paywall conversion", "trial-to-paid", "soft paywall", "hard paywall", "paywall A/B test", "paywall copy", "plan picker", "annual vs monthly display", "best paywall", "RevenueCat paywall", "Superwall", "Adapty", or "my paywall isn't converting". For overall pricing strategy and monetization model choice, see monetization-strategy. For trial nurture, dunning, and churn, see subscription-lifecycle. For where in the onboarding the paywall fires, see onboarding-optimization.

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Bevy ECS skill for a Bevy time trial. Keywords: Bevy, time trial, timer, split, ECS.

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Guides DPIA for health and medical data processing covering Art. 9(2)(h)-(j) exemptions, HIPAA crosswalk for transatlantic operations, clinical trial data protection under EU CTR 536/2014, and genetic data specifics under Art. 9(1). Activate for healthcare systems, clinical research, health apps, or medical device data. Keywords: health data, DPIA, Art. 9, clinical trial, genetic data, HIPAA, medical records, special category.

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End-to-end clinical trial analysis workflow from CDISC SDTM/ADaM loading through ICH E9(R1) estimand-driven primary analysis to CONSORT 2025 regulatory-compliant reporting. Covers data preparation, FDA 2023 marginal vs conditional logistic regression, categorical tests with Boschloo, modern HTE/subgroup methods, missing-data sensitivity (MMRM, reference-based MI, Permutt tipping point), graphical multiplicity (Bretz-Maurer), survival analysis (Cox/RMST/competing risks) when applicable, and Table 1. Use when performing a complete analysis of clinical trial data.

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Think and work like an expert Industrial Microbiologist. Use when a task calls for Industrial Microbiologist judgment. Reasons from SmF/SSF physiology, fed-batch μ/OTR–OUR/RQ, DoE media optimization, PAT (Raman soft sensors), ICH Q8/Q7 characterization, bioleaching, SVI/F/M filament ID, and phage (10⁴–10⁶ PFU/mL) plant hygiene; treats antifoam kLa penalty, F₀ CIP/SIP cold spots, DSP mass balance, and golden-batch vs biofilm red herrings as first-class failure modes.

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Think and work like an expert Industrial Engineer. Use when a task calls for Industrial Engineer judgment. Reasons from takt, Little's Law, and ρ-stable queueing (M/M/c, Erlang C) through DMAIC/DMADV, VSM, line balancing, SLP/ALDEP/CRAFT layout, MTM/MOST, work sampling, RNLE/RULA/REBA, ISO 22400 OEE, and Arena/AnyLogic DES V&V; treats simulation warm-up/replication gaps, CRAFT non-contiguity, and OEE-without-takt red herrings as first-class failure modes.

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Think and work like an expert Industrial Ecologist. Use when a task calls for Industrial Ecologist judgment. Reasons from mass balance closure, in-use stocks, and system boundaries through STAN (ÖNorm S 2096), dynamic MFA with Weibull lifetime distributions, EEIO tables (EXIOBASE, USEEIO) and pedigree-scored Monte Carlo while treating non-closing residuals, re-export trade hubs, downcounted informal-sector leakage, and Kalundborg-copied symbiosis without quality-spec match as first-class failure modes.

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Think and work like an expert Clinical Trial Scientist. Use when a task calls for Clinical Trial Scientist judgment. Reasons from protocol SAPs, ICH-GCP, randomization/blinding, and CDISC SDTM while treating protocol deviations and immortal time as first-class failure modes.

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This skill applies the analytical frameworks of Ronald C. Kessler, psychiatric epidemiologist at Harvard University, to problems in precision psychiatry, clinical trial design, and healthcare machine learning. Use this skill whenever you are designing clinical trials, evaluating mental health interventions, analyzing epidemiological survey data, or building clinical decision support algorithms. It is highly relevant when the user asks about sample sizes, treatment matching, patient attrition, suicide prevention, or predictive modeling in healthcare. Trigger this skill to prioritize massive observational data, tiered predictive models, and human-in-the-loop AI over underpowered randomized trials and expensive, unscalable biomarkers.

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CRE Industrial analysis suite - 8 specialist skills for U.S. industrial acquisitions, covering market study, lease roster analysis, lease abstraction, tenant credit, physical inspection, underwriting, financing fit, and investment committee memo writing.

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When the user wants to optimize signup, registration, account creation, or trial activation flows. Also use when the user mentions "signup conversions," "registration friction," "signup form optimization," "free trial signup," "reduce signup dropoff," "account creation flow," "people aren't signing up," "signup abandonment," "trial conversion rate," "nobody completes registration," "too many steps to sign up," or "simplify our signup." Use this whenever the user has a signup or registration flow that isn't performing. For post-signup onboarding, see onboarding. For lead capture forms (not account creation), see cro.

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Industrial communication protocols for Zephyr RTOS. Covers Modbus RTU (serial), Modbus TCP (Ethernet/Wi-Fi), and CANopen basics. Trigger when building factory automation controllers, industrial sensors, or medical equipment interfaces.

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Use ENCODE functional genomics data for disease mechanism research. Use when the user wants to connect GWAS variants to regulatory elements, annotate disease-associated loci with functional data, identify therapeutic targets from epigenomic data, build disease regulatory models, cross-reference with clinical trials and drug databases, or conduct any disease-focused, pathology-driven, or clinical variant interpretation workflow. Covers the full pipeline from disease-tissue mapping through GWAS variant annotation, heritability enrichment, cancer epigenomics, drug target identification, and clinical trial cross-referencing. Integrates ENCODE with Open Targets, PubMed, ClinicalTrials.gov, and bioRxiv.

日本語の概要は準備中です。原文の説明を表示しています。