「cis」の検索結果

Cyber Risk Quantification engineer for CyberRadar. Implements FAIR (Factor Analysis of Information Risk) methodology, Monte Carlo simulation for loss distribution modeling, financial impact quantification in multi-currency (SAR, USD, EUR, GBP), loss exceedance curves, risk treatment ROI calculation, insurance premium optimization, and board-ready financial risk reporting. Transforms qualitative risk assessments into monetary terms for executive decision-making. Triggers on: CRQ, FAIR, Monte Carlo, financial impact, loss modeling, risk quantification, cyber insurance, risk treatment ROI, financial risk.

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Quantify cyber risk using FAIR methodology with Monte Carlo simulation, assess control effectiveness against NIST CSF/CIS/ISO 27001 frameworks, evaluate risk appetite alignment, and analyze cyber insurance coverage adequacy. Covers threat landscape mapping, asset valuation, loss event frequency and magnitude estimation, annualized loss expectancy calculation, control gap analysis with ROI ranking, residual risk assessment, and insurance policy gap modeling at 90th/95th/99th percentile loss scenarios. Use when building risk registers, preparing board-level risk dashboards, evaluating security investment priorities, assessing cyber insurance coverage, or auditing any organization's risk quantification maturity per NIST RMF, ISO 27005, or FAIR standards.

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 should be used when the user asks to appeal a MetLife pet insurance claim, dispute a denial, challenge an underpayment, draft an appeal letter, review an EOB for appeal grounds, or fight a pet insurance claim decision.

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。

Analyze latest policy analysis sweep runs (*_policy_analysis_*) by comparing episodes/summary metrics, diagnostics, and video artifacts; generate a concise markdown report and optional frame snapshots.

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Expert knowledge of Behandlingsresultat (treatment result) domain in melosys-api. Use when: (1) Understanding result types and their meaning (FASTSATT_LOVVALGSLAND, MEDLEM_I_FOLKETRYGDEN, etc.), (2) Working with periods - Lovvalgsperiode, Medlemskapsperiode, Anmodningsperiode, Utpekingsperiode, (3) Debugging vedtak/decision metadata, criteria evaluation, or social insurance charges, (4) Understanding EU/EEA coordination logic and Article 16 exception handling, (5) Working with trygdeavgift (social insurance charge) calculations.

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Review existing analytics — find all dashboards and reports, check who uses them, whether metrics are defined, and whether they drive decisions. Recommend what to keep, kill, or add. Use when asked "are our dashboards useful", "analytics review", or "metrics audit".

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A/B test design — produce an experiment spec with hypothesis, primary metric, MDE, sample size, run time, and decision rule. Also determines when NOT to A/B test and what to do instead. Use when asked to "design an A/B test", "should we test this", "experiment design", "how do we know if this works", "what's the sample size", or "set up an experiment".

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Design meeting and communication cadence — what meetings to run, how often, who attends, and what decisions they make. Use when asked to "design our meeting structure", "what meetings should we have", "reduce meeting load", or "build our operating rhythm".

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。

Scope arbitration — resolve disagreements between product and engineering on what is in or out of scope, with a decision log and escalation path. Use when asked to "resolve this scope disagreement", "arbitrate between product and eng", "scope is creeping", "we can't agree on what's in scope", or "help us decide what to cut".

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。

Use when asked to define a design token system, create tokens, document tokens, set up CSS custom properties, build a Tailwind token config, establish a spacing scale, define color semantics, or bridge design decisions to code. Examples: "set up design tokens", "define our token system", "create CSS variables for the design system", "document our color tokens", "establish a spacing scale".

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。

Knowledge engineer — architecture docs, ADRs, diagrams, changelogs, onboarding, and reports. Use when asked to "write an ADR", "map the architecture", "document this decision", "onboarding docs", or "what changed".

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。

Design escalation path -- Tier 1 to Tier 2 to Engineering handoff, decision criteria, and communication templates. Use when asked to "design our escalation process", "when should support escalate to engineering", "build an escalation runbook", or "reduce escalation rate".

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。

Generate onboarding documentation — what this project does, how to set up locally, where things live, key decisions, how to deploy. Written for day-one engineers who know nothing. Use when asked for "onboarding docs", "new engineer guide", "how to get started", or "developer setup".

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。

Operating doctrine for AI-era enterprise sales. Use when an agent works on accounts, deals, outreach, coaching, or pipeline — any revenue-affecting decision.

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。

Pipeline from a URL or DESIGN.md to tokens, decisions and a UI spec. Use when building UI for an existing brand, auditing a design system or fixing visual inconsistency.

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。

Mine past agent sessions for working prompts, decisions, and patterns. Use when "what did I ask?", "find that prompt", session archaeology, or agent history.

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。

Evidence-first expert knowledge handoff skill. Use when a founder, senior engineer, salesperson, support lead, consultant, or other domain expert is leaving, changing roles, or needs their judgment made reusable. It turns consented documents, chat, meeting notes, interviews, and explicit facts into a traceable handoff package. Trigger on requests such as: "capture this expert's judgment", "prepare a departure handoff", "make an onboarding mentor", "extract the decision rules", or "把专家经验交接下来".

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。

Real-world problem formulation, mathematical abstraction, and applied mathematics for translating between practical problems and mathematical frameworks. Covers the modeling cycle (problem identification, assumptions, formulation, analysis, validation, interpretation), Polya's framework adapted for modeling, common model types (linear, exponential, logistic, periodic, power-law), dimensional analysis (Buckingham Pi theorem), optimization (linear programming, gradient descent, constraint satisfaction), probability models (Markov chains, queuing theory, Monte Carlo simulation), statistical modeling (regression, hypothesis testing, model selection), model criticism (overfitting, underfitting, sensitivity analysis), and real-world case studies. Use when formulating mathematical models, performing dimensional analysis, optimizing systems, running simulations, or evaluating model validity.

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。

Somatic movement and whole-body retraining as Joseph Pilates designed it in the Contrology method and as Moshé Feldenkrais designed it in Awareness Through Movement and Functional Integration, with enough cross-reference to the broader somatics landscape (Alexander Technique, Hanna Somatics, Body-Mind Centering) that an agent can place a user into the right method. Covers the Pilates reformer and mat system, the six Pilates principles, the Feldenkrais ATM lesson structure, the nervous-system learning frame that distinguishes somatics from exercise, and the safety posture that matters for rehab populations. Use for queries about core training, rehab-adjacent movement, chronic pain patterns, and learning-based movement re-education.

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。

Household-scale nutrition planning that treats meals as a weekly system, not a series of independent decisions. Covers macronutrient balance, the plate method, weekly rotation, leftover architecture, pantry-to-plate translation, and the food-safety floor that every meal plan must meet. Use when building a weekly menu, adapting a diet to a new household member, planning for a week of predictable meals on a budget, or teaching a learner how a kitchen feeds people over time.

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。

The household as an economic unit with income, expenses, savings, and debt. Covers the envelope method, fixed vs variable expenses, the 50-30-20 baseline, emergency reserves, the true cost of ownership, opportunity cost in household decisions, and the distinction between a budget and a spending plan. Use when building a household budget, diagnosing why a household is always over, planning a major purchase, or teaching financial literacy at the household level.

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。

Foundations of individual economic decision-making, market structures, and strategic interaction. Covers supply and demand analysis, price elasticity, consumer and producer surplus, market structures (perfect competition, monopoly, oligopoly, monopolistic competition), game theory (Nash equilibrium, dominant strategies, repeated games), and welfare economics. Use when analyzing individual markets, firm behavior, pricing strategies, consumer choice, or strategic interactions between economic agents.

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。

Systematic departures from rational choice theory and their implications for economic analysis and policy. Covers cognitive heuristics (anchoring, availability, representativeness), biases (loss aversion, status quo, overconfidence), prospect theory (reference dependence, probability weighting, diminishing sensitivity), nudge theory and choice architecture, and the integration of psychological findings into economic models. Use when analyzing decision-making under uncertainty, evaluating policy interventions that exploit behavioral patterns, or assessing where standard rational-agent models break down.

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。

Understanding and managing what digital services collect, store, share, and infer about you. Covers password security and entropy, multi-factor authentication, privacy settings, data minimization, the difference between first-party and third-party tracking, cookies and fingerprinting, privacy laws (GDPR, CCPA), and how to respond to data breaches. Use when helping a learner make informed decisions about what to share, with whom, and under what terms.

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。