「nes」の検索結果

"Apply Hierarchical Linear Modeling (HLM) to analyze nested data structures with random intercepts and slopes, accounting for intra-class correlation and cross-level interactions. Use this skill when the user has students nested in schools, employees in firms, or repeated measures in individuals, needs to partition variance across levels, or when they ask 'how do I handle nested data', 'what is ICC', or 'do group-level factors moderate individual-level relationships'.".

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"SME and family-business management for Taiwan's industrial structure (98% SME, 70%+ family-controlled listed). Integrates four pillars: resource-constrained decision-making, family governance (Tagiuri-Davis three circles), four-layer succession, and professionalization. Use for sub-200-employee strategy, capital-constrained prioritization, family-vs-professional-manager conflict, founder handover, family-constitution design, or SME financing limits. Triggers: 『二代接班』『家族企業』『中小企業』『家族憲章』『家族辦公室』『傳承規劃』『小公司策略』『資源有限』『家族跟專業經理人』『三圓模型』『隱形冠軍』. For 成大 EMBA SME strategy, 政大 EMBA family-business succession, 逢甲 EMBA SME management cases. Fills Asgard gaps in resource-constrained strategy and Chinese-family governance.".

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。

"Run conjoint analysis to measure how product attributes drive consumer preferences and willingness to pay. Use this skill when the user needs to quantify feature value trade-offs, estimate willingness to pay for specific features, or optimize product configuration — even if they say 'which features do customers value most', 'willingness to pay for feature X', or 'product attribute trade-offs'.".

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。

Performs a lightweight pre-release readiness review of Atlassian Forge apps across manifest/module wiring, architecture, runtime compatibility, dependency posture, tests, deploy readiness, and obvious security, cost, or reliability smells. Use when the user asks "review my Forge app", "pre-deploy check", "is this app ready to ship", "review manifest", "general app review", "release readiness", or asks for a broad quality pass. Do not use for deep security audits/SAST/exploitability review, cost optimization, or diagnosing a known broken app; route those to forge-security-review, forge-cost-optimizer, or forge-debugger respectively.

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。

Build production-ready analytical SQL queries using CTEs, window functions, aggregations, and data transformations. Translates business questions into well-documented, performant queries. Use when writing reporting queries, building data pipelines, or answering ad-hoc business questions with SQL.

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。

Write structured business cases with problem framing, options analysis, financial modeling (NPV/ROI/Payback), risk assessment, and implementation roadmap. Follows HBR business case structure. Use when seeking budget approval, proposing new initiatives, or justifying investment decisions.

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。

Audit data quality across six DAMA DMBOK dimensions — Completeness, Accuracy, Consistency, Timeliness, Validity, Uniqueness. Produces a scored data quality report with issue log and remediation plan. Use when profiling datasets, onboarding new data sources, or building data quality gates.

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。

Business logic vulnerability playbook. Use when reasoning about workflows, race conditions, price manipulation, coupon abuse, state machines, and multi-step authorization gaps.

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。

Identify and exploit flaws in the core business rules and logic of web applications. This skill focuses on manipulation of application workflows, pricing, inventory limitations, and multi-step processes. Use this skill when bug hunting or testing e-commerce, banking, or SaaS platforms where standard technical vulnerabilities (e.g., XSS/SQLi) are not present but the application's logic is flawed.

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。

Audit and repair Gaia user-tree timelines so every skill's current rank is explained by a chain of Hero's Journey events. Trigger this skill when: a contributor's profile chart shows a stale rank (e.g. still shows 3★ after a demotion to 1★); someone asks to "trace what happened" to a skill's rank; `validate_timelines.py` is failing in CI; you need to backfill missing `demote` or `rank_up` events before cutting a release; a skill was reclassified or Star-Bar-reset and the user tree was never updated; or any time the registry node and user tree disagree on a skill's current level. Uses `scripts/trace_timeline.py` (dry-run + apply) and `scripts/validate_timelines.py` (CI gate). Do NOT hand-edit `timeline[]` arrays — the script synthesises properly labelled events with `previousValue`/`newValue` so the rank chart plots correctly.

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Establishes behavioral baselines from historical telemetry (process, network, or logon events) so hunts can flag rare and first-seen activity instead of relying on static signatures. Activates for requests to build a telemetry baseline, find rare or first-seen activity, or compute frequency baselines for anomaly hunting.

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The measurement surface for ALL Pulp DSP and audio-pipeline work — read it BEFORE writing or gating DSP, not only when something already sounds wrong. Covers the C++ harness (signal generators, metrics, assertions, RenderScenario, contracts), the offline Audio Doctor (magnitude/frequency response, THD/THD+N, phase/group delay), and their Python sibling the Audio Quality Lab (tools/audio/quality-lab — null residual + alignment, LTAS log-spectral distance, spectral flux/centroid, HNR, Theil-Sen drift slope, Kaiser-sinc resampling, license-guarded corpus, regression-net ratchet). TRIGGER on AUTHORING work — "build/design an oscillator/filter/synth/effect", "add a DSP module", "what should the acceptance gate be", "how do I measure aliasing / anti-aliasing / alias floor", "null against a reference", "is this DSP correct", "choose a tolerance", "golden/regression corpus for audio", "measure drift or jitter", "A/B two renders" — AND on DEBUGGING work — "is there sound / no audio / I hear nothing", "does this filter/compressor/synth/delay produce the right signal", "prove the DSP / prove the contract", "measure the frequency response", "what's the THD / is it distorting", "what's the group delay / phase response / measured latency", "magnitude response curve", "render a test tone and assert", "audio regression", "64-frame works but 128 is silent", "sample-rate change pitch-shifted it", "describe what's in this buffer", "audio doctor", "compare before/after a DSP refactor". Reach for this BEFORE hand-rolling any FFT, null test, alias measurement, pitch tracker, or golden-render script — most of it already exists in one of the two lanes. Test/tool layer over HeadlessHost — deterministic, no audio device, no speakers. Off the realtime thread entirely.

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。

Reviews audio DSP code for numerical correctness. Use when the user asks to review, audit, or check DSP code for correctness issues — filters, feedback loops, fixed-point arithmetic, accumulation loops, or any numeric computation in audio code. Trigger on "check this filter for NaN", "why does my reverb blow up?", "is this numerically stable?", "why do I hear DC in my output?". Pairs with audio-dsp-review (realtime safety); this skill covers numeric correctness. Flag issues proactively.

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 a task needs the judgment of a detective/criminal investigator — developing and stress-testing a case theory against alternate explanations, reconciling conflicting timestamps (DVR, cell tower, witness-reported) into a defensible timeline, structuring a suspect/witness interview using a non-accusatorial method, drafting a probable-cause affidavit, or evaluating whether a confession or eyewitness identification is being treated with the right level of certainty.

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 a task needs the judgment of a Business Continuity Planner — running a business impact analysis (BIA) to derive recovery time objectives, mapping process dependencies to find single points of failure, evaluating whether current recovery capability meets a process's maximum tolerable downtime, designing or debriefing a tabletop exercise, or building the cost-benefit case for a continuity investment.

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 a task needs the judgment of a Business Intelligence Analyst — reconciling a metric discrepancy between reports, designing a dimensional model or semantic layer for self-service dashboards, setting a data freshness/latency SLA, or diagnosing why a dashboard's aggregate number is misleading.

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 the user wants to transact on any Volcengine commercial product — purchase/provision it, ask what it costs, or unsubscribe from it. Covers three actions: buy (purchase, subscribe, provision, open, place an order; Chinese "开通 / 购买 / 下单 / 买 / 售卖 / 订购"), price (how much does it cost, quote, billing rules, estimated spend; Chinese "多少钱 / 价格 / 报价 / 费用 / 怎么收费 / 如何计费 / 预估费用"), and unsubscribe (cancel a subscription, refund a prepaid instance; Chinese "退订 / 退款 / 取消订阅"). Trigger even when the user does not mention "sale" but expresses a clear purchase, pricing, or cancellation intent toward a Volcengine product. Only handles these three transaction actions; anything outside them is out of scope.

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。

Language-agnostic writing style guidelines covering structure, tone, rhetorical constraints, banned phrases, and review checks. Use when writing any kind of content in any language. Including blog posts, notes, technical articles, technical writing, chitchat, social media posts, etc., even when you are just sending a text message. Also use when reviewing or editing existing content for tone and style compliance. For Traditional Chinese (zh-TW) content, apply `chinese-content-writing-guideline` alongside this skill.

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。

Implements concurrent Go patterns using goroutines and channels, designs and builds microservices with gRPC or REST, optimizes Go application performance with pprof, and enforces idiomatic Go with generics, interfaces, and robust error handling. Use when building Go applications requiring concurrent programming, microservices architecture, or high-performance systems. Invoke for goroutines, channels, Go generics, gRPC integration, CLI tools, benchmarks, or table-driven testing.

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。

Builds Flux manifests locally, diffs YAML files, patches FluxInstance upgrades, creates authentication secrets, traces GitOps delivery pipelines, and bootstraps clusters with the Flux Operator. Use this skill whenever the user mentions flux-operator, FluxInstance, FluxReport, ResourceSet, ResourceSetInputProvider, Flux CD operator management, or asks about GitOps CLI tooling for Kubernetes with Flux. Also trigger when users ask about building Flux manifests, diffing YAML, patching Flux instances, creating Flux secrets, tracing GitOps delivery pipelines, or bootstrapping clusters with Flux. Even if the user just says "flux operator" or "flux-operator cli" without details, this skill has the authoritative reference.

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。

Set up and operate a Shopify store with Shopify's official MCP server (the installed `shopify` command, NOT the npm Shopify CLI). Use whenever someone wants to sell online, start an online store or business, try Shopify, or manage a Shopify store — even if they do not say "Shopify": "I want to sell candles online", "help me start an online business", "open my first store", "add products", "check my orders", "set inventory", "make a discount", "show my sales", "how did my Shopify Email campaigns do". Before connection it can show relevant mock.shop sample catalogs, suggest business names, check domains, search shopify.dev, and validate GraphQL. The connect flow can create an account and store for a new user. Do the work here rather than only listing setup steps. Keywords: Shopify MCP, ecommerce, sample products, demo store, starter catalog, ShopifyQL, first store, create product.

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 an incident response program for playbook coverage, MTTR optimization opportunities, evidence collection readiness, root cause analysis quality, and post-incident review effectiveness. Triggers: building a SOC, assessing IR maturity, optimizing detection-to-recovery timelines.

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 fraud detection systems including rule engines, ML scoring models, real-time transaction monitoring, alert triage workflows, false positive management, SAR/CTR regulatory reporting, adversarial robustness testing, and adaptive retraining pipelines for payment fraud, account takeover, identity theft, and AML compliance.

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。

Audit legal case prediction systems for bias, fairness, accuracy, and ethical guardrails. Use when: 'check my prediction model for bias', 'audit case outcome fairness', 'evaluate legal ML model', 'review sentencing prediction ethics', 'analyze bail risk algorithm', 'fairness metrics for justice system AI'.

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。