「casting」の検索結果

Draft episode outlines, show notes and guest preparation from supplied source material. Use for this Bot Generator Bot template; creates drafts or simulations within its declared 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。

Apply SAP HANA database standards for SQL parameterization, in-memory engine optimization, dynamic IN query chunking, column aliasing on joins, and datatype casting. Use when writing SQL for SAP HANA, optimizing HANA queries, or diagnosing HANA driver errors.

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。

Implement event-driven communication using BlueprintEventBus for cross-module coordination. Use this skill when modules need to communicate without tight coupling, broadcasting domain events (task.created, member.added), subscribing to events with proper lifecycle management, and implementing event-driven workflows. Ensures events follow naming conventions ([module].[action]), include Blueprint context, and use takeUntilDestroyed() for automatic cleanup.

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。

Volatility estimation, forecasting, and regime classification using GARCH, EWMA, realized volatility, and volatility cones

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。

Access USPTO patent data via PatentsView REST API and Google Patents Public Data (BigQuery). Search by inventor, assignee, CPC, or keywords; download metadata and claims; analyze portfolios; track tech trends. For IP landscape analysis, competitor monitoring, prior art search, and tech forecasting in life sciences and biotech.

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。

Repository operating skill for using Flow Forecast's flood_forecast package for time-series forecasting, data preparation, training, inference, evaluation, and hydrology-oriented advanced models.

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。

Route Auto-PyTorch tabular and forecasting workflows.

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。

Route AutoGluon repo tasks across tabular ML, time-series forecasting, multimodal AutoML, package setup, diagnostics, and saved predictor troubleshooting.

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 to stress-test predictions by assuming they failed and working backward to identify why. Invoke when confidence is high (>80% or <20%), need to identify tail risks and unknown unknowns, or want to widen overconfident intervals. Use when user mentions premortem, backcasting, what could go wrong, stress test, or black swans.

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。

Fix Monte Carlo project completion simulations that give identical dates for all sub-groups (milestones, priorities, epics). Use when: (1) All sub-group predictions converge to the same date despite different remaining counts, (2) Proportional throughput scaling produces flat/identical results, (3) Applying overall scope rate to individual sub-groups causes simulations to hit max_weeks cap and never converge, (4) Building project forecasting tools that predict completion for sub-groups of a larger backlog. Covers hypergeometric draw model for milestones and cumulative priority model for sequential priorities.

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。

Forecast inventory needs using demand analysis, safety stock calculations, reorder point optimization, and seasonal adjustment

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 about "WebSocket in Bun", "real-time communication", "Bun.serve websocket", "ws server", "socket connections", "pub/sub", "broadcasting messages", "WebSocket upgrade", or building real-time applications with Bun.

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 user provides one baseline image and requests game animation frames, sprite sequences, attack/walk/idle/hit/death/casting motion, transparent PNG frames, a spritesheet, keyframe prompts, or consistent whole-character pose animation.

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。

Meihua Yishu (梅花易數) Plum Blossom I Ching divination skill. Use when users request divination, fortune telling, hexagram casting, or character analysis (測字). Triggers on: 占卜, 算卦, 問卦, 起卦, 解卦, 測字, 拆字, meihua, plum blossom, I Ching divination, 梅花易數.

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。

Think and work like an expert Climatologist. Use when a task calls for Climatologist judgment. Characterizes climate via WMO CLINO baselines (1991–2020 vs 1961–1990), ETCCDI indices, and teleconnection modes; bridges ERA5 climatology to CMIP6/ScenarioMIP SSP deltas (xsdba/QDM), optimal-fingerprint attribution, AR6 ERF/ECS/TCR, and proxy reconstructions (CPS/EIV, PAGES2k, MXD divergence)—distinct from weather forecasting and generic physical-climate narration.

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。

Produce full-length audiobooks and long-form narration with generative voice tools. Use when the task is to turn a manuscript or long text into hours of spoken audio: preparing the manuscript for narration (front/back matter, footnotes, tables, dialogue), casting a single narrator or full cast, controlling pronunciation and voice consistency across a whole book, running a proofing/QC listen, meeting a retailer's technical delivery specs (RMS, peak, noise floor, room tone, chapterized files, metadata), and choosing a distribution route under each platform's current AI-narration policy. Not for short TTS clips, single-line voiceover, podcast production, or captioning existing video.

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。

Provider-independent localization and dubbing production direction for AI agents producing translated videos, dubbed ads, social clips, explainers, avatar videos, documentaries, training media, and multi-market campaigns. Use when planning or reviewing subtitle-versus-dub strategy, translation adaptation, glossary/style-guide setup, casting or voice matching, lip-sync or phrase-sync direction, synthetic dubbing boundaries, captions/subtitles handoff, accessibility, platform delivery, client review, or localization QA.

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。

Provider-independent production workflow for AI avatar spokesperson videos, including presenter briefs, consent and likeness rights, disclosure and platform labeling, casting, localization, performance direction, brand fit, claims review, lip-sync/audio QA, accessibility, approvals, and delivery packages. Use when creating synthetic presenter videos, corporate explainers, training modules, sales/support avatars, localization spokespeople, product announcements, compliance videos, or social avatar clips.

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。

Your most loyal users aren't just customers - they're potential advocates, contributors, and community members who can become the engine of your growth. Community-led growth isn't about broadcasting to an audience - it's about creating a space where your users connect with each other, share knowledge, and become invested in your success. This skill covers community strategy, platform selection, engagement tactics, ambassador programs, and the metrics that matter. Whether you're building a Discord for developers or a forum for power users, the goal is the same: creating genuine value that makes members want to stay and contribute. Use when "community, discord, forum, user group, ambassador, champions, developer relations, devrel, community engagement, community growth, power users, superfans, community, discord, engagement, devrel, ambassador, forum, user-community, growth, retention, champions" mentioned.

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 and backtests demand and revenue forecasts: rolling-origin backtests, prediction intervals, reconciliation, zero-shot foundation models. Use when forecasting time series.

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Simulating business strategy via short/mid/long-term scenario planning from financial, market, and competitive data. Applies SWOT/PESTLE/Porter, KPI forecasting, roadmaps. Does not write code.

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Casting personas: rapid generation from diverse inputs, registry-based persistence and lifecycle, data-driven evolution, inter-agent sync. Not for UI walkthroughs (Echo) or user research (Field).

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Industrial production of iron and steel from ore to finished product — blast furnace, Bessemer converter, open-hearth, basic oxygen, electric arc, continuous casting, rolling, and secondary metallurgy. Covers the chemistry of decarburization, dephosphorization, and desulfurization, the metallurgy of plain carbon steels, the evolution from puddling and rolling to BOF and EAF, and the process-structure-property chain that makes mild steel the default structural material of the industrial era.

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。

Assessing anthropogenic environmental impacts — pollution pathways, habitat destruction and fragmentation, land-use change, invasive species, overharvest, and extinction debt. Covers environmental impact assessment (EIA) methodology, exposure-effect relationships, population viability analysis, IPAT and ecological footprint frameworks, and strategic environmental assessment. Use when quantifying or forecasting human impacts on ecosystems, designing monitoring programs, or evaluating a proposed intervention against a baseline.

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。