「ttl」の検索結果

Strengthen test coverage around changed behavior, edge cases, and regressions without writing noisy or brittle tests.

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。

Serverless Redis-compatible key-value store via Upstash REST API -- edge-compatible, automatic JSON serialization, TTL-based caching

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 a stylized 3D animated ad on the Novoads REST API as a STORYBOARD: four to six beats, each rendered from its own key frame, narration laid into the gaps, a music bed, captions burned on, assembled locally with ffmpeg. Carries the genre's beat formulas (anthropomorphized problem character, protagonist reveal, mascot mechanism scene, composited end card). Use for ANY Pixar-style, Disney-style, 3D animated or animated-film-look ad at ANY length: "Pixar style ad", "3D animated ad", "animated movie look", "cute 3D character ad", "a quick animated ad", "15 second animated ad", "30 second animated ad", "storyboard ad", "several scenes", "animated ad with a voice-over", "little mascots", or a product URL or photo for an emotional animated spot. No one-call tier, so ffmpeg is required. Not for stop-motion clay (use claymation-ad), talking-head UGC (novoads-api), video cloning, or static image ads.

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 esbuild for extreme-speed builds: bundling, transforming, plugins, and integration into larger pipelines. Use when build speed is the bottleneck.

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。

Manage ML datasets with the datasets ecosystem — loading, streaming, preprocessing, versioning, and sharing. Use when data handling (not modeling) is the bottleneck.

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。

Partner, channel, and ecosystem marketing for B2B SaaS — and the design of the partner program itself. Use this skill for co-marketing with technology alliances and channel partners, integration launches, joint campaigns and content, partner tiering and enablement, cloud-marketplace go-to-market (AWS, Azure, GCP listings, private offers, committed-spend drawdown), and for channel program architecture: whether to launch a partner program at all, which motions to run (referral, technology, services/SI, reseller), partner unit economics, margin and SPIF design, partner recruitment and certification ramp, and the rules of engagement that settle a contested deal. Also triggers on: partner marketing, co-marketing, channel, reseller, VAR, MSP, distributor, OEM, alliance, ecosystem, integration launch, marketplace listing, AWS Marketplace, private offer, MDF, joint webinar, co-branded, channel conflict, rules of engagement, deal registration, partner program design, should we build a partner program, partner economics, partner P&L, partner CAC, margin stacking, SPIF, rebate, partner recruitment, partner onboarding, partner certification, partner tiers, our partners signed up and never sold anything.

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。

Raise real concurrency in asyncio LLM batch scorers built on the OpenAI SDK (AsyncOpenAI, including OpenAI-compatible providers like DeepSeek). Use when: (1) raising an asyncio.Semaphore above ~100 produces no throughput gain, (2) a batch pipeline saturates near 100 in-flight requests despite a larger semaphore, (3) planning a high-concurrency campaign against a provider with no hard rate limit (DeepSeek v4-flash tolerates 2000+ in flight). Root cause: AsyncOpenAI's default httpx pool caps max_connections at 100, silently bottlenecking any larger semaphore — you must pass a custom http_client with httpx.Limits sized to the semaphore.

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。

Structured performance profiling workflow. Identifies bottlenecks, measures against budgets, and generates optimization recommendations with priority rankings.

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。

Unity 6 performance profiling and optimization guide. Use when profiling, optimizing frame rate, reducing memory usage, debugging performance bottlenecks, or using the Profiler, Memory Profiler, or Frame Debugger. Based on Unity 6.3 LTS documentation.

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Use when a task needs the judgment of a Credit Counselor — calculating debt-to-income ratio and disposable income to test debt management plan (DMP) feasibility, comparing DMP versus debt settlement versus bankruptcy for a specific client's financial situation, negotiating and applying creditor interest-rate concessions, selecting a debt payoff order matched to a client's follow-through pattern, or providing federally required pre-bankruptcy credit counseling.

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 Conveyor Operator — diagnosing a mistracking belt before it becomes edge damage or spillage, verifying every lockout/tagout isolation point on a conveyor with a gravity take-up or incline before entering the nip zone, calculating each conveyor's actual delivered capacity in a multi-conveyor line to find the true bottleneck, deciding whether a mechanical or vulcanized splice fits a given tension rating, or judging whether a pinch-point guarding gap needs an immediate stop versus a scheduled fix.

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 Continuous Mining Machine Operator in underground coal — reading a methane monitor trend and deciding whether to de-energize, judging whether cumulative unbolted advance has hit the roof control plan's cut-depth limit, deciding whether a permissibility defect requires immediate shutdown in a gassy heading, diagnosing whether a shift's production shortfall traces to cutting rate or shuttle-car haulage, or deciding whether a "drummy" roof sound stops the 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 a task needs the judgment of a brokerage clerk — resolving a trade-break/reconciliation exception before settlement, recomputing a weighted-average execution price across partial fills, processing a corporate action's effect on share count and cost basis, or responding to a DK (don't-know) notice from a contra broker.

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 manufacturing production optimization systems for OEE (Overall Equipment Effectiveness) calculation accuracy, Six Big Losses categorization, job shop and flow shop scheduling algorithms (LP, MIP, constraint programming, genetic algorithm), Theory of Constraints bottleneck detection.

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 litigation analytics and case outcome prediction systems -- ML outcome models (logistic regression, gradient boosting, neural nets with temporal train/test splits), settlement range modeling (Monte Carlo simulation, comparable case matching, BATNA analysis).

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 game code for performance bottlenecks including draw call batching and overdraw, shader complexity and LOD strategy, per-frame GC allocation pressure, object pooling gaps, physics timestep and collision matrix tuning, spatial partitioning for entity queries.

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 game monetization implementations including IAP purchase flow and server-side receipt validation, ad mediation waterfall and rewarded video placement, subscription lifecycle and grace period handling, battle pass XP progression.

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 insurance claims workflow systems for intake optimization, adjudication rules, subrogation, litigation management, and settlement analytics. Triggers: 'audit claims process', 'review FNOL workflow', 'evaluate claims automation', 'check subrogation recovery'.

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Diagnose ROS 2 Jazzy faults that reading the code cannot settle: QoS mismatch, sensor-mount and frame errors (REP 103/105), and odometry calibrated against CAD instead of the floor. Ships four runnable pass/fail checks.

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Surface and visualize hidden dependencies blocking delivery. Use when the user says "map our dependencies", "what's blocking what", "dependency audit", "why are teams blocked", "we have a bottleneck", "draw out the dependencies", or asks to identify cross-team blockers - even if they don't explicitly say "dependency mapping". Based on "Making Work Visible" by Dominica DeGrandis (time theft, invisible work, flow blockers).

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Analyze flow metrics to identify delivery bottlenecks and improve throughput. Use when the user says "flow metrics", "why is delivery slow", "cycle time analysis", "WIP analysis", "throughput review", "what's our lead time", "DORA metrics", "where is work getting stuck", "Kanban metrics", or wants to diagnose delivery performance using data - even if they don't explicitly say "flow metrics review". Based on "Making Work Visible" by Dominica DeGrandis (WIP limits, flow blockers, time thieves) and "Accelerate" by Forsgren, Humble & Kim (DORA metrics, delivery performance indicators).

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。

Plan and build 2D game maps and scenes - top-down and side-scrolling levels, tilemaps, prop packs, parallax backgrounds and HD-2D battle or story plates - with collision, navigation and reachability checks, environment motion, a playable HTML preview and export to Tiled, Godot 4 or LDtk. Map art (terrain, tiles, props, plates, parallax layers) is image generation by default, through generate2dmedia route_media.py (the API when a key is configured, else the local Codex or Grok CLI); map data (layout, collision, exits, spawns) is written as data. Use for any map, level, room, scene, background, terrain or prop-kit request. Not for characters, creatures, items or FX (generate2dsprite), character animation (video2dsprite), code-drawn tiles or maps (codeart2d, only on request) or calling an image or video API by itself (generate2dmedia).

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Provides data-retrieval best practices, tool selection guidance, and performant SQL query syntax for BigQuery telemetry across INFORMATION_SCHEMA, Cloud Monitoring, and the REST API. Use when the telemetry to fetch is already known, selecting telemetry tools, writing performant INFORMATION_SCHEMA queries, retrieving telemetry for diagnosing single-job performance bottlenecks, investigating slot contention, job concurrency and queue latency, analyzing reservation capacity, utilization and autoscaling saturation, or auditing capacity-based and on-demand compute and storage resource billable usage. Don't use for root-cause diagnosis or symptom troubleshooting when the cause is unknown (use bigquery-troubleshooting first), or for writing or optimizing business logic SQL (use bigquery-optimization).

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。

Analyzes Google Cloud BigQuery slot consumption, query costs, and execution bottlenecks using INFORMATION_SCHEMA. Use when diagnosing slow BigQuery queries, slot starvation, high on-demand query costs, unpartitioned table scans, or join performance issues. Don't use for generic BigQuery administration (use bigquery-basics), BigQuery ML (use bigquery-ai-ml), or DataFrame operations (use bigquery-bigframes).

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。