「time」の検索結果

JLCPCB PCB fabrication and assembly — BOM/CPL generation, basic vs extended parts, assembly constraints, design rules, ordering workflow. Use with KiCad for JLCPCB manufacturing. Use this skill when the user mentions JLCPCB, wants to order PCBs or assembled boards, needs prototype bare PCBs and stencils, wants to know JLCPCB design rules and capabilities, or is asking about PCB manufacturing costs or turnaround times. For gerber/CPL export, stencil ordering, and BOM management, see the `bom` 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。

How to use the coding agents installed on this machine (claude, codex, droid, grok, vktr, hermes, opencode, copilot, pi) as plain sub-agents in orchestrated workflows: one headless invocation, one prompt in, one result out. Deliberately ignores each CLI's own orchestration features (droid --mission, codex multi_agent, hermes delegation/moa/kanban, grok --agents, opencode personas). The orchestrator is whatever session is running the skill; the CLIs are interchangeable workers. Use when the user asks "which agent should run this", "fan out workers", "run X headless", or when a lifeos skill (overnight, timeboxed-iterating, workgraph, gauntlet, bughunt, dark-factory) needs to launch an agent CLI, or when a worker needs company context it should ask Viktor for (vktr).

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。

Observes runtime behavior of binaries via QEMU emulation, GDB debugging, and Frida hooking — syscall tracing, breakpoints, memory inspection, and function interception. Use when running, tracing, or debugging a binary, verifying hypotheses from static analysis, or watching actual syscalls, network calls, or file access; requires human approval before any execution.

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 a fully customized Amazon Connect AI agent PoC bundle (Lambda handlers, OpenAPI spec, AI agent prompt, Contact Flows, CloudFormation/CDK infrastructure, FAQ knowledge base) from a ~15-minute structured interview — or from a requirements doc, a flow sketch, or an existing flow/prompt you want to improve. Use when the user wants to build, scaffold, prototype, or refine an Amazon Connect contact center, an AI voice/chat agent on Amazon Connect, or any contact-center use case that mentions Connect, Lex, Wisdom, Q in Connect, "AI agent domain", or Agentic CX Designer (ACXD, Connect Customer, the Agentic CX block — the ACXD runtime target). Also triggers on "I have a requirements document", "turn this flow sketch into a Contact Flow", or "improve my existing Contact Flow / AI prompt". Produces 6 asset packages on local disk.

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。

Publishes structured, actionable artifacts from Claude Code sessions — PR walkthroughs, incident timelines, system explainers, release checklists, and security audits.

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。

Multi-carrier shipping rate comparison and label-generation pipeline — real-time quotes from USPS, UPS, FedEx, DHL via direct carrier APIs OR aggregators (Shippo / EasyPost / ShipStation / Pirate Ship)..

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。

Evaluate edtech tools for their real impact on teacher time and administrative burden.

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。

Evaluate survey data pipelines and research methodology. Analyzes response bias detection (speeders, straight-liners, bots), statistical significance testing (t-test, chi-square, ANOVA with Bonferroni correction), sentiment and theme extraction from open-ended responses.

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 airline, airport, cruise, and tour operator systems for ground handling efficiency, aircraft turnaround optimization, disruption management, and GDS integration. Covers IATA/ICAO compliance, IROPS recovery, on-time performance tracking, embarkation logistics, transfer coordination, and NDC implementation for travel technology platforms.

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 vehicle routing and fleet optimization systems for route planning algorithms, multi-stop sequencing, time window constraints, and last-mile delivery performance.

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 predictive maintenance systems for OPC-UA/MQTT sensor data pipelines, time-series storage retention, ML model lifecycle (training-serving skew, drift detection, retraining triggers), MTBF/MTTF/MTTR reliability calculations, Weibull survival 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。

Generate a production-grade Vehicle Routing Problem (VRP) solver using Google OR-Tools — supports CVRP (capacity), VRPTW (time windows), VRPPD (pickup + delivery), multi-depot, multi-trip, heterogeneous fleet, driver hours-of-service (HOS) 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 academic peer review operations -- reviewer matching algorithms, conflict of interest detection, turnaround time optimization, review quality scoring, and editorial workflow management..

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。

Audio file → full publishing kit pipeline. From a single .mp3 or .wav per episode, generates Whisper-large-v3 transcript with word-level timestamps, automatic chapter detection (audio scene change + topic shift), two show-notes lengths (scannable summary + long-form SEO).

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 material forecasting and requirements planning systems including MRP/MRP II logic evaluation, BOM explosion accuracy, supplier lead time variability tracking, seasonal demand pattern detection, fabric yield and marker efficiency optimization, MOQ aggregation strategies.

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。

Computes development quality and velocity metrics from git history and tracks improvement over time. Measures fix ratios, rework hotspots, test coverage, DORA metrics, and code churn.

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 MRO parts inventory systems for field service optimization -- truck stock composition, first-time fix rate improvement, reorder point calculation, safety stock sizing, obsolescence detection, and demand forecasting..

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。

Profile and audit mobile app performance -- cold/warm/hot startup time, memory leaks, battery drain, network efficiency, frame rate jank, and binary size. Triggers: diagnosing slow launches, investigating ANRs, reducing app size, or benchmarking rendering performance.

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 demand forecasting systems — time-series decomposition (ARIMA, Prophet, ETS), seasonal and event-driven demand modeling, booking curve and pickup analysis, cancellation prediction, forecast accuracy metrics (MAPE, bias), and model retraining pipelines.

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 donor retention and CRM systems — lapse risk scoring and prediction models, stewardship workflow automation, donor lifetime value (LTV) modeling, communication personalization engines, giving pattern analysis, and retention metric benchmarking.

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 fleet maintenance programs for preventive maintenance scheduling effectiveness, parts inventory forecasting, vehicle downtime minimization, total cost of ownership modeling, and telematics integration.

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。