「tempo」の検索結果

Produce a weekly digest of what Ahmad saved in OpenMark over a given time window, clustered by topic. Use whenever Ahmad asks "what did I save this week", "what's new in OpenMark", "last week's saves", "this week's bookmarks", "what happened in my feed", "weekly recap", or any temporal summary of saved content. Faster than full newsletter — output is a scannable digest, not a published post. Triggers on "this week", "last week", "what's new", "weekly digest", "recent saves", "recap".

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 the threat landscape using MISP (Malware Information Sharing Platform) by querying event statistics, attribute distributions, threat actor galaxy clusters, and tag trends over time. Uses PyMISP to pull event data, compute IOC type breakdowns, identify top threat actors and malware families, and generate threat landscape reports with temporal trends.

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。

Explicit-only workflow that writes a redacted continuation handoff to a unique temporary Markdown file. Use only when the user explicitly asks for a handoff for another session.

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 an entity-relationship link-analysis graph of an investigation — nodes, typed edges carrying source and confidence, aliases, and temporal validity — to expose shared infrastructure, bridging nodes and the real principal behind a [человек]. Use when an investigation has outgrown a list and needs a graph, or when asked how a set of people, companies and domains connect

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 durable workflows with Temporal for distributed systems. Covers workflow vs activity separation, saga patterns, state management, and determinism constraints. Use when building long-running processes, distributed transactions, or microservice orchestration.

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 distributed tracing with Jaeger and Tempo to track requests across microservices and identify performance bottlenecks. Use when debugging microservices, analyzing request flows, or implementing observability for distributed systems.

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。

Anomaly Detection Specialist IA — Expert en détection d'anomalies (statistique, ML, séries temporelles, alerting temps réel)

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。

Diagnose, fix, or validate SwiftUI animation and scroll behavior by temporarily instrumenting views with MotionEyes, capturing console traces over time, and comparing motion data to user intent or test expectations. Use when users report bugs such as wrong direction, premature fade, timing or easing mismatch, unexpected movement, missing movement, incorrect relative movement between views, scroll jumps, scroll restoration drift, content-offset desynchronization, or when they want to turn MotionEyes traces into focused assertions. Prefer XcodeBuildMCP log capture first and fallback to CLI log streaming; remove only agent-added MotionEyes instrumentation after validation.

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 how to "use VibeTags", "add VibeTags annotations", "set up AI guardrails", "protect code from AI", "configure AI platforms", asks about @AILocked, @AIContext, @AIDraft, @AIAudit, @AIIgnore, @AIPrivacy, @AICore, @AIPerformance, @AIContract, @AITestDriven, @AIThreadSafe, @AIImmutable, @AIDeprecated, @AIObservability, @AIRegulation, @AIArchitecture, @AILegacyBridge, @AIStrictClasspath, @AIInternationalized, @AIPublicAPI, @AISchemaSafe, @AIStrictExceptions, @AIStrictTypes, @AIParallelTests, @AIIdempotent, @AIFeatureFlag, @AISecure, @AICallersOnly, @AISandboxOnly, @AIMemoryBudget, @AIPure, @AIDomainModel, @AIExtensible, @AIInputSanitized, @AISecureLogging, @AIExplain, @AIPrototype, @AISunset, @AITemporary annotations, or wants to control how AI tools interact with Java code.

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。

Workflow automation is the infrastructure that makes AI agents reliable. Without durable execution, a network hiccup during a 10-step payment flow means lost money and angry customers. With it, workflows resume exactly where they left off. This skill covers the platforms (n8n, Temporal, Inngest) and patterns (sequential, parallel, orchestrator-worker) that turn brittle scripts into production-grade automation. Key insight: The platforms make different tradeoffs. n8n optimizes for accessibility

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, maintain, and run conformance tests for canonical App-in-Skill projects created by kelly-app-skill-creator. Use when a Kelly app skill needs contract checks, local server smoke tests, responsive browser acceptance, temporary open-source Busabase integration, environment-gated Busabase Cloud OAuth verification, lazy provisioning checks, persistence tests, AirApp parity tests, or CI coverage. Keep app creation in kelly-app-skill-creator and own only the reusable test harness, per-skill fixtures, execution gates, diagnostics, and truthful pass/fail/skip reporting.

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 the threat landscape using MISP (Malware Information Sharing Platform) by querying event statistics, attribute distributions, threat actor galaxy clusters, and tag trends over time. Uses PyMISP to pull event data, compute IOC type breakdowns, identify top threat actors and malware families, and generate threat landscape reports with temporal trends.

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。

Debug the signals pipeline locally end-to-end. Covers emitting test signals from fixtures, monitoring Temporal workflows via the REST API, reading sandbox agent logs from object storage, inspecting Docker sandbox containers, and diagnosing common failures (stale ClickHouse embeddings, agentsh network denials, inactivity timeouts). Use when a signal isn't reaching the inbox, a signal-report-summary workflow fails, or a sandbox task run times out.

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。

Patterns for loading PDE simulation datasets (PDEBench, PhiFlow, JAX-CFD) from HDF5 files. Handles layout detection (single tensor vs separate variables), spatial/temporal downsampling, multi-variable systems, HuggingFace and DaRUS data sources, and efficient PyTorch DataLoader creation. Use when preparing PDE data for neural operator training.

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。

Validates predictive models on omics and biomedical data with nested cross-validation, group/batch/temporal-aware splits, the full data-leakage taxonomy, probability calibration, decision-curve net benefit, optimism correction, sample-size planning, and TRIPOD+AI reporting. Use when estimating model performance honestly, choosing a CV scheme, detecting leakage, or judging whether reported discrimination means the model is actually useful. For feature selection itself see machine-learning/biomarker-discovery; for confirmatory-trial inference see clinical-biostatistics/trial-reporting.

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 for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

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。

Push browser tab and download lifecycle events plus manager-owned background tabs. Read this when an agent needs lifecycle notifications or a temporary tab without polling.

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。

Estimates time-scaled phylogenies, molecular clock rates, effective reproduction number R_e (or R_t), and population dynamics from dated pathogen genomes using TreeTime (maximum-likelihood) and BEAST2 (Bayesian; strict / uncorrelated lognormal / ORC clocks; constant / exponential / Bayesian Skyline / Skygrid / BICEPS / Birth-Death-Skyline / sampled-ancestor BDSKY priors; structured coalescent via MASCOT). Covers root-to-tip clock signal QC via TempEst, date-randomisation tests (Ramsden 2009; Duchêne 2015), recombination masking via Gubbins and ClonalFrameML before clock inference for recombining bacteria, BDSKY origin-vs-rootHeight pitfalls, sampling-bias correction (Volz & Frost 2014; preferential-sampling extensions), MASCOT structured coalescent for migration, BICEPS-vs-BSP skyline choice, multi-chain BEAST2 convergence diagnostics, and reconciliation between phylodynamic R_e and case-based R_t. Use when dating outbreak origins, estimating substitution rates, inferring R_e through time, building time-calibrated Nextstrain Augur trees, choosing between strict and relaxed clocks, fitting Birth-Death-Skyline (Stadler 2013) models, diagnosing temporal-signal failure, masking recombination before clock inference for *Streptococcus pneumoniae* / *Neisseria gonorrhoeae* / *Klebsiella* / *E. coli* phylodynamics, running MASCOT for structured-population analyses, or using UShER for pandemic-scale placement.

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 time-series and longitudinal RNA-seq for differential expression and trajectory structure. Covers DESeq2 LRT with reduced models, time as factor vs continuous vs natural splines, maSigPro (Nueda 2014 for RNA-seq), ImpulseDE2 with explicit impulse-model failure modes, DREAM for repeated measures via linear mixed models, pseudoreplication avoidance, conditional vs marginal modeling, and trajectory clustering with DPGP, Mfuzz (with Schwämmle 2010 fuzzifier estimation), and splines+k-means. Use when modeling time-course or longitudinal expression, choosing factor vs spline, handling repeated measures from the same subject, avoiding pseudoreplication, clustering temporal trajectories, or selecting between dedicated time-course tools and pairwise+LRT.

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 durable workflows with Temporal for distributed systems. Covers workflow vs activity separation, saga patterns, state management, and determinism constraints. Use when building long-running processes, distributed transactions, or microservice orchestration.

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。

Contemporary editorial style with serif typography, minimal palettes, and clean layouts for polished digital products.

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。

Current-era minimalist design with bento grids, dark mode support, and high-performance accessible layouts.

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。

Escreve roteiros de vídeo e VSL — gancho, abertura, desenvolvimento, prova, CTA e fechamento, com marcação de tempo e indicação visual. Use quando o usuário disser "roteiro", "VSL", "script de vídeo", "vídeo de vendas", "reels", "roteiro para YouTube".

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。

Métricas, logs, traces, dashboards, alertas e SLO — Prometheus, Grafana, Loki, Tempo, Mimir, Zabbix, Alertmanager. Use quando o usuário disser "dashboard", "alerta", "métrica", "PromQL", "Grafana", "SLO", "golden signals", "não temos visibilidade", "monitoramento".

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。