495 件 ・ 関連度順
概要と使いどころ
Critical Akka.NET best practices including EventStream vs DistributedPubSub, supervision strategies, error handling, Props vs DependencyResolver, work distribution patterns, and cluster/local mode abstractions for testability.
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 stable, compatible public APIs using extend-only design principles. Manage API compatibility, wire compatibility, and versioning for NuGet packages and 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。
Model gene-family birth-death dynamics across a species tree using CAFE5 (Mendes et al 2020 Bioinformatics 36:5516 gamma-distributed rate categories), CAFE5-error (annotation-error-aware), Count (Csurös 2010 ancestral state reconstruction), BadiRate (Librado 2012 likelihood + parsimony), DupliPHY-Family, and ALE/AleRax (for per-family DTL; see [[gene-tree-species-tree-reconciliation]]). Test lineage-specific gene-family expansions and contractions, distinguish biological dynamics from annotation artifacts, account for assembly fragmentation, identify functional enrichment in expanded / contracted families. Use when correlating gene-family changes with phenotype evolution, ranking lineages by adaptive gene-family-rate shifts, post-WGD dosage-balance analysis, or building Birth-death models from OrthoFinder presence/absence matrices.
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。
Add OpenTelemetry tracing to MATLAB code. Use when the user asks to "add tracing", "instrument with spans", "add OpenTelemetry", "trace my code", "add observability" (when about tracing), or mentions "spans", "distributed tracing", or "OTel tracing" in the context of MATLAB functions. Covers span creation, parent-child context propagation, error handling, attributes, events, and semantic conventions.
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 infrastructure for fine-tuning LLMs with QLoRA, LoRA, and full fine-tuning using Hugging Face TRL, Axolotl, and distributed training with DeepSpeed or FSDP. Covers dataset prep, training runs, and model export.
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。
Configure New Relic observability platform for infrastructure and application monitoring. Set up APM agents, create dashboards, configure alerts, and implement distributed tracing. Use when implementing full-stack observability with New Relic One.
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 Datadog monitoring and APM for infrastructure and applications. Configure agents, create dashboards, set up alerts, and implement distributed tracing. Use when implementing enterprise monitoring, APM, or unified observability 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。
Instrument applications and infrastructure with OpenTelemetry for unified traces, metrics, and logs. Use when implementing distributed tracing, service-level troubleshooting, or vendor-neutral observability.
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。
Prepare, publish, resume, and verify private iOS release-testing installs with asc distribute. Use when distributing an IPA to registered devices outside TestFlight, reconciling ad hoc profiles, publishing through caller-owned S3-compatible storage, or diagnosing a resumable private distribution run.
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。
When the user wants to choose or execute third-party platform SEO (high-authority sites for rankings or backlinks). Also use when the user mentions "parasite SEO," "parasitic SEO," "barnacle SEO," "hosted content," "third-party publishing," "Medium SEO," "Reddit SEO," "GitHub parasite SEO," "LinkedIn Pulse SEO," "high-authority platforms," "distributed authority," "borrow domain authority," or "rank without own website." For GitHub-specific playbooks, use github. For Medium.com posts, use medium-posts. For Grokipedia, use grokipedia-recommendations. For AI answer-engine visibility (not platform selection), use generative-engine-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。
Parallel and distributed computing for large-scale data processing
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。
Designs distributed system architectures, decomposes monoliths into bounded-context services, recommends communication patterns, and produces resilience 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。
Writes Spark jobs, debugs performance issues, configures cluster settings for distributed data processing, DataFrame transformations, and structured streaming analytics.
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。
Configures monitoring systems, implements structured logging, creates Prometheus/Grafana dashboards, defines alerting rules, instruments distributed tracing, and performs load 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。
Designs chaos experiments, creates failure injection frameworks, and facilitates game day exercises for distributed systems with runbooks, experiment manifests, and rollback procedures.
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。
System architecture design and review. Use when designing architecture, evaluating microservices vs monolith, writing ADRs, choosing a database, planning for scalability, reviewing system design, or generating architecture diagrams.
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 an AI agent skill before installing it. Use proactively whenever the user is about to add, install, enable, or evaluate an unfamiliar skill — including phrases like "audit this skill", "is this skill safe", "scan skill before install", "check skill for malicious code", "review this plugin", or any pre-install gate on an untrusted, third-party, or community-distributed skill bundle. Runs ten categories of static checks (code execution, network exfiltration, credential harvesting, persistence, prompt injection, supply-chain hooks, obfuscation, destructive filesystem ops, secrets, Trojan Source / homoglyphs) and emits a PASS / WARN / FAIL verdict.
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。
Adversarial verification of AI-generated legal content with systematic fact-checking, source validation, and quality control. Use when a user asks to verify, fact-check, red-team, validate sources, or quality-control a legal document, briefing, compliance summary, or regulatory analysis before it is distributed to clients, stakeholders, or published. Trigger phrases include "verify", "fact-check", "red team", "red-flag", "check accuracy", "validate sources", "quality control", "is this correct", and "review for errors". Produces a structured verification report with severity-categorized errors, verified sources, unsupported claim list, missing disclaimers, and an explicit distribution-readiness assessment.
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 a spaced retrieval schedule for any topic list and timeline. Use when planning units, term sequences, or revision programmes.
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, build, test, and distribute external Temps plugins with TypeScript/Bun; provide development and local-testing guidance for existing Rust plugins. Use for creating a plugin, adding an embedded console UI or deployment-event automation, testing host permissions and AI, installing from GitHub, or submitting a plugin to the public catalog. End-to-end publishing covers TypeScript GitHub-source plugins, not Rust native-package releases or in-process TempsPlugin backend crates.
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。
Orchestrate multi-agent swarms with agentic-flow for parallel task execution, dynamic topology, and intelligent coordination. Use when scaling beyond single agents, implementing complex workflows, or building distributed AI 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。
The MsDataHub package uses the ExperimentHub infrastructure to distribute raw mass spectrometry data files, peptide spectrum matches or quantitative data from proteomics and metabolomics experiments.
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。
Two-phase contributor rewards - plan builds a tier-priced payout from the repo's merged-PR ranking; send executes it on-chain via Bankr Wallet API with per-recipient idempotency and dry-run.
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。
Advanced distributed patterns - event sourcing, CQRS, saga, stream processing, append-only log, exactly-once delivery, sequencer, double-entry ledger, erasure coding, order book, watermarks
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。