「reliability」の検索結果

Expert Haskell engineer specializing in advanced type systems, pure functional design, and high-reliability software. Use PROACTIVELY for type-level programming, concurrency, and architecture guidance.

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。

Master error handling patterns across languages including exceptions, Result types, error propagation, and graceful degradation to build resilient applications. Use when implementing error handling, designing APIs, or improving application reliability.

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。

Expert SRE incident responder specializing in rapid problem resolution, modern observability, and comprehensive incident management. Masters incident command, blameless post-mortems, error budget management, and system reliability patterns. Handles critical outages, communication strategies, and continuous improvement. Use IMMEDIATELY for production incidents or SRE practices.

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。

You are a cloud cost optimization expert specializing in reducing infrastructure expenses while maintaining performance and reliability. Analyze cloud spending, identify savings opportunities, and implement cost-effective architectures across AWS, Azure, and GCP.

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。

Expert DevOps troubleshooter specializing in rapid incident response, advanced debugging, and modern observability. Masters log analysis, distributed tracing, Kubernetes debugging, performance optimization, and root cause analysis. Handles production outages, system reliability, and preventive monitoring. Use PROACTIVELY for debugging, incident response, or system 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。

Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability.

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。

Elite code review expert specializing in modern AI-powered code analysis, security vulnerabilities, performance optimization, and production reliability. Masters static analysis tools, security scanning, and configuration review with 2024/2025 best practices. Use PROACTIVELY for code quality assurance.

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。

Cut through the model soup to buy the right appliance — the features that actually matter for you, the ones that are marketing, and when to buy. Use when asked which [fridge/washer/dishwasher] should I buy, help me choose an appliance, what features do I need, or is this appliance worth it. Produces a needs-based feature shortlist (must-have vs nice-to-have vs marketing fluff), fit and capacity checks, reliability and running-cost considerations, warranty/extended-warranty guidance, and timing tips — flagging to verify current models, prices, and specs before buying.

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 only after explicit Codex `$superloopy:superloopy-backend` or Claude Code `/superloopy:superloopy-backend` invocation, a backend task beginning with leading `loopy` or `루피`, or an active Superloopy loop routes backend work here. Relevant work includes backend application boundaries, APIs, persistence, migrations, transactions, background jobs, caches, events, reliability, security, and runtime database-agent tools. Plain API, server, database, SQL, or agent vocabulary does not activate this workflow.

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。

Discovers, classifies, filters, and quantifies full-length transcript isoforms from PacBio Iso-Seq/Kinnex (HiFi) and Oxford Nanopore (cDNA/direct-RNA) long reads, using the isoseq+pigeon pipeline, SQANTI3, and ONT tools (IsoQuant, FLAIR, Bambu, StringTie2). Covers why a novel isoform is an artifact until proven otherwise (RT template-switching, intra-priming, and 5' degradation manufacture junctions and truncations), the SQANTI3 structural categories and their trust order, the Kinnex skera-split step, orthogonal CAGE/poly-A/short-read-junction validation, and why long-read isoform quantification needs EM. Use when building a full-length isoform catalog, classifying/filtering long-read transcripts, running Iso-Seq or ONT cDNA/dRNA analysis, or judging novel-isoform reliability.

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 AI agents with tracing, token metrics, latency, and cost visibility. Use for reliability and debugging.

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。

Perform a full codebase review, categorize findings by severity, file GitHub issues, then fix each issue in an isolated git worktree and submit PRs. Use this skill when the user asks to audit the codebase, do a comprehensive code review, find and fix security/quality/reliability issues, or run a proactive health check across the entire repository.

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。

Define and check simple service-level objectives for Claude Code from Agent Monitor data — session completion rate, tool success rate (PostToolUse/PreToolUse), and error rate (APIError/total) — then compare each to its target and report the error budget remaining. Use when reporting reliability or when someone asks "are we meeting our SLOs?".

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 and reduce infrastructure and tooling costs without sacrificing reliability or velocity. Use this skill when reviewing monthly cloud or SaaS spend, finding unused resources, rightsizing infrastructure, negotiating vendor contracts, deciding what to consolidate, or planning for budget cuts. Triggers on cost optimization, cloud spend, SaaS spend, rightsizing, unused resources, FinOps, infrastructure audit, vendor consolidation, budget cut, cost review. Also triggers when finance flags rising costs or when a contract renewal is up.

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。

Expert NIST AI Risk Management Framework (AI RMF 1.0) advisor covering all four functions: GOVERN, MAP, MEASURE, MANAGE. Use this skill whenever a user asks about NIST AI RMF, AI risk management, AI trustworthiness, GOVERN function, MAP function, MEASURE function, MANAGE function, AI RMF Playbook, AI risk profiles, responsible AI, AI bias management, AI transparency, AI explainability, AI reliability, AI safety, NIST AI 100-1, AI risk assessment, AI incident response, or alignment to EU AI Act, ISO 42001, or NIST CSF via AI RMF. Trigger even if the user doesn't say "skill" — any NIST AI RMF or AI governance risk question should use this 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。

Analyze prompts for clarity, structure, examples, and output reliability

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, review, and validate Google Cloud (GCP) architectures. Use when choosing GCP compute, storage, networking, or identity services, or applying the Google Cloud Architecture Framework (reliability, security, cost, 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。

Chaos engineering: hypothesis-driven fault injection to surface weakness before users do. Use when designing a chaos experiment, planning a gameday, choosing what to inject, computing blast radius, or building a chaos maturity model.

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 to "build a computer-use agent", "automate a GUI with an AI agent", "when to use computer use vs an API", "make browser automation reliable", or "design screenshot-driven agent actions".

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。

Expert NIST AI Risk Management Framework (AI RMF 1.0) advisor covering all four functions: GOVERN, MAP, MEASURE, MANAGE. Use this skill whenever a user asks about NIST AI RMF, AI risk management, AI trustworthiness, GOVERN function, MAP function, MEASURE function, MANAGE function, AI RMF Playbook, AI risk profiles, responsible AI, AI bias management, AI transparency, AI explainability, AI reliability, AI safety, NIST AI 100-1, AI risk assessment, AI incident response, or alignment to EU AI Act, ISO 42001, or NIST CSF via AI RMF. Trigger even if the user doesn't say "skill" — any NIST AI RMF or AI governance risk question should use this 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。

Build students' capacity to interrogate a historical source before reading — asking who authored it, when, why, and what this means for reliability. Use when students read documents without attending to authorship.

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 sequence where students compare AI's handling of the same question across disciplines, developing a mental model of where AI is reliable vs. distorting based on knowledge type.

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。

Comprehensive truth scoring, code quality verification, and automatic rollback system with 0.95 accuracy threshold for ensuring high-quality agent outputs and codebase reliability.

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。

Improve the agent itself, or audit its recent performance - better skills, prompts, workflows, and config, plus a quality/reliability/memory-hygiene review of what it did and what failed

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。