「reliability」の検索結果

GStack developer-experience review for implemented repositories and workflows, focused on setup friction, command reliability, docs gaps, and maintainability.

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。

Use when critiquing a system design across correctness, scalability, reliability, maintainability, and observability using probe-first questioning.

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。

Mine a codebase for breakthrough, evidence-backed opportunities — features, additions, refactors, simplifications, DX, UX, reliability, performance, or architecture cleanup. Researches project reality first, then forces two escalation gates (reject the merely-adequate idea; expand ambition 10x then collapse to the smallest proof-bearing artifact). Outputs ranked opportunities, an escalation ledger, and one plan seed. Does not implement or create tickets.

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。

Knowledge base from the NIST Framework for Cyber-Physical Systems (SP 1500-201/202/203, v1.0, 2017). Use for engineering and analyzing cyber-physical systems (CPS) and IoT: the CPS Framework's aspect × facet grid (nine aspects of concern examined through conceptualization, realization, and assurance facets), cross-property trustworthiness risk management (safety/security/privacy/reliability/resilience together), data interoperability (syntactical/semantic/contextual, canonical models, identifiers, provenance), the timing aspect (time-interval classes, time domains, time-aware networking) and secure/resilient time (GNSS jamming/spoofing, PTP/NTP attacks, holdover, the clock-error model and Allan/MTIE metrics), plus the use-case requirements method. Bridges IT and OT under a clock. Scope limits: a 2017 living/draft document, thin on post-2017 standards and on detailed control catalogs; it names but does not reproduce ISO/IEC/IEEE 42010 and 15288 process text or permission-licensed third-party figures.

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。

Knowledge base from the NASA Schedule Management Handbook (2024 edition, Revision 2). Use for program/project schedule management of aerospace and large engineering efforts: the five sub-functions (Planning, Development, Assessment & Analysis, Maintenance & Control, Documentation & Communication); the Integrated Master Schedule (IMS) and the WBS/OBS/CBS breakdown structures; the Schedule Management Plan (SMP) and Basis of Estimate (BoE); Critical Path Method, logic, durations, float and margin; tiered schedule assessment (Requirements/Health/Basis checks, the Shock Test) and the Dimensions of Schedule Reliability; Schedule Risk Analysis (SRA), Integrated Cost-Schedule Risk Analysis (ICSRA), Monte Carlo, correlation, merge bias, and the Joint Confidence Level (JCL); baselining, replan vs. rebaseline, EVM schedule metrics (SPI, BEI, CEI, HMI, Earned Schedule, CPLI), and status/progress/forecast reporting. This is NASA's scheduling doctrine grounded in NPR 7120.5 and the GAO Schedule Assessment Guide. Thin on the underlying mechanics of CPM, statistics, and EVM (it points to companion handbooks); aerospace/space-flight-centric; anchored to the 2024 second edition.

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。

Knowledge base from the DoD Test & Evaluation Enterprise Guidebook (August 2022 edition 8.02) with DOT&E-facing enterprise emphasis. Use for T&E enterprise roles (DOT&E vs USD(R&E)), DT&E/OT&E/LFT&E, cybersecurity T&E, MOSA/automation/digital enablers, suitability/reliability growth, and TEMP/Strategy/STE planning. Distinct routing from dod-te-guidebook (same guidebook family, pathway-deep pack). Guidance only—defer to DoDI 5000.89 and Title 10 for binding requirements.

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。

Knowledge base from the NASA Probabilistic Risk Assessment Procedures Guide (NASA/SP-2011-3421, 2nd ed.). Use for quantitative PRA of aerospace and safety-critical systems: the risk triplet and scenario logic stack (MLD, ESD, event trees, fault trees, minimal cut sets), Bayesian data collection and parameter estimation, aleatory/epistemic uncertainty modeling and common-cause failure (Alpha Factor, beta-factor, CCBE), human reliability analysis (THERP, CREAM, NARA, SPAR-H), context-based software risk (CSRM), physics-based and structural/phenomenological models (stress-strength, limit states, FORM/SORM, NASGRO, range safety), uncertainty propagation and importance measures (F-V, RAW, Birnbaum, DIM), and launch-abort modeling with worked PRA examples. This is the QUANTITATIVE engine beneath NASA's risk doctrine — for the qualitative RIDM/CRM decision framework use the nasa-risk pack. Thin on programme management, organisational risk governance, and non-aerospace regulatory contexts; aerospace-focused and anchored to the 2011 second edition.

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。

Knowledge base from the GAO Schedule Assessment Guide (GAO-16-89G, 2015). Use for building, assessing, or auditing a program's integrated master schedule (IMS): the ten best practices of a reliable schedule (capture, sequence, resource, duration, traceability, valid critical path, reasonable total float, schedule risk analysis, updating, baseline), the four characteristics (comprehensive, well-constructed, credible, controlled), critical path method arithmetic (forward/backward pass, total float), schedule risk analysis (Monte Carlo, contingency), statusing and baseline control, and the Appendix II audit triad. Scheduling/time-management oriented: it is the authority on schedule reliability, but it is NOT a cost-estimating guide, a full SE process model, an agile guide, or a risk-management framework — see sibling packs (gao-cost, dau-se-guidebook, nasa-schedule, nasa-risk) for those.

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。

Knowledge base from the FAA Human Factors Design Standard (HF-STD-001B, 2016) — the FAA's consolidated human-factors / human-systems-integration design criteria for systems it manages, operates, or maintains. Use for requirement-level design rules on: general human-factors design principles; automation and function allocation; designing equipment for maintenance; displays, controls, and visual indicators; alarms, audio, and voice communications; the computer-human interface (information presentation, coding, interaction styles, windows, dialogue); keyboards, input devices, and workstation/workplace ergonomics; system security, personnel safety, environment, anthropometry, and user documentation; plus intended-use and tailoring guidance. It is a quantified, source-attributed synthesis (heavily citing MIL-STD-1472G, MIL-HDBK-759C, DOD-HFDG-ATCCS, NUREG-0700, NASA-STD-3000, ANSI, OSHA, ISO 9241). Complements nasa-hsi as a parallel HSI tradition from the civil-aviation domain. Scope limits: it is design criteria, NOT an HSI lifecycle/process model (use nasa-hsi, nasa-npr-7123, or dau-se-guidebook for process); it does NOT reproduce the third-party standards it cites; it is FAA-acquisition-oriented (ATC, towers, TRACONs, airway facilities), not a general consumer-UX guide; and it is thin on modern touchscreen/mobile, web, and AI-driven interface patterns and on quantitative human-reliability 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。

Evidence-driven application audit protocol for security, authorization, privacy, concurrency, state integrity, reliability, performance, accessibility, visual consistency, responsive behavior, and release readiness. Use for incremental checks, pre-merge review, pre-launch audits, high-stakes deep audits, AI-generated code review, hardening, or release-gate design. Prefer this protocol over ad-hoc review when the user asks whether software is safe or ready to 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。

Use this skill whenever the user asks to inspect, audit, review, assess, or evaluate a codebase/project for maintainability, extensibility, architecture, reliability, security, testing, engineering quality, or AI-agent/prompt/tooling risks. Also use this when invoked by `/inspect`. It performs a read-only, evidence-backed, multi-agent project inspection with complete file coverage, per-dimension scoring, blunt technical findings, and explicit degraded-mode disclosure.

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。

Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. Use when optimizing prompts, improving LLM outputs, or designing production prompt templates.

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 implement Service Level Indicators (SLIs) and Service Level Objectives (SLOs) with error budgets and alerting. Use when establishing reliability targets, implementing SRE practices, or measuring service 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。

Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on re...

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。

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。

Analyze graded `trial_result` JSONL outputs from the eval pipeline, including reliability and custom slicing.

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。

Reviews code for objective correctness, security, and 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。

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。

Choose and compare cloud design patterns for distributed systems. Use when reviewing architecture, selecting workload patterns, or mapping reliability, performance, messaging, security, and migration concerns to concrete design options.

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 en chaos engineering (Gremlin, Litmus, fault injection, game days, fiabilité)

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。