「raci」の検索結果

Maps the full set of legal obligations for the EU AI Act based on role + risk tier, producing an actionable compliance matrix with RACI assignments and implementation priorities. This skill should be used when the user asks to "map AI Act obligations", "check what we need to do under the AI Act", "create a compliance checklist", "check deployer obligations", "assess provider duties", or mentions Art. 26, Art. 16-17, AI literacy Art. 4, DPIA, fundamental rights assessment, or "Pflichtenkatalog" under the AI Act.

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。

Map knowledge components and skill hierarchies for a cognitive tutoring system or adaptive learning platform. Use when designing intelligent tutoring software or skill-based mastery 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。

Expert blueprint for sports games (FIFA, NBA 2K, Rocket League, Tony Hawk) covering physics-based ball interaction, team AI formations, contextual input, and match umpire/score authority. Broadcast framing routes to godot-camera-systems. Use when building soccer, basketball, hockey, racing sports, or arcade sports games. Keywords ball physics, magnus effect, formation AI, team tactics, contextual controls, steering behaviors.

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。

Manage, deploy, operate, and instrument applications with Temps. Use this skill whenever the user mentions Temps, `@temps-sdk/cli@0.1.37`, deploying or migrating an app to Temps, projects, environments, services, domains, backups, logs, monitoring, analytics, browser Performance Insights/Core Web Vitals, observability, error tracking, OpenTelemetry, tracing, session replay, or Temps Cloud. Also use it when preparing an application for production on Temps even if the user does not explicitly ask for the CLI. Route to focused references, proactively detect missing observability during create/link/deploy journeys, and use pinned `bunx` or `npx` CLI invocations with explicit target contexts.

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 debugging Godot projects — remote debugger, print techniques, signal tracing, common error patterns and fixes

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 knowledge for Azure Microsoft Opentelemetry development including configuration. Use when setting sampling, exporters, resource attributes, env vars, or tuning tracing/metrics behavior, and other Azure Microsoft Opentelemetry related development tasks. Not for Azure Monitor (use azure-monitor).

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。

Systematically trace bugs backward through call stack to find original trigger

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。

Understand how ideas evolved over time to find old solutions for new problems and avoid repeating past failures

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。

Java logging best practices with SLF4J, structured logging (JSON), and MDC for request tracing. Includes AI-friendly log formats for Claude Code debugging. Use when user asks about logging, debugging application flow, or analyzing logs.

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。

Cyber threat intelligence and OSINT analysis toolkit. Runs structured investigations and delivers analyst-grade intelligence products with sourced, trust-scored findings. Use for OSINT and CTI cases, digital-footprint and exposure review, domain/subdomain/DNS/certificate recon, web-infrastructure pivoting (favicon hashes, tracker IDs, TLS certs, phishing-kit fingerprinting, campaign clustering), username/email/phone enumeration, breach and infostealer-log triage, image forensics, geolocation, crypto-wallet and IBAN/bank-account tracing, darknet search, M365/Azure and SaaS tenant recon, China/Sinophone recon (ICP filings, PRC corporate registries, Baidu/FOFA/Quake/ZoomEye), vulnerability and ransomware lookup, threat modeling, PII redaction, and structured reporting. Commands include /case, /sweep, /query, /webpivot, /username, /phone, /email-deep, /breach-deep, /icp, /cn-corp, /iban, /stealer-log, /exposure, /threat-model, /report, /brief, /redact, /apikeys.

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。

Train and optimize AI agents using Microsoft's Agent Lightning framework with reinforcement learning. Use when setting up agent training, instrumenting agents with tracing, configuring LightningStore, implementing reward functions, or optimizing prompts with RL/APO algorithms.

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 structured logging with Pino, Prometheus metrics, and health checks for NestJS services. Use when adding JSON logging, request tracing with correlation IDs, Prometheus metric endpoints, or liveness/readiness health checks.

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。

Standards for structured logging and observability in Golang. Use when adding structured logging or tracing to Go services.

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。

Troubleshoot systematically using the Scientific Method. Use when debugging crashes, tracing errors, diagnosing unexpected behavior, or investigating exceptions.

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。

Deeply analyze an existing codebase feature by tracing execution paths, mapping architecture layers, understanding patterns and abstractions, and documenting dependencies. Use this skill when you need to understand how a feature works before modifying or extending it, when dispatched as a sub-task during feature-dev exploration, or when the user asks "how does X work in this codebase".

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。

OpenTelemetry setup, span context propagation, sampling strategies, Jaeger 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。

Find similar vulnerabilities across a codebase after discovering one instance. Uses pattern matching, AST search, Semgrep/CodeQL queries, and manual tracing to propagate findings. Adapted from Trail of Bits. Use after finding a bug to check if the same pattern exists elsewhere.

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。

Structured logging with Pino/Winston, OpenTelemetry tracing, metrics collection, Grafana dashboards, and alerting rules.

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。

Debugging workflows for Python (pdb, debugpy), Go (delve), Rust (lldb), and Node.js, including container debugging (kubectl debug, ephemeral containers) and production-safe debugging techniques with distributed tracing and correlation IDs. Use when setting breakpoints, debugging containers/pods, remote debugging, or production 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。

Train strict project ownership from repo-local docs/ai memory and central LLM Wiki project entities. Use when Codex or Claude Code should initialize a learning question bank, quiz or grade the user, run code tracing or failure drills, record misconceptions, schedule spaced repetition, or generate Markdown/JSON mastery dashboards without replacing llm-wiki or ai-project-memory.

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 Ontoly's deterministic Software Graph and MCP capabilities for repository architecture, request tracing, dependency analysis, configuration lookup, and impact analysis before falling back to source search.

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。

Activate this skill when BenchmarkDotNet (BDN) is involved in the task — creating, running, configuring, or reviewing BDN benchmarks. Also activate when microbenchmarking .NET code would be useful and BenchmarkDotNet is the likely tool. Consider activating when answering a .NET performance question requires measurement and BenchmarkDotNet may be needed. Covers microbenchmark design, BDN configuration and project setup, how to run BDN microbenchmarks efficiently and effectively, and using BDN for side-by-side performance comparisons. Do NOT use for profiling/tracing .NET code (dotnet-trace, PerfView), production telemetry, or load/stress testing (Crank, k6).

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 MSBuild binary logs to diagnose build failures. USE FOR: build errors that are unclear from console output, diagnosing cascading failures across multi-project builds, tracing MSBuild target execution order, and generally any MSBuild build issues. Requires an existing .binlog file. DO NOT USE FOR: generating binlogs (use binlog-generation), non-MSBuild build 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。

Configure and collect crash dumps for modern .NET applications. USE FOR: enabling automatic crash dumps for CoreCLR or NativeAOT, capturing dumps from running .NET processes, setting up dump collection in Docker or Kubernetes, using dotnet-dump collect or createdump. DO NOT USE FOR: analyzing or debugging dumps, post-mortem investigation with lldb/windbg/dotnet-dump analyze, profiling or tracing, or for .NET Framework processes.

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。