本文へ移動
cccskills

「reliability」の検索結果

343 件 ・ 関連度順

概要と使いどころ

Build and maintain CI/CD pipelines, containerization, Kubernetes manifests, and quality gates. Use when tasks involve Docker, Kubernetes, GitHub Actions/Azure DevOps pipelines, deployment, reliability, or static analysis workflows.

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。

ThiagoGuislotti/copilot-instructions22026年6月29日 更新

Design and implement observability and SRE controls including SLO/SLI, OpenTelemetry instrumentation, alerting, dashboards, and incident readiness. Use when tasks involve telemetry coverage, operational reliability metrics, incident response, or runbook quality.

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。

ThiagoGuislotti/copilot-instructions22026年6月29日 更新

Improve platform resilience with timeout/retry/circuit-breaker strategy, capacity controls, chaos testing, and disaster recovery readiness. Use when tasks involve reliability hardening, failure-mode testing, incident prevention, or recovery objectives.

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。

ThiagoGuislotti/copilot-instructions22026年6月29日 更新

Build and maintain CI/CD pipelines, containerization, Kubernetes manifests, and quality gates for this repository. Use when the user asks for Docker, Kubernetes, GitHub Actions/Azure DevOps pipelines, deployment, reliability, or static analysis workflows.

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。

ThiagoGuislotti/copilot-instructions22026年6月29日 更新

Use when coding interviews, focus groups, or open-ended responses with AI assistance, when a codebook needs building or auditing, when intercoder reliability must be computed, or when qualitative quotes need integrity checks before publication.

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。

OnourImpram/claude-code-for-social-scientists22026年9月6日 更新

Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it's making them reliable. Every extra decision multiplies failure probability. This skill covers agent loops (ReAct, Plan-Execute), goal decomposition, reflection patterns, and production reliability. Key insight: compounding error rates kill autonomous agents. A 95% success rate per step drops to 60% b

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。

danstrem2/clawdbot-skill-master-pack22026年2月1日 更新

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。

ruvnet/mcp-studio22026年10月10日 更新