「dev tool」の検索結果

Quick reference for Terraform conventions including file organization, naming, modules, state, security, and anti-patterns. Use when writing or reviewing Terraform code.

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。

PR review checklist for Terraform changes covering structure, state safety, security, naming, modules, variables, providers, and CI/CD. Use when reviewing Terraform PRs or doing pre-merge 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。

Investigate a monitoring alert end-to-end. Pulls metrics, logs, traces, and recent code changes to identify root cause. Works with any monitoring MCP.

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。

CI/CD pipeline patterns for GitHub Actions, PR automation, and deployment workflows. Use when setting up CI, fixing broken pipelines, automating PR checks, or configuring deployment.

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。

Create a structured on-call log by pulling alerts from monitoring and writing a summary to your team's wiki. Requires a monitoring MCP (recommended: Datadog) and a wiki MCP (Confluence or Notion).

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。

Create or extend reusable Terraform modules with proper structure, interfaces, and documentation. Use when building new infrastructure modules or extending existing ones.

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 CLI tools in Python, JS/TS, Bash, or Go following clig.dev standards. Use when writing or reviewing CLI code.

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。

Sub-skill: Implement ARD (Agentic Resource Discovery). Publish /.well-known/ai-catalog.json so agents can discover MCP servers, A2A agents, skills and API tools (agenticresourcediscovery.org / ai-catalog).

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 optimizing, auditing, or preparing to ship web animations or rendering performance work: frame loops, scroll/viewport reveals, mount/unmount transitions, canvas/WebGL lifecycles, reduced motion, lazy rendering, and deferred off-screen work. Use for jank, per-frame allocations, forced reflows, render loops, animations that do not pause off-screen, content-visibility, requestIdleCallback, analyzing Chrome DevTools performance traces for animation or rendering issues, or choosing between browser-driven CSS/WAAPI, minimal JS, phase, and Motion/GSAP. Use when the user explicitly asks about phase APIs or behavior. Do not use during exploratory animation ideation, prototyping, visual iteration, or trying variants, even when the code already imports phase; wait until the user asks for performance guidance, an audit, production implementation, ship-readiness review, or about phase itself.

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。

Anti-detection behaviour for Genesis browser automation - what the Camoufox tools already do, what the agent must still do, Cloudflare Turnstile handling, and per-site notes for bot-hostile sites

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 coding-agent observability and evals. Use when measuring traces, replay, checkpoint lineage, tool grading, regression, or cost. Not for general LLM evals (ai-evals).

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。

Generates Angular code and provides architectural guidance. Trigger when creating projects, components, services, or HTTP communication, or for best practices on reactivity (signals, linkedSignal, resource, httpResource), forms, dependency injection, routing, SSR, accessibility (ARIA), animations, styling (component styles, Tailwind CSS), testing, naming conventions, or CLI tooling.

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。

CLI-first serial port debugging and macro automation with serial-mcp-server for Codex and Claude Code. Use when working with UART or USB-serial devices, listing serial ports, probing STM32/Arduino/ESP32 boards, writing or reading serial data, running JSON serial macros, controlling RTS/DTR, or using serial-mcp-server through MCP tools.

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。

Troubleshoots Monte Carlo AI agent alerts and traces — eval score drops, latency/token spikes, trajectory and validation breaches. Not for data incidents (monte-carlo-analyze-root-cause) or monitor creation (monte-carlo-monitoring-advisor).

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。

Route data-related requests to the right Monte Carlo skill or workflow. USE WHEN alerts, incidents, data broken, stale, coverage gaps, data quality, or any ambiguous data observability request.

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。

Diagnoses pipeline performance issues -- slow jobs, expensive queries, latency trends -- using Monte Carlo's cross-platform observability. Uses a tiered investigation approach: discover problems, bridge to affected tables, then drill into root causes. Activates when a user asks about slow pipelines, expensive queries, or performance regressions.

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 data coverage, create monitors for warehouse tables and AI agents. Covers coverage gaps, use-case analysis, data monitor creation, and agent 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。

Investigate data incidents and find root causes using Monte Carlo's observability data. Guides the agent through systematic investigation: alert lookup, lineage tracing, ETL checks, query analysis, and data profiling. Activates when a user asks about data issues, incidents, alerts, or why data looks wrong.

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 a new AI agent in a Python codebase for Monte Carlo Agent Observability. Detects AI libraries, installs the Monte Carlo OpenTelemetry SDK, and proposes tracing setup and decorator placements as diffs. Asks before editing any file.

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。

Guide users from coverage analysis to monitor creation. USE WHEN user asks what should I monitor, where are my gaps, improve coverage, or wants a systematic approach to monitoring across their data estate.

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。

Reinforces an AI agent by turning Monte Carlo's reinforcement loop diagnosis into code fixes. Reads the daily reinforcement loop report for an agent's workflows, ranks the diagnosed issues, proposes what to fix, and — with the user's approval at each step — opens a pull request. Activates on "fix my agent", "improve my agent's health", "reinforce my agent", "what should I fix in my agent". Not for investigating a specific agent alert or trace (monte-carlo-troubleshoot-agent-traces), creating agent monitors (monte-carlo-monitoring-advisor), or instrumenting a new agent (monte-carlo-instrument-agent).

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。

Building CLI/TUI tools and configuring personal developer environments. Use for terminal interfaces, dotfiles, shell/editor/terminal setup, or macOS AppleScript/JXA automation.

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。

Automating browsers via Playwright and Chrome DevTools for data collection, form interaction, screenshot capture, and network monitoring. Task completion focus (vs Voyager for E2E 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。

Design metacognitive checkpoints that prevent AI-assisted learning from bypassing genuine understanding. Use when students use AI tools and may overestimate their own comprehension.

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。