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概要と使いどころ
Optimizes GKE costs, rightsizes workloads, and configures Spot VMs, CUDs, cost allocation, and resource quotas. Use when optimizing GKE cluster or workload costs, configuring GKE cost allocation or quotas, rightsizing CPU/memory requests, or selecting Spot VMs and machine types. Don't use for general compute class provisioning or GPU Selection (use gke-compute-classes instead).
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Locates and loads the right Google product skill on demand from a remote catalog index, instead of preloading every skill. Use at the START of any request touching a Google product, API, or developer platform - including Google Cloud (GKE, Cloud Run, IAM, BigQuery, Vertex AI, Spanner), Google Ads, Google Analytics, Google Workspace (Gmail, Drive, Admin SDK), Chrome and Chrome extensions, Android, Firebase, YouTube, Google Maps, Gemini and the Gemini API, Google Play, and Flutter. Consult the index before answering from memory or searching the web. Don't use for non-Google products.
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。
Configures best-practice, high-signal alerting policies for Google Cloud Run resources (services, jobs, and worker pools) based on seasoned SRE practices. Use when analyzing, recommending, writing, or deploying Terraform PromQL alerting policies to monitor Cloud Run error rates (4xx/5xx), request latency, container instance saturation (warning/critical), container CPU/memory utilization and allocation, billable instance time, job execution status, and worker pool queue backlog. Don't use for GKE workloads (use gke-alert-configuration) or Compute Engine VMs.
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。
Monitors and troubleshoots GKE TPU workloads, nodes, and node pools using GKE system metrics and PromQL. Use when monitoring TensorCore duty cycle, TPU memory, node readiness, multi-host TPU node pool availability, host maintenance or preemption interruptions, and calculating MTTR or MTBI metrics for GKE TPUs. Don't use for general non-TPU GKE workload monitoring or non-metric TPU 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。
Configures alerting policies in Terraform for Google Kubernetes Engine (GKE) clusters, workloads, and services using PromQL and Google Cloud Managed Service for Prometheus. Use when writing, analyzing, validating, or deploying Terraform alerting policies to monitor GKE service latency, traffic, error rates using Multi-Window Multi-Burn-Rate SLO alerts, memory saturation, and cluster health such as CrashLoopBackOff and Node NotReady conditions. Don't use for non-GKE compute runtimes such as standalone Compute Engine VMs or standalone Cloud Run services without GKE.
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。
Write and harden reusable agent-loop specification documents for biomechanics, data science, and vailá coding work. Use when the user says `/preto-loop`, asks to write/create/forge/specify/document an agent loop, turn an iterative vailá task into an autonomous loop, make an overnight loop, or design a biomechanics, data-pipeline, markerless-tracking, testing, refactoring, or performance loop. Include external verification, scientific validity, GUI/CLI parity, reproducibility, memory, guardrails, and named terminal states. Do not use merely to repeat a fixed scheduled prompt, execute an existing loop, or create unrelated documents.
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。
Read and reuse AI coding session context across Claude Code, Codex, Antigravity/agent hooks, Cursor adapters, and compatible AI tools. Use this skill whenever the user asks to read, inspect, resume, compare, or continue another session by name or session ID, asks what happened in the previous session, or starts a new session that should inherit the nearest prior context. The paired provider adapter automatically loads the nearest previous project session at startup and resolves explicit session-read requests.
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 Agentforce conversations that span multiple turns without losing context: session variable scoping, conversation memory, clarifying-question patterns, topic-to-topic (now subagent) handoff, and the right abstractions for accumulating state across turns. NOT for deciding the topic boundaries themselves or out-of-scope behavior — use agentforce/agent-topic-design. NOT for single-turn agent actions and their input/output contracts — use agentforce/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。
Firmware development for the Waveshare ESP32-S3-RLCD-4.2 (SKU 33298 / 33507, and the -EN variant) — an ESP32-S3-WROOM-1-N16R8 AIoT board built around a 4.2" fully reflective 300x400 monochrome LCD on a Sitronix ST7305/ST7306, with no backlight, plus an ES8311 speaker codec, an ES7210 dual-microphone ADC, an SHTC3 temperature/humidity sensor, a PCF85063A RTC, a microSD/TF slot on SDMMC, an 18650 holder with battery sensing, BOOT/KEY/PWR buttons and a 2x8 2.54 mm expansion header. Use when working on this board: project setup, platformio.ini, the qio_opi PSRAM memory type, the pin map and which header pins are traps, U8g2 and the ST7305 constructor, a blank or shifted panel, ST7305 low-power mode HPM/LPM, the shared I2C bus and its four slaves, I2S audio directions and the speaker amplifier enable, mounting the TF card, battery voltage, USB CDC console with no UART bridge, flashing over Type-C, deep sleep, or debugging why something on the board does not work.
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。
Firmware development for the 40-pin ESP32-S3-WROOM-1 camera board — the Freenove ESP32-S3-WROOM (FNK0085) and the AliExpress "ESP32-S3 CAM" / "ESP32-S3-WROOM N16R8 CAM" clones that copy its header: 20 pins a side, a 24-pin DVP camera FPC connector for an OV2640/OV3660, an on-board microSD slot on the SDMMC bus, a WS2812, two USB-C sockets (CH343 bridge and native USB), ESP32-S3-WROOM-1-N8R8 or N16R8 with 8 MB octal PSRAM. Use when working on this board or any ESP32-S3 DVP camera board: project setup, platformio.ini, the qio_opi PSRAM memory type, camera and SD_MMC pin maps, esp_camera_init failures, PSRAM not found, frame buffer allocation, SD card mount failures, which GPIOs are left once the camera is wired, strapping pins, ADC1/ADC2, flashing over the two USB ports, BOOT/RESET recovery, brownouts, or debugging why the camera, the card or the board does not work.
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。
Firmware development for the Waveshare ESP32-C6-Touch-LCD-1.47 board (ESP32-C6FH8, QFN32, 8 MB flash, SKU 31203 / 31201-M) — its 1.47" 172x320 JD9853 IPS panel, AXS5106L capacitive touch controller on I2C 0x63, QMI8658A IMU on I2C 0x6B, microSD/TF slot on the shared SPI2 bus, ETA6098 battery charger with VBAT sensing, USB-Serial-JTAG console, Wi-Fi 6/BLE/802.15.4 radio, and the ESP-IDF + PlatformIO setup around them. Use when working on this board: project setup, platformio.ini and sdkconfig, pin mapping and strapping pins, JD9853 bring-up, touch coordinates and calibration, LVGL memory budgeting, backlight PWM, TF card on the shared bus, battery voltage, flashing over Type-C, or debugging why something on the board does not work. This is NOT the non-touch ESP32-C6-LCD-1.47 (ST7789, 4 MB) — the two share a name and almost no pins.
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。
Complete fullstack development mastery covering modern web architectures, automation tools, AI integration, and production deployment 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。
Git expert covering branching strategies, collaboration, conflict resolution, and best 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。
Code refactoring expert for improving code quality, reducing technical debt, and safe transformations
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。
Application performance specialist for profiling, optimization, and scaling strategies
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。
Microservices design expert covering service decomposition, communication patterns, and distributed 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。
Container orchestration expert for Docker, Kubernetes, and cloud-native deployments
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。
Database expert for query optimization, indexing, schema design, and performance tuning
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。
Application security specialist covering OWASP top 10, secure coding, and vulnerability prevention
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 MCP server development guide covering FastMCP 2.14.3 features, Anthropic standards, ecosystem integration, and production deployment across all agentic IDEs
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。
Test automation expert covering unit tests, integration tests, TDD, and testing best 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。
Master debugger for Python code with expertise in common errors, performance issues, and debugging 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。
Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.
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 code reviewer focusing on quality, maintainability, performance, and best 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。