给你的 AI 助手 装上 专业技能

run-e2e

Run SkyWalking E2E tests locally

测试与质量

Rebuild the SkyWalking distribution and OAP Docker image after source changes. Use before running e2e tests so the image reflects your code changes. Avoids the "image looks updated but runtime has stale jars" trap.

图像与多媒体运维与部署

license

Check and fix Apache 2.0 license headers and dependency licenses using skywalking-eyes. Use before submitting a PR.

gh-pull-request

Verify, commit, and push changes on a PR branch. Runs pre-flight checks (compile, checkstyle, license headers) before every push. Also creates the PR if one doesn't exist yet.

代码审查

generate-classes

Generate bytecode classes from DSL scripts (MAL, OAL, LAL, Hierarchy). Runs the compiler and dumps .class files for inspection.

compile

Build SkyWalking OAP server, run javadoc checks, and verify checkstyle. Use to validate changes before submitting a PR.

后端与接口

ci-e2e-debug

Download and inspect CI e2e test logs from GitHub Actions artifacts. Use when investigating e2e test failures in CI.

开发工程师测试与质量运维与部署

cloud-operator-share-context

Shares Google Cloud operating context the user or their team already have — environments, projects, services and regions, product preferences, best practices, architecture guidance, runbooks — found in agent instruction files, local memory, skills and workflows, and the repository's GCP docs, with the operator agent's memory after the user reviews the list. Use when the user runs this skill, accepts the cloud-operator or cloud-operator-setup skill's offer to share their existing Google Cloud context, or asks to share, import or save that context or a GCP fact for the operator agent. Don't use for ordinary GCP requests, which go to the cloud-operator skill.

cloud-operator-setup

Sets up the operator agent in a Google Cloud project via the autocloud MCP server: picks the operator project, creates or checks the user's operator agent, saves the project, and offers to share the user's existing Google Cloud operating context with the operator agent's memory. Use when the user runs this skill, asks to set up the operator or create their operator agent, or chooses setup when the cloud-operator skill asks. Don't use for ordinary GCP requests or installing gcloud.

developer-device-platform-device-run

Runs automated, non-interactive jobs on remote Android devices with Developer Device Platform (DDP) Device Run using `gcloud beta device-run sessions`: native Android executables or shell scripts (for example CLI tools, custom runners, test harnesses or benchmarks) and Android instrumentation tests, on one or many devices, then waits for completion and collects per-device outputs and logs. Use when running a binary or test suite on remote devices without an interactive connection. Don't use it for interactive device reservations, adb tunnels or screen streaming (use developer-device-platform-basics), or for local devices.

compute-vm-creation

Guides the planning, configuration, and provisioning of standalone Google Compute Engine virtual machines (VMs) and bare metal (-metal) instances using gcloud CLI commands or direct MCP tool execution. Use ONLY when the user wants to create, configure, or provision a new standalone Compute Engine VM or bare metal instance from conversational intents (hardware sizing, region selection, OS image, networking, security defaults). DO NOT read or use for Google Kubernetes Engine (GKE) Autopilot or Standard clusters, Kubernetes node pools or Pods, container orchestration, managing existing VMs, Managed Instance Groups (MIGs), or Instance Templates.

agent-platform-autorater-customization

Customizes an autorater (evaluation metric) for an AI agent so its grades match the user's own judgment, starting from the agent's real trajectories. Use when a user wants a metric tailored to their agent, wants to turn failing trajectories into rubrics, says an existing autorater grades their agent wrong, or wants an autorater to agree with human labels (autorater hillclimbing, judge calibration). Don't use for quick evals with built-in metrics or for improving the agent itself (use agent-platform-eval-flywheel).

iap-agent-platform-troubleshooting

Diagnoses Identity-Aware Proxy (IAP) denials of outbound requests from Gemini Enterprise Agent Platform agents that run on Agent Runtime and send traffic through Agent Gateway. Use when an agent request fails with "Egress request is not authorized", when Cloud Audit Logs show an IAP egressViaIAP decision with granted false, when IAP audit entries are missing while debugging agent egress, or when Access policies, IAP allow policies, policy bindings, or Agent Registry entries might block agent egress. Don't use for Gemini model IAM permission errors (such as aiplatform.endpoints.predict), Agent Platform API errors, or Agent Runtime deployment or 500 errors (use agent-platform-troubleshooting instead). Don't use for setting up, creating, or connecting an Agent Gateway, or for IAP web apps, IAP TCP forwarding, or SSH.

bigquery-migration-validator

Validates that tables migrated to BigQuery match their source (Teradata, Snowflake, Oracle, Redshift, Databricks, PostgreSQL, SQL Server, MySQL, MariaDB, Hive, Impala, Vertica) with the BigQuery Migration Service (BQMS) validation tool: schema and data type comparison, column aggregate comparison, and row-level data comparison. Use when generating or editing a validation config, running a validation, choosing sampling or partitioning, fixing connection or driver errors, or investigating validation results in BigQuery and Cloud Storage to decide which mismatches to fix. Don't use for SQL translation (use bigquery-migration-sql-translator), extracting source database catalog metadata or query logs, generating SQL scripts (such as COPY INTO) or exporting or unloading source tables to Parquet on Cloud Storage, loading staged files into BigQuery (such as bq load), or general BigQuery query failures and tuning (use bigquery-troubleshooting or bigquery-optimization).

bigquery-migration-metadata-extractor

Extracts database catalog metadata and query logs from data warehouses by running the DWH migration extractor locally or on a temporary Google Cloud VM through the BigQuery Migration Cloud Extractor (credentials held in Secret Manager), and inspects extracted `.zip` archives to report object and volume statistics. Use when extracting database metadata or query logs from Snowflake, Redshift, Teradata, Oracle, PostgreSQL, SQL Server, Hive, etc., deciding between local and cloud (Cloud Extractor) extraction, or inspecting extracted dwh-migration-*.zip archives to print post-extraction statistics. Don't use for SQL dialect translation (use bigquery-migration-sql-translator), exporting table data to Parquet or CSV files (such as Snowflake `COPY INTO` unloads) or loading it into BigQuery (use the BigQuery Data Transfer Service or bigquery-basics), or validating migrated data.

bigquery-migration-data-loader

Provisions BigQuery datasets, ingests staged Parquet files directly into native BigQuery tables by using bq load, and deploys already-translated views from local or Cloud Storage BigQuery Migration Service (BQMS) translation outputs. Use when provisioning target BigQuery datasets for warehouse migrations, batch loading staged Parquet files into native BigQuery tables by using bq load, auditing loaded table row counts, or deploying already-translated BigQuery views into a target dataset. Don't use for extracting source database catalog metadata, translating SQL queries, views, stored procedures, or DDL schemas to GoogleSQL (use bigquery-migration-sql-translator), exporting or unloading source database tables (such as Snowflake, Databricks, Teradata, or Oracle) to Parquet on Cloud Storage, or running post-migration cross-database data validation.

litert-google-tensor

Compiles LiteRT (.tflite) models ahead-of-time for Google Tensor TPUs (Tensor G5 on Pixel 10, Tensor G6 on Pixel 11) locally on Linux x86_64 or on Google Colab (--colab on macOS, Windows, or ARM hosts), benchmarks them on a USB-attached Pixel with LiteRT's benchmark_model (NPU latency, peak memory, TPU tile time, TPU energy, optional CPU baseline), and packages compiled models for an Android Gradle app as a Google Play AI pack with the Tensor NPU runtime module and a Kotlin loader, applies it to the app and verifies it on a Pixel. Use when the user asks to compile, AOT-compile, or prepare a .tflite model for the Pixel TPU/NPU, check whether a .tflite is already Tensor-compiled, measure on-device NPU performance of a compiled model, or package or deploy a compiled model into their Android app (or replace the one it ships). Don't use for Qualcomm, MediaTek, or Samsung NPUs, GPU-only benchmarking, LiteRT-LM or LLM (.litertlm) models, on-device JIT compilation, non-Gradle builds, or publishing to Google Play.

compute-spot-capacity-advice

Evaluates Google Compute Engine (GCE) Spot VM capacity obtainability, placement likelihood, and machine family or regional flexibility. Use when planning batch workloads, finding available Spot CPU or GPU capacity, assessing stockout risks, or optimizing instance distribution across zones. Don't use for TPU availability, on-demand reservation capacity (use compute-on-demand-reservations), or future GPU/TPU calendar bookings (use compute-future-reservations).