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概要と使いどころ
Auto-detect network issues and force proxy usage with proxychains4. Use this skill when encountering connection timeouts, DNS failures, or blocked network access. Default proxy is http://127.0.0.1:9910
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。
Perform a requested security review of a NemoClaw PR or a PR linked to an issue. Use for vulnerability or trust-boundary assessment.
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。
Reproduce stale NemoClaw bug reports on the reported and newest releases, then propose evidence-backed triage. Never auto-closes issues.
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。
Remove bracketed NemoClaw tags from GitHub issue and PR titles. Use for a requested title cleanup.
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。
Compare competing NemoClaw PRs for one issue and recommend a merge or salvage candidate from review evidence.
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。
Check Screenpipe health status, process state, and diagnose common issues
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, remediates, and manages Identity and Access Management (IAM) access issues on Google Cloud. Supports two distinct operational modes: (1) Requester Flow for developers encountering access denials (capturing error context, self-service PAM JIT activations, elevated developer self-remediation, or logging structured tickets), and (2) Resolver Flow for privileged administrators (authoritative Policy Troubleshooter analysis, deny policy exemptions, least-privilege role discovery, and PAM/IAM provisioning).
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。
Assists in provisioning instances and databases, designing performant schemas, and querying data in Cloud Spanner on Google Cloud. Use when designing primary keys, writing SQL queries or client library code, or diagnosing performance issues.
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。
Investigates Google Cloud networking issues by analyzing GCP logs, metrics, and diagnostics. Use when investigating dropped network traffic, packet drops, drop reasons, VPC Flow Logs (including Private Service Connect / PSC, serverless / App Engine Direct VPC, and cost estimation), NAT, firewall, or threat logs, querying latency and throughput metrics, or running Connectivity Tests for path diagnostics. Don't use for generic VM management or non-observability tasks.
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。
Trigger on mention of GKE cluster autoscaler, node autoscaling, node pool auto-creation / node auto-provisioning. Provides guidance on enabling and optimizing cluster autoscaler, best practices, and troubleshooting issues such as nodes not scaling up or down, zonal stockouts, or capacity buffers. Do not use for ComputeClass-specific YAML generation or priority configuration (defer to gke-compute-classes skill).
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。
Develop AI-powered applications using Genkit in Python. Use when the user asks about Genkit, AI agents, flows, or tools in Python, or when encountering Genkit errors, import issues, or API problems.
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 GKE observability, including Cloud Logging, Cloud Monitoring, and managed Prometheus. Use when configuring GKE monitoring, setting up GKE logging, or configuring Prometheus metrics collection, and to troubleshoot Managed Service for Prometheus (GMP) issues such as missing metrics, unhealthy scrape targets, PodMonitoring misconfiguration, rule/alert evaluation failures, and monitoring permission errors. Don't use to configure local application logging frameworks or external APMs outside 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。
Diagnoses GKE persistent-storage failures — volume attach/mount errors (Regional PD on optimized VMs, fsGroup mount timeouts), disk-performance and node storage-pressure issues, slow-disk Pod-creation failures, volume-expansion problems, Local SSD / Hyperdisk Storage Pool creation errors, and Cloud Storage FUSE OOM. Use when Pods are stuck in ContainerCreating, volumes fail to attach or mount, or nodes report storage pressure. Don't use for routine storage provisioning or StorageClass/PVC authoring (see the gke-storage skill).
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。
Summarizes data lineage graphs on Google Cloud to help users debug data quality issues and understand data provenance for BigQuery and Cloud Storage. Use when summarizing upstream and downstream data flows, and presenting complex lineage data as an intuitive Markdown report. Don't use for generic BigQuery queries, editing lineage relationships, or downstream deprecation. Don't use for downstream blast-radius impact analysis (use datalineage-bigquery-asset-impact-analysis skill instead).
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。
Develop AI-powered applications using Genkit in Node.js/TypeScript. Use when the user asks about Genkit, AI agents, flows, or tools in JavaScript/TypeScript, or when encountering Genkit errors, validation issues, type errors, or API problems.
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。
Guides developers through Google Ads API quickstart: credential setup, choosing from 6 client libraries/REST, configuring environments, and running a "retrieve campaigns" script. Troubleshoots common setup errors: USER_PERMISSION_DENIED, login_customer_id issues, and DEVELOPER_TOKEN_NOT_APPROVED. Use this skill when: - The user asks how to get started with the Google Ads API. - The user needs to set up Google Ads credentials or developer tokens. - The user wants to write a quickstart/example script for Google Ads. - The user encounters errors like USER_PERMISSION_DENIED or DEVELOPER_TOKEN_NOT_APPROVED.
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 Logging Query Language (LQL) queries for Cloud Logging on Google Cloud from natural language. Use this skill when you need to query log data or when you are debugging issues. You can filter log data by Google Cloud service. Don't use this skill to query other databases, such as SQL or Cloud Spanner.
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。
Assists in provisioning instances/tables, designing performant schemas, and querying data in Bigtable. Use when designing Bigtable row keys, configuring column families, writing SQL queries or client library code (Java, Go, Python) for Bigtable, or diagnosing performance/hotspotting issues. Also use when provisioning Bigtable clusters using gcloud or cbt CLIs. Don't use for generic Cloud SQL administration.
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。
Provides diagnostic workflows and step-by-step root-cause analysis procedures for actively broken, failing, or slow BigQuery jobs, execution graph and query plan stage bottlenecks, system performance issues, or unexpectedly expensive workloads. Use when interpreting symptoms, isolating bottlenecks, diagnosing cost spikes (on-demand query spend, capacity slot autoscaling, storage growth), execution graph stages or substep variables, identifying root causes, and determining remediation steps. Don't use for writing or optimizing SQL, proactive capacity planning, or storage layout design (use bigquery-optimization), or when the user already knows which telemetry they want and just needs the query (use bigquery-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。
The 15-minute escalation-routing runbook for the Plain support queue — detecting SLA breaches against {{sla_targets}}, VIP accounts in {{vip_accounts}}, and high severity, routing each qualifying ticket to the right team, opening a linked issue in {{linear_team}} when engineering is needed, and posting the alert to {{escalation_channel}}. Never closes a ticket or promises a resolution.
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。
Hourly warehouse health sweep for {{warehouse_schema}}. Checks every monitored table's freshness against its SLA, compares row counts to the table's own trailing baseline, diffs the schema for drift, and checks for failed or stalled loads. Posts anomalies to {{alert_channel}} and drafts a GitHub issue in {{incident_repo}} with the likely cause. Read-only across the warehouse — never modifies data, tables, or pipelines.
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。
Daily reusable-session flaky-test triage for {{target_repo}}. Pulls CI run history from GitHub, scores each test's non-determinism against a persistent per-test ledger, and once a test reaches {{quarantine_threshold}} opens a quarantine PR (skip + reason, never delete) plus a running tracking issue, and posts a summary to {{alert_channel}}. Never merges.
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。
The pull request loop for this repo: branch → commit → verify in your own box (local tests + local stack) → PR into main → demo video recorded with agent-browser on the local stack → `gh --attach` → self-merge → verify on dev. A PR into main runs no CI unless a person adds the `test` or `preview` label (one run each). Load when opening, updating, or finishing a pull request; when writing a PR body; when asking what CI runs where; when adding or explaining PR labels or the per-PR preview environment; or when attaching an image or video to a PR, issue, or comment.
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。
Turns feedback from Plain, public reviews, and a Slack channel into deduplicated, quantified themes, and reconciles each theme against one Linear issue in {{linear_team}} — never prioritizing, assigning, or closing.
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。