How do I install the Managing GCP Cloud Run Deep skill?

Run npx skillmds@latest add cloudthinker-ai/managing-gcp-cloud-run-deep in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

What does the Managing GCP Cloud Run Deep skill do?

Use when working with Gcp Cloud Run Deep — deep Google Cloud Run analysis covering service inventory, revision management, traffic splitting, autoscaling configuration, request latency metrics, container resource limits, domain mappings, and VPC connector usage. Use for comprehensive Cloud Run optimization and health assessment. It is listed under DevOps & Infra on SkillMD.

Is Managing GCP Cloud Run Deep safe to use?

This skill has not completed SkillMD's automated safety review yet. Independent scanners report: SkillSpector: PASS, Skill Scanner: PASS. Capability flags: reads secrets. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Managing GCP Cloud Run Deep?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Managing GCP Cloud Run Deep free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Managing GCP Cloud Run Deep?

cloudthinker-ai (@cloudthinker-ai) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Managing GCP Cloud Run Deep by cloudthinker-ai

Name: Managing GCP Cloud Run Deep
Rating: 4.2 (5 reviews)
Author: cloudthinker-ai

GCP Cloud Run Deep Management

Comprehensive analysis of Cloud Run services, revisions, traffic routing, and performance.

Phase 1: Discovery

#!/bin/bash
PROJECT="${GOOGLE_CLOUD_PROJECT}"

echo "=== Cloud Run Services ==="
gcloud run services list --project="$PROJECT" \
  --format="table(name, region, status.url, status.conditions[0].status:label=READY, metadata.annotations.'run.googleapis.com/launch-stage':label=STAGE)" \
  2>/dev/null | head -20

echo ""
echo "=== Service Details ==="
for SVC in $(gcloud run services list --project="$PROJECT" --format="value(name)" 2>/dev/null); do
  REGION=$(gcloud run services list --project="$PROJECT" --format="value(region)" --filter="name=${SVC}" 2>/dev/null)
  gcloud run services describe "$SVC" --project="$PROJECT" --region="$REGION" --format="json" \
    | jq '{name: .metadata.name, image: .spec.template.spec.containers[0].image, cpu: .spec.template.spec.containers[0].resources.limits.cpu, memory: .spec.template.spec.containers[0].resources.limits.memory, concurrency: .spec.template.spec.containerConcurrency, timeout: .spec.template.metadata.annotations["run.googleapis.com/execution-environment"], minInstances: .spec.template.metadata.annotations["autoscaling.knative.dev/minScale"], maxInstances: .spec.template.metadata.annotations["autoscaling.knative.dev/maxScale"]}' 2>/dev/null
done | head -30

echo ""
echo "=== Traffic Splitting ==="
for SVC in $(gcloud run services list --project="$PROJECT" --format="value(name)" 2>/dev/null); do
  REGION=$(gcloud run services list --project="$PROJECT" --format="value(region)" --filter="name=${SVC}" 2>/dev/null)
  gcloud run services describe "$SVC" --project="$PROJECT" --region="$REGION" \
    --format="json" | jq ".status.traffic[] | {revision: .revisionName, percent: .percent, tag: .tag}" 2>/dev/null
done

echo ""
echo "=== Domain Mappings ==="
gcloud run domain-mappings list --project="$PROJECT" \
  --format="table(name, routeName, status.conditions[0].status:label=READY)" 2>/dev/null

echo ""
echo "=== Revisions ==="
for SVC in $(gcloud run services list --project="$PROJECT" --format="value(name)" 2>/dev/null); do
  REGION=$(gcloud run services list --project="$PROJECT" --format="value(region)" --filter="name=${SVC}" 2>/dev/null)
  gcloud run revisions list --service="$SVC" --project="$PROJECT" --region="$REGION" \
    --format="table(name, active, status.conditions[0].status:label=READY, metadata.creationTimestamp)" \
    2>/dev/null | head -5
done

Phase 2: Analysis

#!/bin/bash
PROJECT="${GOOGLE_CLOUD_PROJECT}"
END=$(date -u +"%Y-%m-%dT%H:%M:%SZ")
START=$(date -u -d "7 days ago" +"%Y-%m-%dT%H:%M:%SZ" 2>/dev/null || date -u -v-7d +"%Y-%m-%dT%H:%M:%SZ")

echo "=== Request Count & Latency ==="
gcloud monitoring time-series list \
  --project="$PROJECT" \
  --filter='metric.type="run.googleapis.com/request_count"' \
  --interval-start-time="$START" --interval-end-time="$END" \
  --format="table(resource.labels.service_name, metric.labels.response_code_class, points[0].value.int64Value:label=REQUESTS)" \
  2>/dev/null | head -20

echo ""
echo "=== Request Latencies ==="
gcloud monitoring time-series list \
  --project="$PROJECT" \
  --filter='metric.type="run.googleapis.com/request_latencies"' \
  --interval-start-time="$START" --interval-end-time="$END" \
  --format="table(resource.labels.service_name, points[0].value.distributionValue.mean:label=AVG_MS)" \
  2>/dev/null | head -20

echo ""
echo "=== Instance Count ==="
gcloud monitoring time-series list \
  --project="$PROJECT" \
  --filter='metric.type="run.googleapis.com/container/instance_count"' \
  --interval-start-time="$START" --interval-end-time="$END" \
  --format="table(resource.labels.service_name, metric.labels.state, points[0].value.int64Value:label=COUNT)" \
  2>/dev/null | head -20

echo ""
echo "=== CPU & Memory Utilization ==="
gcloud monitoring time-series list \
  --project="$PROJECT" \
  --filter='metric.type="run.googleapis.com/container/cpu/utilizations"' \
  --interval-start-time="$START" --interval-end-time="$END" \
  --format="table(resource.labels.service_name, points[0].value.distributionValue.mean:label=CPU_UTIL)" \
  2>/dev/null | head -20

echo ""
echo "=== VPC Connectors ==="
for SVC in $(gcloud run services list --project="$PROJECT" --format="value(name)" 2>/dev/null); do
  REGION=$(gcloud run services list --project="$PROJECT" --format="value(region)" --filter="name=${SVC}" 2>/dev/null)
  VPC=$(gcloud run services describe "$SVC" --project="$PROJECT" --region="$REGION" --format="json" \
    | jq -r '.spec.template.metadata.annotations["run.googleapis.com/vpc-access-connector"] // "none"' 2>/dev/null)
  echo "${SVC}: ${VPC}"
done

Output Format

GCP CLOUD RUN DEEP ANALYSIS
==============================
Service          Region       CPU    Memory  Min/Max  Requests  P50-ms  P99-ms
──────────────────────────────────────────────────────────────────────────────────
order-api        us-central1  1      512Mi   1/10     125000    45      230
auth-service     us-central1  2      1Gi     2/50     890000    12      85
batch-worker     us-east1     1      2Gi     0/5      8900      2400    5000

Traffic: 3 services with 100% on latest | 1 canary at 10%
VPC Connectors: 2/3 services connected | Domain Mappings: 2

Safety Rules

Read-only: Only use gcloud run services list, describe, revisions list, and monitoring queries
Never deploy, update traffic, or delete services without confirmation
Secrets: Never output secret environment variable values
Quota: Cloud Monitoring API quota is 6000 read requests per minute

Anti-Hallucination Rules

NEVER assume resource names — always discover via CLI/API in Phase 1 before referencing in Phase 2.
NEVER fabricate metric names or dimensions — verify against the service documentation or --help output.
NEVER mix CLI commands between service versions — confirm which version/API you are targeting.
ALWAYS use the discovery → verify → analyze chain — every resource referenced must have been discovered first.
ALWAYS handle empty results gracefully — an empty response is valid data, not an error to retry.

Counter-Rationalizations

Shortcut	Counter	Why
"I'll skip discovery and check known resources"	Always run Phase 1 discovery first	Resource names change, new resources appear — assumed names cause errors
"The user only asked for a quick check"	Follow the full discovery → analysis flow	Quick checks miss critical issues; structured analysis catches silent failures
"Default configuration is probably fine"	Audit configuration explicitly	Defaults often leave logging, security, and optimization features disabled
"Metrics aren't needed for this"	Always check relevant metrics when available	API/CLI responses show current state; metrics reveal trends and intermittent issues
"I don't have access to that"	Try the command and report the actual error	Assumed permission failures prevent useful investigation; actual errors are informative

Managing GCP Cloud Run Deep

File contents

GCP Cloud Run Deep Management

Phase 1: Discovery

Phase 2: Analysis

Output Format

Safety Rules

Anti-Hallucination Rules

Counter-Rationalizations

Frequently asked questions

Managing GCP Cloud Run Deep

File contents

GCP Cloud Run Deep Management

Phase 1: Discovery

Phase 2: Analysis

Output Format

Safety Rules

Anti-Hallucination Rules

Counter-Rationalizations

Related skills

Frequently asked questions

How do I install the Managing GCP Cloud Run Deep skill?

What does the Managing GCP Cloud Run Deep skill do?

Is Managing GCP Cloud Run Deep safe to use?

Which AI agents work with Managing GCP Cloud Run Deep?

Is Managing GCP Cloud Run Deep free to use?

Who published Managing GCP Cloud Run Deep?