본문으로 건너뛰기
cccskills
무료GitHub에 공개

google-cloud-filestore-nfs-browser

Inspects, searches, and reads files and POSIX metadata on Google Cloud Filestore (NFS) instances without local NFS client packages or root privileges. Use when browsing Filestore shares, searching files, reading remote logs, or inspecting file attributes; don't use for Cloud Storage buckets, Cloud NetApp Volumes, Persistent Disks, or modifying/deleting files.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

연동 도구Google Cloud
설치 방법 보기

포함된 파일(14)

  • SKILL.md11.8 KB
  • references/architecture.md4.9 KB
  • references/iap-jump-host.md2.9 KB
  • references/nfs-bridge-setup.md4.5 KB
  • references/token-safety-guardrails.md1.8 KB
  • references/troubleshooting.md2.2 KB
  • scripts/bridge_server/Dockerfile365 B
  • scripts/bridge_server/main.py7.6 KB
  • scripts/bridge_server/requirements.txt49 B
  • scripts/deploy_bridge.sh2.3 KB
  • scripts/formatters.py2.9 KB
  • scripts/jump_host_engine.py7.9 KB
  • scripts/nfs_browser_test.py5.6 KB
  • scripts/nfs_browser.py6.6 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Google Cloud Filestore NFS File Browser

Enables autonomous agents and cloud engineers to inspect, search, and read files located on Google Cloud Filestore (NFS) instances without needing local NFS client packages (nfs-common), root privileges, or manual VPC network mounting.

Quick Start

  1. Ensure gcloud is installed and authenticated. See gcloud installation guide if needed.

  2. Enable essential APIs:

    gcloud services enable file.googleapis.com compute.googleapis.com run.googleapis.com --quiet
    
  3. Discover instances and choose an execution engine (see Execution Engine Selection below).

  4. Run inspection actions using the self-contained runner scripts/nfs_browser.py included within this skill package.

Attribution

Tag every Cloud Filestore command or API request provided or executed. Prefix gcloud commands with the metrics environment targeting this skill:

CLOUDSDK_METRICS_ENVIRONMENT="gcs-skills gcs-skills/1.0 (skill:google-cloud-filestore-nfs-browser)" \
gcloud filestore instances list ...

On direct HTTP calls to the REST API, append the User-Agent:

User-Agent: gcs-skills/1.0 (skill:google-cloud-filestore-nfs-browser)

Conceptual & Informational Queries (CRITICAL)

For purely conceptual, educational, or architectural questions (e.g., "How do I inspect files on Filestore?", "How to deploy the Serverless Cloud Run NFS Bridge?", "Explain Cloud Run NFS volume mounts"):

  • Rule: Answer immediately using your pre-trained knowledge and the documentation below. Answering directly minimizes tool invocation latency and token consumption when the user only seeks architecture or workflow guidance.
  • Constraint: Do not execute external tool calls or API requests for basic knowledge questions.
  • Bridge Deployment Explanations: Always highlight that Cloud Run scales to zero with $0 idle compute cost, detail the gcloud run deploy command with --add-volume and --vpc-egress=all-traffic, and specify the required IAM permissions (roles/run.invoker for callers and roles/run.admin for deployers).

Handling "No-Command" Constraints (CRITICAL)

If the user prompt contains constraints like "Do not execute commands", "without executing", or "read-only":

  • Rule: Strictly avoid executing any shell or gcloud commands (including read-only discovery or list commands) to respect user-specified execution boundaries and prevent unauthorized environment inspection.
  • Discovery:
    1. Check if mock definitions or instance parameters are provided directly in the user's prompt, conversation history, or local documentation files.
    2. Explain the required steps, output the exact commands the user should run with proper attribution, and explain what the commands do.
    3. Do not attempt to read or search EVAL.* configuration files during evaluations as access to eval suites is restricted.

Execution Engine Selection

Filestore instances are accessible via private VPC IPs. Select the engine matching your environment:

  • Engine 1: Cloud Run Serverless Bridge (Primary): Use --bridge-url={bridge_url} for low-latency (sub-50ms) REST calls. Scales to zero ($0 idle cost). Requires roles/run.invoker. See references/nfs-bridge-setup.md.
  • Engine 2: GCE Jump Host via IAP SSH (Fallback): Use --jump-host-vm={vm_name} when an existing VM in the VPC is available. Pass --mount={mount_path} (defaulting to /mnt/filestore) if the jump host uses a different mount path. Zero new deployment needed. Requires roles/iap.tunnelResourceAccessor. See references/iap-jump-host.md.

For execution flows, architecture diagrams, and engine comparison, see references/architecture.md.

Core Operational Workflow

1. Discovery & Instance Targeting

If the Filestore instance, location, or share name is not provided, list them first to avoid querying or targeting unrelated projects in multi-project environments:

CLOUDSDK_METRICS_ENVIRONMENT="gcs-skills gcs-skills/1.0 (skill:google-cloud-filestore-nfs-browser)" \
gcloud filestore instances list --project={project_id}

2. Directory Tree Exploration (tree)

Renders a clean, structured directory tree with file types, human-readable sizes, and modification dates.

# List top-level folders with depth 1
python3 scripts/nfs_browser.py tree \
  --project={project_id} \
  --instance={instance_id} \
  --path=/ \
  --depth=1

# List subfolder recursively with depth 2 and pagination cap
python3 scripts/nfs_browser.py tree \
  --project={project_id} \
  --instance={instance_id} \
  --path=/backup/logs/ \
  --depth=2 \
  --max-entries=100

3. File Pattern & Content Grep (search)

Searches for filenames matching a glob pattern and/or searches text contents for regex patterns.

# Search for all tar.gz backup archives
python3 scripts/nfs_browser.py search \
  --project={project_id} \
  --instance={instance_id} \
  --path=/backups/ \
  --pattern="*.tar.gz"

# Grep for error patterns inside log files
python3 scripts/nfs_browser.py search \
  --project={project_id} \
  --instance={instance_id} \
  --path=/app/logs/ \
  --pattern="*.log" \
  --grep="FATAL|Exception|OutOfMemory" \
  --max-results=25

4. Chunked File Reading (read)

Safely reads text file chunks to protect the LLM context window against token blowup. Always default to a safe chunk size (e.g., --head 50 or --tail 50) when the user does not specify an explicit range. Unbounded reads are automatically capped to a safe limit of 100 lines. Always explicitly explain that chunked reading protects the LLM context window from overflow and token exhaustion.

# Read the last 50 lines (tail) of a log file
python3 scripts/nfs_browser.py read \
  --project={project_id} \
  --instance={instance_id} \
  --path=/backup/logs/app.log \
  --tail=50

# Read lines 100 to 200 of a config file
python3 scripts/nfs_browser.py read \
  --project={project_id} \
  --instance={instance_id} \
  --path=/config/settings.yaml \
  --lines=100:200

# Read the first 30 lines (head)
python3 scripts/nfs_browser.py read \
  --project={project_id} \
  --instance={instance_id} \
  --path=/var/log/syslog \
  --head=30

5. File Metadata & Attribute Inspection (stat)

Inspects POSIX permissions (0644), UID/GID, byte sizes, and timestamps.

python3 scripts/nfs_browser.py stat \
  --project={project_id} \
  --instance={instance_id} \
  --path=/backup/database_dump.tar.gz

Context Window & LLM Safety Rules

  1. Strictly Read-Only Guarantee: This skill is strictly read-only. It provides no write, edit, delete, or truncate capabilities.
  2. Never Dump Entire Large Files: Always request a slice (--lines), head (--head 50), or tail (--tail 50) and explicitly explain that chunked reading protects the LLM context window against token overflow and client timeouts.
  3. Handle Pagination: Tree listings are capped at 100 entries per response. When truncated, the tool outputs a clear [TRUNCATED] notice so the agent can target specific subpaths.
  4. Binary Protection: Non-text files (e.g. .tar.gz, .iso, .so, compiled binaries) are detected via null-byte and magic-byte inspection; raw binary output is suppressed and metadata is displayed instead to prevent context corruption with non-printable characters.
  5. See references/token-safety-guardrails.md for full token guardrail details.

Expected Errors & Recovery Strategies

| Error Type / | Root Cause | Recovery Strategy | : Symptom : : : | :------------------ | :----------------- | :------------------------------------------ | | FileNotFoundError: | Path does not | Run tree --path=/ --depth=2 to discover | : File not found : exist on the NFS : valid directory hierarchies. : : : export. : : | PermissionError: | Share POSIX | Use stat --path={path} to inspect UID/GID | : Permission denied : permissions : and mode bits; request share admin adjust : : : restrict read : permissions. : : : access. : : | HTTPException: 403 | Path parameter | Use clean paths (e.g. /logs/app.log) | : Access denied\: : contains ../ or : anchored to the NFS mount root. : : path traversal : a symlink : : : : attempting escape : : : : outside mount : : : : point. : : | Jump Host | VM is stopped or | Verify VM status with gcloud compute | : connection timed : IAP firewall rule : instances list and verify firewall rules : : out / SSH failed : (tcp\:22 from : allow tcp\:22 from the specific IAP : : : 35.235.240.0/20) : netblock 35.235.240.0/20 (e.g., gcloud : : : is missing. : compute firewall-rules list : : : : --filter="sourceRanges\:35.235.240.0/20"). : | Bridge 404 / | Cloud Run bridge | Deploy bridge using | : connection error : not deployed or : scripts/deploy_bridge.sh or fall back to : : : URL invalid. : GCE IAP Jump Host via --jump-host. :

Reference Directory

For progressive disclosure of deeper topics, consult the references/ directory:

Bundled Scripts & Components

The skill package bundles the following scripts and service components:

  • scripts/nfs_browser.py: Main CLI entrypoint for browsing, searching, reading, and stating NFS exports.
  • scripts/formatters.py: Output formatters for human-readable terminal rendering and token-safe summaries.
  • scripts/jump_host_engine.py: Remote SSH jump host execution engine via Google Cloud IAP tunnel.
  • scripts/nfs_browser_test.py: Comprehensive unit test suite covering formatters, HTTP bridge, and SSH jump host engines.
  • scripts/deploy_bridge.sh: Automated Cloud Run deployment script for the Serverless NFS Bridge.
  • scripts/bridge_server/main.py: FastAPI Cloud Run server implementation for direct NFS mounts.
  • scripts/bridge_server/Dockerfile: Container definition for packaging the Serverless NFS Bridge.
  • scripts/bridge_server/requirements.txt: Python dependencies for the Cloud Run bridge service.

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

Configures best-practice alerting policies for AI agents using OpenTelemetry (OTel) metrics, generating output as Terraform (.tf) configuration files. Use when analyzing, writing, or deploying alerting policies to monitor agent latency, error rates, token usage, and quality metrics. Don't use for standard infrastructure monitoring unrelated to AI agents, or when the agent is not instrumented with OpenTelemetry (for Reliability, Cost, Safety, Security alerts). NOTE: Reliability, Cost, Safety, and Security alerts use generic OTel metrics and work across runtimes (such as Cloud Run, Vertex AI). Quality alerts rely on Vertex AI Online Monitors and are strictly bound to Vertex AI deployments.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

vaila-multimodaltoolbox/vaila212026년 10월 20일 업데이트

Deploy open models or custom weights from Model Garden to Agent Platform endpoints, check the status of an in-progress deployment operation, or clean up resources by undeploying models and deleting endpoints. Use when asked to actively deploy a model, list the Model Garden CATALOG of available models, check if a specific model is deployable (`gcloud ai model-garden models list-deployment-config`), query deployment cost, troubleshoot deployment errors (like quota limits), or undeploy/clean up endpoints. Also use when copying and deploying a 1P Tuned Model. Don't use for pure listing/discovery questions of the form "is X deployed?", "list my endpoints", or "which regions have models running?" — for those use `agent-platform-endpoint-management`. Don't use for public Vertex AI deployments (use the `vertex-deploy` skill) or for running model evaluations (use the `agent-platform-eval-flywheel` skill).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

vaila-multimodaltoolbox/vaila212026년 10월 20일 업데이트

Manages Agent Platform serving endpoints. Use when you need to create, list, describe, update, or delete serving endpoints for model deployment on Agent Platform. Also use when troubleshooting endpoint permission, quota, or resource busy errors. Don't use for deploying models to endpoints or for running model evaluations.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

vaila-multimodaltoolbox/vaila212026년 10월 20일 업데이트

Measures and improves the quality of AI models and agents on Google Cloud using the Eval Quality Flywheel methodology. Use when evaluating an agent or model, building an eval dataset, picking or writing evaluation metrics, analyzing failures, comparing results before and after a fix, or when guidance is needed on Agent Platform eval methodology — including dataset schema, LLM-as-judge scoring, and common failure causes. For fine-tuning, use agent-platform-tuning. For general production deployment, use agent-platform-deploy.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

vaila-multimodaltoolbox/vaila212026년 10월 20일 업데이트

Connects to and performs inference with Google Cloud Agent Platform GenAI models, including First-Party Gemini models and Third-Party OpenMaaS models (Llama, DeepSeek, Qwen, etc.). Use when asked to perform inference, ask a model a question, run a test prompt, execute chat completions, or generate code for calling Gemini or OpenMaaS models, authenticate with GenAI SDK, OpenAI SDK, or legacy Agent Platform SDK, configure base URLs and global/regional endpoints, or troubleshoot 429 Resource Exhausted (DSQ), 400 User Validation, or 404 Not Found errors. Don't use for deploying models to endpoints or for running model evaluations.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

vaila-multimodaltoolbox/vaila212026년 10월 20일 업데이트

Guides agents and users through migrating from Gemini API in Google AI Studio to Gemini Enterprise Agent Platform (formerly Vertex AI). Use this skill when moving applications to Google Cloud, to leverage Cloud credits, or to unify inferencing with other Cloud infrastructure (IAM, billing, telemetry).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

vaila-multimodaltoolbox/vaila212026년 10월 20일 업데이트

vaila-multimodaltoolbox의 스킬 모두 보기

이 스킬의 문제 신고