본문으로 건너뛰기
cccskills
무료GitHub에 공개

iam-helper-for-policy-management

Streamlines the creation, modification, and management of IAM allow policies (v1) and deny policies (v2). Manages access control across Resource Manager resources (Organization, Folder, Project) and individual resources. Use when creating, updating, listing, or deleting IAM allow policies or deny policies. Don't use for access denial troubleshooting (use iam-helper-for-troubleshooting), temporary privileged access (use iam-helper-for-privileged-access-management), configuring VPC Service Controls, or managing network firewall rules.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

연동 도구Google Cloud
설치 방법 보기

포함된 파일(3)

  • SKILL.md5.3 KB
  • references/v1-allow-policies.md6.1 KB
  • references/v2-deny-policies.md7.6 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

IAM Helper for Policy Management

Orchestrates the lifecycle and management of IAM allow and deny policies across IAM v1 (allow policies) and IAM v2 (deny policies).


Core Concepts & Paradigms

IAM operates across two policy paradigms:

  1. IAM v1 (Allow Policies): Grants roles to principals (users, service accounts, groups, domains) on specific resources. Supports Resource Manager resources (organizations, folders, projects) as well as individual resources across supported Google Cloud services.
  2. IAM v2 (Deny Policies): Sets explicit organization-, folder-, or project-level guardrails that prevent specified principals from using designated permissions, regardless of any allow policies granted. Evaluated before allow policies.

Workflow & Decision Tree

When receiving a policy management request, determine whether the operation is Read-Only or Mutating, and whether it targets IAM v1 (Allow Policies) or IAM v2 (Deny Policies):

1. Read-Only Operations (Autonomous Execution)

Read-only actions include the following:

  • IAM v1 Allow Policies: get-iam-policy on project/folder/organization, or gcloud iam list-testable-permissions //cloudresourcemanager.googleapis.com/projects/PROJECT_ID.
  • IAM v2 Deny Policies: gcloud iam policies list or gcloud iam policies get with --attachment-point and --kind=denypolicies.

For read-only actions, execute the command autonomously to inspect state, and present the query results clearly to the user.

2. Mutating Operations (Plan & Confirm Protocol)

Mutating operations include the following:

  • IAM v1 Allow Policies: add-iam-policy-binding, remove-iam-policy-binding, or set-iam-policy across project, folder, organization, or resource levels (see references/v1-allow-policies.md).
  • IAM v2 Deny Policies: create, update, or delete deny policies on attachment points (cloudresourcemanager.googleapis.com/projects/PROJECT_ID, cloudresourcemanager.googleapis.com/folders/FOLDER_ID, or cloudresourcemanager.googleapis.com/organizations/ORG_ID) using YAML/JSON policy files (see references/v2-deny-policies.md).

For mutating operations, follow the Plan & Confirm Protocol below. DO NOT execute mutating commands autonomously without prior user approval.


Execution & Safety Protocol

  • Plan and Confirm (No Autonomous Mutation): Mutating allow and deny policy changes modify live security perimeters and access controls. You MUST NOT execute mutating gcloud commands directly via tool calls without explicit prior confirmation from the user. When asked to apply a mutating change, do the following:
    1. Formulate the Command: Generate the exact, fully constructed gcloud command (including all parameters such as --member, --role, --attachment-point, --kind=denypolicies, and --policy-file).
    2. Warn of Impact & Propagation: Issue a general warning that the change could impact access in a live environment and takes time to propagate across Google Cloud global infrastructure.
    3. Request User Confirmation: Prompt the user for approval before applying the changes to the live environment.
  • Post-Execution Verification: After the user approves and the mutating policy change is executed, run the corresponding verification command (see references/v1-allow-policies.md and references/v2-deny-policies.md for exact verification steps) to verify that the active state matches expectations before reporting completion.
  • Security Guardrail (Public & Blanket Access Refusal): Never grant allUsers or allAuthenticatedUsers basic roles (roles/owner, roles/editor, roles/viewer, roles/admin, roles/writer, and roles/reader) or broad permissions. Explicitly refuse blanket public access requests, explain the severe security risks of public project ownership/access, and propose scoped, least-privileged role bindings for specific authenticated identities instead.

Supporting Links

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 23일 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 23일 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 23일 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 23일 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 23일 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 23일 업데이트

vaila-multimodaltoolbox의 스킬 모두 보기

이 스킬의 문제 신고