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k8s-yaml-generatorKubernetes YAML 生成器

Agent Skill

k8s-yaml-generator 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

3,528

周安装

150

GitHub Stars

197

下载量

1,236
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:k8s-yaml-generator(Kubernetes YAML 生成器)
来源仓库:https://github.com/akin-ozer/cc-devops-skills
仓库路径:skills/k8s-yaml-generator
安装命令:
npx skills add https://github.com/akin-ozer/cc-devops-skills --skill k8s-yaml-generator
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/akin-ozer/cc-devops-skills --skill k8s-yaml-generator

简介

自动生成 Kubernetes YAML 配置文件,支持常见资源类型模板。

  • 适用于 Codex、Claude、Cursor、Gemini CLI 中的部署脚本编写。
  • 通过 GitHub 仓库安装,使用 npx skills add 命令添加技能。
  • 生成内容需人工复核字段准确性和环境适配性。
  • 建议从现有代码或文档中提取真实参数,避免虚构配置。

SKILL.md

Kubernetes YAML Generator

Generate Kubernetes manifests with deterministic steps, bounded CRD research, and mandatory validation for full-resource output.

Trigger Guidance

Use this skill when the user asks to create or update Kubernetes YAML, for example:

  • "Generate a Deployment + Service manifest for my app."
  • "Create an Argo CD Application CRD."
  • "Write a StatefulSet with PVC templates."
  • "Produce production-ready Kubernetes YAML with best practices."

Do not use this skill for validation-only requests. For validation-only work, use k8s-yaml-validator.

Execution Model

Normative keywords:

  • MUST: required
  • SHOULD: default unless user requests otherwise
  • MAY: optional

Deterministic sequence:

  1. Preflight request and path/rendering sanity.
  2. Capture minimum required inputs.
  3. Resolve CRD references (bounded workflow only when CRD/custom API is involved).
  4. Generate YAML with baseline quality checks.
  5. Run mandatory validation (or documented fallback path when tooling is unavailable).
  6. Deliver YAML plus explicit validation report and assumptions.

If one step is blocked by environment constraints, execute that step's fallback and continue.

1) Preflight

Before generation:

  • Confirm whether output is full manifest(s) or snippet-only.
  • Confirm target Kubernetes version when provided.
  • Verify any referenced local file path exists before using it.
  • Normalize resource naming to DNS-1123-compatible names where applicable.

Preflight stop condition:

  • If required core inputs are missing (resource type, workload image for Pod-based resources, or CRD kind/apiVersion), ask for those first.

2) Capture Required Inputs

Collect:

  • Resource types (Deployment, Service, ConfigMap, CRD kind, etc.)
  • apiVersion + kind
  • Namespace/scoping requirements
  • Ports, replicas, images, probes, storage, and secret/config needs
  • Environment assumptions (dev/staging/prod)
  • For CRDs: project name and target CRD version if known

Safe defaults (state explicitly in output):

  • Namespace: default (namespace-scoped resources)
  • Deployment replicas: 2
  • Service type: ClusterIP
  • Image pull policy: IfNotPresent (unless user needs forced pulls)

3) CRD Lookup Workflow (Bounded)

Run this step only for custom APIs outside Kubernetes built-in groups.

3.1 Identify CRD target

Extract:

  • API group, version, kind (for example argoproj.io/v1alpha1, Application)
  • Requested product/version (for example Argo CD v2.9.x)

3.2 Context7 primary path

Use the correct Context7 tools and payloads:

  1. mcp__context7__resolve-library-id
  2. mcp__context7__query-docs

Sample payloads:

Tool: mcp__context7__resolve-library-id
libraryName: "argo-cd"
query: "Find Argo CD documentation for Application CRD schema compatibility"
Tool: mcp__context7__query-docs
libraryId: "/argoproj/argo-cd/v2.9.0"
query: "Application CRD required spec fields for apiVersion argoproj.io/v1alpha1 with minimal valid example"

Selection rules:

  • Prefer exact project/library name matches.
  • Prefer versioned libraryId when user specifies a version.
  • Otherwise use unversioned ID and note version uncertainty.

3.3 Thresholds and stop conditions

Bound the lookup to prevent unbounded retries:

  • resolve-library-id: max 2 attempts (primary name + one alternate name).
  • query-docs: max 3 focused queries total.
  • Web fallback: max 2 version-specific searches.

Stop early when all are true:

  • Required CRD fields are identified.
  • At least one authoritative example is found.
  • Version compatibility is known or explicitly marked unknown.

Hard stop when budgets are exhausted:

  • Generate only fields verified by sources.
  • Mark remaining fields as Needs confirmation.
  • Report residual risk and request one of:

- exact CRD docs URL, or - cluster introspection output (for example kubectl explain <kind>.spec when available).

3.4 Fallback order

Use this order:

  1. Context7 (resolve-library-id -> query-docs)
  2. Official project docs via web search
  3. Cluster-local introspection (kubectl explain, if cluster access exists)

If none are available, provide a minimal, clearly marked draft and do not claim full CRD correctness.

4) YAML Generation Rules

Apply these checks:

  • Use explicit, non-deprecated API versions.
  • Include consistent labels (app.kubernetes.io/*) across related resources.
  • Include namespace for namespace-scoped resources.
  • Add resource requests/limits for Pod workloads unless user opts out.
  • Add readiness/liveness probes for long-running services where applicable.
  • Use securityContext to avoid root execution by default.
  • Keep multi-resource ordering dependency-safe (for example ConfigMap before Deployment consumers).

Minimal label baseline:

labels:
  app.kubernetes.io/name: myapp
  app.kubernetes.io/instance: myapp-prod
  app.kubernetes.io/part-of: myplatform
  app.kubernetes.io/managed-by: codex

5) Mandatory Validation and Contingencies

For full manifest generation, validation is mandatory.

Primary path:

  • Invoke k8s-yaml-validator.
  • Iterate fix -> revalidate until blocking issues are gone.

Required reporting after each validation pass:

  • Validation mode: k8s-yaml-validator | script fallback | manual fallback
  • Syntax: pass/fail
  • Schema: pass/fail/partial
  • CRD check: pass/fail/partial
  • Dry-run: server/client/skipped
  • Blocking issues remaining: yes/no

Contingency A: validator skill unavailable

Run direct commands:

bash devops-skills-plugin/skills/k8s-yaml-validator/scripts/setup_tools.sh
yamllint -c devops-skills-plugin/skills/k8s-yaml-validator/assets/.yamllint <file.yaml>
kubeconform -schema-location default -strict -ignore-missing-schemas -summary <file.yaml>
server_out="$(mktemp)"
client_out="$(mktemp)"
trap 'rm -f "$server_out" "$client_out"' EXIT

if kubectl apply --dry-run=server -f <file.yaml> >"$server_out" 2>&1; then
  echo "server_validation=passed"
elif grep -Eqi "connection refused|no such host|i/o timeout|tls handshake timeout|unable to connect to the server|no configuration has been provided|the server doesn't have a resource type" "$server_out"; then
  echo "server_validation=skipped"
  if kubectl apply --dry-run=client -f <file.yaml> >"$client_out" 2>&1; then
    echo "client_validation=passed"
  else
    echo "client_validation=failed"
    cat "$client_out"
    exit 1
  fi
else
  echo "server_validation=failed"
  cat "$server_out"
  exit 1
fi

Contingency B: local tools partially unavailable

  • Run available checks.
  • Record skipped checks explicitly.
  • Add residual risk for every skipped check.

Contingency C: repeated validation failure

  • Maximum 3 fix/revalidate cycles.
  • If still failing, stop and return:

- current YAML, - exact failing errors, - smallest required user decision/input to unblock.

Validation exceptions:

  • Snippet-only or docs-only requests MAY skip full validation, but the output MUST state Validation status: Skipped (reason).

6) Delivery Contract

Final output MUST include:

  1. Generated YAML.
  2. What was generated (resource list, namespace/scoping).
  3. Validation report in the required format.
  4. Assumptions and defaults used.
  5. References used:

- Context7 IDs/queries used (for CRDs) - external docs/searches used - items skipped/missing and impact

Suggested next commands:

kubectl apply -f <filename>.yaml
kubectl get <resource-type> <name> -n <namespace>
kubectl describe <resource-type> <name> -n <namespace>

7) Canonical Example Flows

Example A: Built-in resources (Deployment + Service)

  1. Capture app image, ports, replicas, namespace.
  2. Generate Deployment and Service with consistent labels/selectors.
  3. Validate with k8s-yaml-validator.
  4. Return YAML + validation report + assumptions.

Example B: CRD resource (Argo CD Application)

  1. Extract argoproj.io/v1alpha1 + Application.
  2. Run bounded Context7 lookup (resolve-library-id then query-docs).
  3. If needed, perform bounded web fallback.
  4. Generate CRD YAML only with verified fields.
  5. Validate, report any partial verification, and return residual risks.

8) Definition of Done

Execution is complete only when all applicable checks pass:

  • Trigger use case is correct (generation, not validation-only).
  • Required inputs are captured or explicit assumptions are documented.
  • CRD lookup follows bounded thresholds and stop conditions.
  • Tool names and command paths are valid and consistent.
  • Full manifests are validated (or fallback path is documented with residual risk).
  • Final response includes YAML, validation report, assumptions, and references.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

34.34%
按下载量换算424

Claude

29.89%
按下载量换算369

Cursor

19.62%
按下载量换算243

Gemini CLI

8.89%
按下载量换算110

安全审计

Gen Agent Trust Hub

通过

Socket

可疑

Snyk

可疑

权限和风险

external-service

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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