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deploy部署

Agent Skill

用于辅助云资源、部署、容器、基础设施和运维自动化任务。它适合让 Agent 检查配置、整理部署步骤、分析资源状态、生成排障思路或辅助云服务接入。使用时需要明确目标环境、账号权限、区域和资源组,区分本地测试与生产操作;涉及删除资源、重启服务、修改网络或权限配置时,应先确认影响范围。

总安装

441

周安装

18

GitHub Stars

13

下载量

143
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:deploy(部署)
来源仓库:https://github.com/truefoundry/tfy-agent-skills
仓库路径:skills/deploy
安装命令:
npx skills add https://github.com/truefoundry/tfy-agent-skills --skill deploy
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/truefoundry/tfy-agent-skills --skill deploy

简介

deploy 用于辅助云资源、部署、容器和基础设施的运维自动化任务,帮助检查配置和分析资源状态。

  • 适用于云环境部署、资源管理和运维排障的场景。
  • 通过 npx skills add 命令从 GitHub 仓库安装使用。
  • 使用时需明确目标环境和权限,涉及生产操作时应先确认影响范围。
  • deploy 属于前端设计类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Routing note: For ambiguous user intents, use the shared clarification templates in references/intent-clarification.md.

Deploy to TrueFoundry

Route user intent to the right deployment workflow. Load only the references you need.

Intent Router

Gateway configs are NOT services. If the user says "apply" or "deploy" in the context of a gateway YAML (type: gateway-*, type: provider-account/*), run tfy apply -f <file> and test the endpoint. Do not build Docker images, create secrets, or spin up new services.
User IntentActionReference
"apply gateway config", "apply gateway yaml", "tfy apply gateway", "deploy gateway"Gateway manifest apply — use tfy apply -f <file> directly; do NOT build a service imageInline: tfy apply -f gateway.yaml
"deploy", "deploy my app", "ship this"Single HTTP servicedeploy-service.md
"attach this deployment to mcp gateway", "register deployed mcp service", "connect deployment to mcp gateway"Post-deploy MCP registrationUse mcp-servers skill after deployment endpoint is known
"mount this file", "mount config file", "mount certificate file", "mount key file"Single service with file mounts (no image rebuild)deploy-service.md
"tfy apply", "apply manifest", "deploy from yaml"Declarative manifest applydeploy-apply.md
"deploy everything", "full stack", docker-compose, "docker-compose.yaml", "compose.yaml"Multi-service: use compose as source of truthdeploy-multi.md + compose-translation.md
"async service", "queue consumer", "worker"Async/queue servicedeploy-async.md
"deploy LLM", "serve model"Model serving intent (may be ambiguous)Ask user: dedicated model serving (llm-deploy) or generic service deploy (deploy)
"deploy helm chart"Helm chart intentConfirm Helm path and collect chart details, then proceed with helm workflow
"deploy postgres docker", "dockerized postgres", "deploy redis docker", "database in docker/container"Containerized database intentProceed with deploy workflow (do not route to Helm)
"deploy database", "deploy postgres", "deploy redis"Ambiguous infra intentAsk user: Helm chart (helm) or containerized service (deploy)

Load only the reference file matching the user's intent. Do not preload all references.

Prerequisites (All Workflows)

# 1. Check credentials
grep '^TFY_' .env 2>/dev/null || true
env | grep '^TFY_' 2>/dev/null || true

# 2. Derive TFY_HOST for CLI (MUST run before any tfy command)
export TFY_HOST="${TFY_HOST:-${TFY_BASE_URL%/}}"

# 3. Check CLI
tfy --version 2>/dev/null || echo "Install: pip install 'truefoundry==0.5.0'"

# 4. Check for existing manifests
ls tfy-manifest.yaml truefoundry.yaml 2>/dev/null
  • TFY_BASE_URL and TFY_API_KEY must be set (env or .env).
  • TFY_HOST must be set before any tfy CLI command. The export above handles this automatically.
  • TFY_WORKSPACE_FQN required. HARD RULE: Never auto-pick a workspace. Always ask the user to confirm, even if only one workspace exists or a preference is saved. See references/prerequisites.md for the full workspace confirmation flow.
  • For full credential setup, see references/prerequisites.md.
WARNING: Never use source.env. The tfy-api.sh script handles .env parsing automatically. For shell access: grep KEY.env | cut -d= -f2-

Quick Ops (Inline)

Apply a manifest (most common)

# tfy CLI expects TFY_HOST when TFY_API_KEY is set
export TFY_HOST="${TFY_HOST:-${TFY_BASE_URL%/}}"

# Preview changes
tfy apply -f tfy-manifest.yaml --dry-run --show-diff

# Apply
tfy apply -f tfy-manifest.yaml

Deploy from source (local code or git)

# tfy CLI expects TFY_HOST when TFY_API_KEY is set
export TFY_HOST="${TFY_HOST:-${TFY_BASE_URL%/}}"

# tfy deploy builds remotely — use for local code or git sources
tfy deploy -f truefoundry.yaml --no-wait
tfy apply does NOT support build_source. Use tfy deploy -f for source-based deployments.

Minimal service manifest template

name: my-service
type: service
image:
  type: image
  image_uri: docker.io/myorg/my-api:v1.0
ports:
  - port: 8000
    expose: true
    app_protocol: http
resources:
  cpu_request: 0.5
  cpu_limit: 1
  memory_request: 512
  memory_limit: 1024
  ephemeral_storage_request: 1000
  ephemeral_storage_limit: 2000
env:
  LOG_LEVEL: info
replicas: 1
workspace_fqn: "WORKSPACE_FQN_HERE"

Check deployment status

TFY_API_SH=~/.claude/skills/truefoundry-deploy/scripts/tfy-api.sh
bash $TFY_API_SH GET '/api/svc/v1/apps?workspaceFqn=WORKSPACE_FQN&applicationName=SERVICE_NAME'

Or use the applications skill.

Post-Deploy Verification (Automatic)

After any successful deploy/apply action, verify deployment status automatically without asking an extra prompt.

Preferred verification path:

  1. Use MCP tool call first:
tfy_applications_list(filters={"workspace_fqn": "WORKSPACE_FQN", "application_name": "SERVICE_NAME"})
  1. If MCP tool calls are unavailable, fall back to:
TFY_API_SH=~/.claude/skills/truefoundry-deploy/scripts/tfy-api.sh
bash $TFY_API_SH GET '/api/svc/v1/apps?workspaceFqn=WORKSPACE_FQN&applicationName=SERVICE_NAME'

Always report the observed status (BUILDING, DEPLOYING, DEPLOY_SUCCESS, DEPLOY_FAILED, etc.) in the same response.

If status is DEPLOY_FAILED or BUILD_FAILED, follow deploy-debugging.md: fetch logs (use logs skill), identify cause, apply one fix and retry once; if still failed, report to user with summary and log excerpt and stop.

Optional Post-Deploy: Attach to MCP Gateway

If the deployed service exposes an MCP endpoint, ask if the user wants to register it in MCP gateway right away.

Handoff checklist to mcp-servers skill:

  • deployment/service name
  • endpoint URL (https://.../mcp or in-cluster URL)
  • transport (streamable-http or sse)
  • auth mode (header, oauth2, or passthrough)

REST API fallback (when CLI unavailable)

See references/cli-fallback.md for converting YAML to JSON and deploying via tfy-api.sh.

Auto-Detection: Single vs Multi-Service

Before creating any manifest, scan the project:

  1. Check for docker-compose.yml, docker-compose.yaml, or compose.yaml first. If present (or user mentions docker-compose), treat it as the primary source of truth: load deploy-multi.md and compose-translation.md, generate manifests from the compose file, wire services per service-wiring.md, then complete deployment. Do not ask the user to manually create manifests when a compose file exists.
  2. Look for multiple Dockerfile files across the project
  3. Check for service directories with their own dependency files in services/, apps/, frontend/, backend/
  • Compose file present or user says "docker-compose" → Multi-service from compose: load deploy-multi.md + compose-translation.md
  • Single service → Load references/deploy-service.md
  • Multiple services (no compose) → Load references/deploy-multi.md

Secrets Handling (Default: Secret Groups)

By default, do not put secrets in env as raw values. For any env var that looks sensitive (e.g. *PASSWORD*, *SECRET*, *TOKEN*, *KEY*, *API_KEY*, *DATABASE_URL* with credentials):

  1. Create a secret group (use the secrets skill or API) with those keys.
  2. Reference them in the manifest with tfy-secret:// format.
env:
  LOG_LEVEL: info                                              # plain text OK
  DB_PASSWORD: tfy-secret://my-org:my-service-secrets:DB_PASSWORD  # sensitive

Pattern: tfy-secret://<TENANT_NAME>:<SECRET_GROUP_NAME>:<SECRET_KEY> where TENANT_NAME is the subdomain of TFY_BASE_URL.

Use the secrets skill for guided secret group creation. For the full workflow, see references/deploy-service.md (Secrets Handling section).

File Mounts (Config, Secrets, Shared Data)

When users ask to mount files into a deployment, prefer manifest mounts over Dockerfile edits:

  • type: secret for sensitive file content (keys, certs, credentials)
  • type: config_map for non-sensitive config files
  • type: volume for writable/shared runtime data

See references/deploy-service.md (File Mounts section) for the end-to-end workflow.

Shared References

These references are available for all workflows — load as needed:

ReferenceContents
manifest-schema.mdComplete YAML field reference (single source of truth)
manifest-defaults.mdPer-service-type defaults with YAML templates
cli-fallback.mdCLI detection and REST API fallback pattern
cluster-discovery.mdExtract cluster ID, base domains, available GPUs
resource-estimation.mdCPU, memory, GPU sizing rules of thumb
health-probes.mdStartup, readiness, liveness probe configuration
gpu-reference.mdGPU types and VRAM reference
container-versions.mdPinned container image versions
prerequisites.mdCredential setup and.env configuration
rest-api-manifest.mdFull REST API manifest reference

Workflow-Specific References

ReferenceUsed By
deploy-api-examples.mddeploy-service
deploy-errors.mddeploy-service
deploy-scaling.mddeploy-service
load-analysis-questions.mddeploy-service
codebase-analysis.mddeploy-service
tfy-apply-cicd.mddeploy-apply
tfy-apply-extra-manifests.mddeploy-apply
compose-translation.mddeploy-multi
dependency-graph.mddeploy-multi
multi-service-errors.mddeploy-multi
multi-service-patterns.mddeploy-multi
service-wiring.mddeploy-multi
deploy-debugging.mdAll deploy/apply (when status is failed)
async-errors.mddeploy-async
async-queue-configs.mddeploy-async
async-python-library.mddeploy-async
async-sidecar-deploy.mddeploy-async

Composability

  • Find workspace: Use workspaces skill
  • Check what's deployed: Use applications skill
  • View logs: Use logs skill
  • Manage secrets: Use secrets skill
  • Deploy Helm charts: Use helm skill
  • Deploy LLMs: Use llm-deploy skill
  • Register deployment in MCP gateway: Use mcp-servers skill
  • Test after deploy: Use service-test skill

Success Criteria

  • User confirmed service name, resources, port, and deployment source before deploying
  • Deployment URL and status reported back to the user
  • Deployment status verified automatically immediately after apply/deploy (no extra prompt)
  • Health probes configured for production deployments
  • Secrets stored securely (not hardcoded in manifests)
  • For multi-service: all services wired together and working end-to-end

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

35.49%
按下载量换算51

Claude

28.97%
按下载量换算41

Cursor

17.59%
按下载量换算25

Gemini CLI

8.24%
按下载量换算12

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/truefoundry/tfy-agent-skills --skill deploy 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

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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