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Agent skill

Cloudbase Declarative Deploy

by TencentCloudBase in TencentCloudBase/CloudBase-AI-Toolkit

CloudBase declarative deployment from a cloudbaserc config (声明式部署, 配置式部署, cloudbaserc 部署) through the deployBuild / deployPlan / deployApply MCP tools.

MITAuto-check passedDevOps & Cloud

Install Cloudbase Declarative Deploy

skills CLI
$ npx skills add TencentCloudBase/CloudBase-AI-Toolkit --skill cloudbase-declarative-deploy -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install TencentCloudBase/CloudBase-AI-Toolkit cloudbase-declarative-deploy --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/TencentCloudBase/CloudBase-AI-Toolkit.git skills-src && mkdir -p .claude/skills && cp -r skills-src/config/source/skills/cloudbase-declarative-deploy .claude/skills/cloudbase-declarative-deploy && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
cloudbase-declarative-deploy
GitHub stars
1.1k
Used in
1 other repo
Token cost
~2.6k tokens
SKILL.md length
1,123 words
Files
4 (incl. references)
Skills in repo
49
Repo updated
First seen
Licence
MIT

At a glance

CloudBase declarative deployment from a cloudbaserc config (声明式部署, 配置式部署, cloudbaserc 部署) through the deployBuild / deployPlan / deployApply MCP tools.

  • Works in 3 steps: queryApps(action=getUploadUrl) to get… → Upload source/build zip to uploadUrl… → manageApps(action=deployApp,…
  • Deploying database
  • SKILL.md covers Sibling skills (local only), When to use this skill, Do NOT use for and Cloud mode, plus 8 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Cloudbase Declarative Deploy is an agent skill from TencentCloudBase/CloudBase-AI-Toolkit. CloudBase declarative deployment from a cloudbaserc config (声明式部署, 配置式部署, cloudbaserc 部署) through the deployBuild / deployPlan / deployApply MCP tools. Use when deploying database, functions, app, hosting, or gateway resources described in cloudbaserc.json/yaml as a single desired-state config, when a user wants to build static hosting artifacts locally first (deployBuild), or wants a dry-run plan before applying (deployPlan), or when handling multi-environment deploys via mode / envOverrides. Covers…

Its SKILL.md is about 2.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including reference files (for example `references/config-schema.md`, `references/multi-env.md` and `references/plan-and-apply.md`).

It sits in DevOps & Cloud, covering Deployment, MCP servers and GitOps. The repository describes itself as: Backend for AI coding agents on CloudBase — database, auth, functions via Plugin, Skills & MCP. The licence is MIT.

When your agent uses it

  • Deploying database
  • Gateway resources described in cloudbaserc.json/yaml as a single desired-state config
  • A user wants to build static hosting artifacts locally first (deployBuild)
  • Wants a dry-run plan before applying (deployPlan)

Example prompts

  • “/cloudbase-declarative-deploy”

Workflow steps

3 steps, taken from the first numbered list in SKILL.md.

  1. queryApps(action=getUploadUrl) to get uploadUrl, uploadHeaders, unixTimestamp
  2. Upload source/build zip to uploadUrl with returned headers
  3. manageApps(action=deployApp, cosTimestamp=, installCmd?, buildCmd?, deployCmd?)

What it can do on your machine

Read from SKILL.md and the folder at commit ea2c202. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Cloudbase Declarative Deploy loads about 2.6k tokens when it runs, and up to ~6.4k if it reads all its reference files. Until then it costs about 230 tokens; SKILL.md has 1,123 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~230
When it runs · the whole SKILL.md, loaded when a task matches
~2.6k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~6.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from TencentCloudBase/CloudBase-AI-Toolkit at commit ea2c202, republished under its MIT licence (© TencentCloudBase). 1,123 words, ~2,620 tokens.

Download SKILL.mdSave it as .claude/skills/cloudbase-declarative-deploy/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
cloudbase-declarative-deploy
description
CloudBase declarative deployment from a cloudbaserc config (声明式部署, 配置式部署, cloudbaserc 部署) through the deployBuild / deployPlan / deployApply MCP tools. Use when deploying database, functions, app, hosting, or gateway resources described in cloudbaserc.json/yaml as a single desired-state config, when a user wants to build static hosting artifacts locally first (deployBuild), or wants a dry-run plan before applying (deployPlan), or when handling multi-environment deploys via mode / envOverrides. Covers build-plan-apply flow (deployBuild local build → deployPlan dry-run → deployApply confirm=true), hosting build-output neutralization, envId resolution priority, only/skip filtering, concurrency, and continueOnError. Prefer deployBuild (when hosting declares a buildCommand) and deployPlan before deployApply; do not confuse with per-resource tcb CLI deploy or single-function deploy.
version
2.34.8
alwaysApply
false

CloudBase Declarative Deploy

Deploy a whole CloudBase project from one cloudbaserc config as desired state, using the deployBuild (local hosting build), deployPlan (dry-run) and deployApply (apply) MCP tools. The orchestrator applies resources in a fixed dependency order:

database → functions → app → hosting → gateway

Sibling skills (local only)

Sibling CloudBase skills ship beside this skill. Use local relative paths such as ../cloudbase-cli/SKILL.md.

Cloud-hosted MCP mode does not guarantee access to a local workspace filesystem or stable relative paths. If a referenced sibling file is not available in cloud mode, use this skill's embedded guidance as source of truth and ask the user for any missing constraints (or to install the missing skill). Do not HTTP-fetch remote skill or protocol markdown into the agent context.

Cross-cutting protocols (required before applying any deploy):

  • Change Safety Protocol: ../cloudbase-platform/references/protocols/change-safety-protocol.md
  • Deployment Gate: ../cloudbase-platform/references/protocols/deployment-gate.md

When to use this skill

  • The project has a cloudbaserc.json / .yaml / .yml / .js describing multiple resources, and the user wants to deploy them together as one config.
  • The user asks for 声明式部署 / 配置式部署 / "deploy from cloudbaserc" / "deploy the whole project".
  • The user wants to preview what a deploy will change before applying (dry-run plan).
  • The user wants to build the static hosting artifact locally first (declarative hosting deploys no longer build implicitly — see deployBuild).
  • Multi-environment deploy: production/staging via mode + envOverrides.

Do NOT use for

  • Deploying a single cloud function or one static site via tcb CLI → ../cloudbase-cli/SKILL.md.
  • In-app SDK integration (web/miniprogram/node) → the matching SDK skill.
  • Console UI operations.

Cloud mode

deployBuild / deployPlan / deployApply in this skill are the local-form declarative executor. In cloud-hosted MCP mode these tools are intentionally not registered (filtered at tool registration), because that runtime has no local cwd / filesystem-bound execution path.

If you are in cloud mode and do not see deployBuild / deployPlan / deployApply in the tool list, this is expected behavior.

Use the cloud upload-channel path instead:

  1. queryApps(action=getUploadUrl) to get uploadUrl, uploadHeaders, unixTimestamp
  2. Upload source/build zip to uploadUrl with returned headers
    • If cloud build requires private/offline dependencies, package node_modules explicitly
    • For public dependencies, uploading package.json + lockfile is typically enough
  3. manageApps(action=deployApp, cosTimestamp=<unixTimestamp>, installCmd?, buildCmd?, deployCmd?)
    • installCmd / buildCmd / deployCmd are pipeline declarations executed in cloud container
    • Agent passes data + declarations; it does not execute local shell commands

Planned cloud declarative path (incremental roadmap): upload cloudbaserc as a data artifact, then run server-side plan/apply orchestration. deployApply remains the local-form executor of the same declarative spec.

For parameter details, see references/plan-and-apply.md (Cloud-hosted upload pipeline path).

Core principles

  1. Plan before apply — always. Run deployPlan first (dry-run, zero side effects). Read the per-resource action classification and show it to the user before calling deployApply.

  2. Apply requires explicit confirm. deployApply will refuse unless confirm=true is passed. This is the destructive-write guard.

  3. Deployment Gate. Before any apply, complete cloudbase-platform/references/protocols/deployment-gate.md and present the mandatory declaration.

  4. Conservative on existing resources by default. yes defaults to false → existing resources are skipped, not overwritten. Only pass yes=true when the user explicitly wants to overwrite/update existing resources.

  5. database failure always aborts. Even with continueOnError=true, a database-stage failure stops the whole deploy, because later resources depend on it.

  6. Resolve envId explicitly. Never rely on implicit defaults silently — know which environment is targeted (see the priority table below) and confirm it with the user before applying.

Plan action classification

deployPlan returns a list of resource entries. Each status means:

statusmeaning
createnew resource, will be created
updateexists, will be overwritten/updated
skipno change needed
conflictconflict detected — deploy will abort, must resolve first
deploydirect overwrite upload

If any entry is conflict, stop and resolve it before applying.

envId resolution priority

explicit envId param  >  cloudbaserc `envId`  >  logged-in / bound environment

If none can be resolved, the tool errors out. Prefer confirming the resolved envId with the user before applying to production.

Show full SKILL.md (509 more words)Show less

Local workflow (build → plan → apply)

When hosting declares a buildCommand, declarative deploy is a three-step flow — deployApply no longer runs the local build implicitly:

  1. Ensure a cloudbaserc config exists under the project root (cwd).
  2. Build first (only when hosting has a buildCommand): call deployBuild({ cwd, mode? }) to produce the local artifacts (builds every hosting item; pure-static items without a build command are skipped automatically).
    • If the build artifacts are missing at apply time, deployApply fails with BUILD_OUTPUT_NOT_FOUND and directs you back to this step — call deployBuild first, then retry.
    • deployBuild needs no envId and no confirm (local build only, never touches cloud resources); if dependencies are not installed it fails with DEPENDENCY_NOT_INSTALLED and tells you to run install first.
  3. Call deployPlan (optionally with mode, envId, only, skip). Read the plan.
  4. Present the plan + Deployment Gate declaration to the user; get confirmation.
  5. Call deployApply with confirm=true (plus yes / concurrency / continueOnError as needed). Reuse the same mode / envId / only / skip as the plan. A hosting item with existing build output is uploaded directly (the tool clears the build command and reports hostingNeutralized: true); rebuild with deployBuild after source changes so the upload is not stale.
  6. Report the applied result back to the user.

For cloud-hosted MCP mode, do not ask for local cwd/filesystem reads; use the Cloud mode upload-channel flow above.

Build & pipeline execution

Use this mental model: build → plan → apply. The build executor depends on resource type.

resourcetypical build executordeployment path
hostingdeployBuild (local shell build, run before apply)deployApply uploads the built output directly; missing output → BUILD_OUTPUT_NOT_FOUND
app (framework=static)local prebuilt artifactpackage upload + deploy record
app (non-static frameworks)cloud pipelinesource zip upload + cloud build + deploy
functionslocal zip / cloud build / image pipelinedepends on buildStrategy (zip/cloud/local/image)

deployBuild builds every hosting item that has a buildCommand (framework mapping or package.json auto-detection); it skips pure-static items. Build failures surface as BUILD_FAILED, missing local dependencies as DEPENDENCY_NOT_INSTALLED (run install first — deployBuild never installs dependencies for you).

Build command resolution follows declaration priority:

explicit config > framework mapping defaults > package.json auto-detection

buildCommand / installCommand / deployCmd are declarative intent in config. Execution ownership depends on path:

  • local-form paths: specific steps may run in local shell executor
  • cloud-hosted paths: commands are executed by cloud pipeline container (staticCmd), or replaced by prebuilt artifact upload

So the answer to "can cloud mode run local CLI commands" is: execution authority is moved from local shell to cloud pipeline; agent transmits declarations and artifacts.

Routing

User taskRead
cloudbaserc resource fields & desired-state config shapereferences/config-schema.md
deployPlan → deployApply two-step flow, parameters, safetyreferences/plan-and-apply.md
Multi-env (mode / envOverrides), envId priority, env varsreferences/multi-env.md

Minimum self-check

  • Built the hosting artifacts first (deployBuild) when hosting declares a buildCommand?
  • Ran deployPlan and read the action classification before deployApply?
  • Resolved and confirmed the target envId?
  • Completed the Deployment Gate declaration before applying?
  • Passed confirm=true only after user confirmation?
  • Left yes=false unless overwrite of existing resources was explicitly requested?
  • Handled any conflict entries before applying?

Reference index

All packaged reference files (required for skill lint reachability):

© TencentCloudBase, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (references) in config/source/skills/cloudbase-declarative-deploy of TencentCloudBase/CloudBase-AI-Toolkit.

  • SKILL.md
  • references/config-schema.md
  • references/multi-env.md
  • references/plan-and-apply.md

Open the folder on GitHubat commit ea2c202

Used in 1 other repository

We found 4 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in TencentCloudBase/CloudBase-AI-Toolkit, which our catalogue first saw on October 7, 2026.

Compare with similar skills

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Categories

Questions about Cloudbase Declarative Deploy

What does Cloudbase Declarative Deploy do?

CloudBase declarative deployment from a cloudbaserc config (声明式部署, 配置式部署, cloudbaserc 部署) through the deployBuild / deployPlan / deployApply MCP tools. Cloudbase Declarative Deploy is an agent skill from TencentCloudBase/CloudBase-AI-Toolkit. CloudBase declarative deployment from a cloudbaserc config (声明式部署, 配置式部署, cloudbaserc 部署) through the deployBuild / deployPlan / deployApply MCP tools.

When should I use Cloudbase Declarative Deploy?

Cloudbase Declarative Deploy fits situations like: deploying database; gateway resources described in cloudbaserc.json/yaml as a single desired-state config; A user wants to build static hosting artifacts locally first (deployBuild); wants a dry-run plan before applying (deployPlan).

How do I install Cloudbase Declarative Deploy in Claude Code?

Run `npx skills add TencentCloudBase/CloudBase-AI-Toolkit --skill cloudbase-declarative-deploy -a claude-code`. Or copy the skill folder (config/source/skills/cloudbase-declarative-deploy in TencentCloudBase/CloudBase-AI-Toolkit) into .claude/skills/cloudbase-declarative-deploy in your project. Claude Code loads it when a task matches its description.

How do I install Cloudbase Declarative Deploy in Codex?

Run `npx skills add TencentCloudBase/CloudBase-AI-Toolkit --skill cloudbase-declarative-deploy -a codex`. Or copy the skill folder (config/source/skills/cloudbase-declarative-deploy in TencentCloudBase/CloudBase-AI-Toolkit) into .agents/skills/cloudbase-declarative-deploy in your project. Codex loads it when a task matches its description.

Can I use Cloudbase Declarative Deploy in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add TencentCloudBase/CloudBase-AI-Toolkit --skill cloudbase-declarative-deploy -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/cloudbase-declarative-deploy, .gemini/skills/cloudbase-declarative-deploy, .github/skills/cloudbase-declarative-deploy and .opencode/skills/cloudbase-declarative-deploy in your project.

What does Cloudbase Declarative Deploy need to run?

SKILL.md names no scripts, command-line tools or credentials: Cloudbase Declarative Deploy is instructions for the agent only.

Does Cloudbase Declarative Deploy access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Cloudbase Declarative Deploy safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Cloudbase Declarative Deploy use?

Cloudbase Declarative Deploy is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Cloudbase Declarative Deploy use?

About 2.6k tokens (SKILL.md is roughly 10k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 3.8k tokens, read only when the agent opens those files.

What are the alternatives to Cloudbase Declarative Deploy?

Skills that share tags, products or a category with Cloudbase Declarative Deploy: AWS Cdk Development (zxkane/aws-skills, 367 stars), AWS Agentic AI (zxkane/aws-skills, 367 stars), Deploy Observability (aliyun/alibabacloud-observability-mcp-server, 166 stars) and GitOps with ArgoCD and Flux (wshobson/agents, 40k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Cloudbase Declarative Deploy?

TencentCloudBase (a GitHub organization) maintains it in TencentCloudBase/CloudBase-AI-Toolkit, which has 1,132 GitHub stars. The repository holds 49 skills in this directory. The repository was last updated on October 6, 2026.

Source: TencentCloudBase/CloudBase-AI-Toolkit on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.