ai-model-nodejs
Use this skill for Node.js backend AI via @cloudbase/node-sdk (>=3.16.0) — cloud functions, CloudRun, Express/Koa/NestJS, serverless APIs, scheduled jobs, LLM…
Code review and validation for CloudBase projects. After writing code for Web / miniprogram / CloudRun / cloud-function projects, call this skill to check for known pitfalls — auth guard misuse, missing database tables, RLS misconfiguration, storage domain setup, and SDK API
$ npx -y skills add TencentCloudBase/CloudBase-AI-Toolkit --skill cloudbase-code-review --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/cloudbase-code-reviewContext preview
The summary Claude sees to decide when to auto-load this skill.
Code review and validation for CloudBase projects. After writing code for Web / miniprogram / CloudRun / cloud-function projects, call this skill to check for known pitfalls — auth guard misuse, missing database tables, RLS misconfiguration, storage domain setup, and SDK API
name: cloudbase-code-review description: "Code review and validation for CloudBase projects. After writing code for Web / miniprogram / CloudRun / cloud-function projects, call this skill to check for known pitfalls — auth guard misuse, missing database tables, RLS misconfiguration, storage domain setup, and SDK API misuse. Supports automated lint scripts (regex-based) + LLM semantic review." version: 2.34.3 alwaysApply: false
Sibling CloudBase skills ship beside this skill. Use local relative paths such as `../auth-tool-cloudbase/SKILL.md`.
If a referenced sibling skill file is missing from this environment, ask the user to install the full CloudBase plugin (or the missing skill). Do **not** HTTP-fetch remote skill or protocol markdown into the agent context.
> **One-liner**: After implementing CloudBase features, call this skill to catch common mistakes before users do.
Call this skill **after** completing a CloudBase implementation task, before declaring done:
The skill runs in two layers:
| Layer | Method | Speed | What it catches | |-------|--------|-------|-----------------| | **Lint (optional)** | No executable script is shipped. If the user approves running lint, review the code block in `references/lint-rules/README.md`, copy it to a temporary local `cloudbase-lint.mjs`, then run `node cloudbase-lint.mjs --project-dir <path>` | Seconds | Deterministic regex checks — wrong API calls, missing configs, pattern mismatches | | **LLM review** | Read each rule's "LLM 检查" section, inspect code semantically | Variable | Semantic issues — route guard logic, RLS completeness, architecture-level problems |
See `references/RULES_INDEX.md` for the full matrix (module × frontend type → applicable rules).
Do not promote a single failed run or case-specific workaround into a hard rule. A rule should be backed by stable SDK/API documentation, repeated failures, or deterministic runtime behavior. Case-specific observations belong in attribution reports; only broadly applicable constraints should enter `RULES_INDEX.md` or the optional lint checklist.
# Step 1: Read relevant rules for identified modules # references/rules/cross-cutting/AUTH001.md # references/rules/cross-cutting/SEC001.md # references/rules/postgresql/PG-CR001.md # ... # Optional: if the user approves running lint, review the script code block in # references/lint-rules/README.md, copy it to a temporary cloudbase-lint.mjs, # then run: node cloudbase-lint.mjs --project-dir . # Step 2: For each applicable rule, read the "LLM 检查" section # and manually inspect your code before claiming done.
Each rule `.md` file follows this structure:
# RULE-ID Rule Name - **Module**: which module (auth / postgresql / storage / ...) - **Severity**: error | warning - **Stage**: code-generation | deployment | config ## 正则检查 (Lint) The condition checked by the optional script code block in `references/lint-rules/README.md`. ## LLM 检查 Semantic review prompt for human or LLM to evaluate. ## 修复指引 How to fix the issue.
All packaged reference files (required for skill lint reachability):
AI writes the code. CloudBase runs the backend. The CloudBase integration layer for AI coding tools: Plugin installs the stack, Skills steer how code is written, MCP operates databases, functions, storage, and deploys from chat.
Repo: TencentCloudBase/CloudBase-AI-Toolkit
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