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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 proxies, agent orchestration. The only SDK supporting image generation (ai.createImageModel + generateImage). Text via ai.createModel with groups cloudbase, hunyuan-exp, or custom-*; model ids (e.g. deepseek-v4-flash, glm-5, kimi-k2.6) go in the `model` field of generateText/streamText. MUST run two-step preflight before code — see body. NOT for browser/Web (use ai-model-web) or Mini Program (use ai-model-wechat).

74

Quality

93%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

The canonical home for this skill is ai-model-nodejs in TencentCloudBase/CloudBase-AI-Toolkit

SKILL.md
Quality
Evals
Security

Quality

Content

85%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A strongly engineered skill body: exact tool calls, gated workflow with validation and recovery loops, and clean one-level reference splitting. The only real weakness is redundancy — the createModel contract and preflight rules are each stated three to four times across the STOP section, provider section, and Best Practices, which inflates token cost without adding information.

Suggestions

State the createModel three-legal-values contract once (in the STOP section) and have the 'Available Providers and Models' section and Best Practice #3 reference it ('see the createModel contract above') instead of restating it verbatim.

Trim Best Practices #1–2, which re-narrate the entire two-step preflight already specified above; a one-line reminder ('run the mandatory preflight before any SDK code') would preserve the reinforcement at a fraction of the tokens.

Collapse the 'Never assume a model is already enabled' guidance, which appears in the decision procedure, preflight step 2, the provider section, and Best Practice #2, into a single strongly-worded rule stated once at the point of highest risk.

DimensionReasoningScore

Conciseness

The body is dense and free of concept padding (no explanations of what cloud functions or LLMs are), but the same rules are repeated nearly verbatim several times: the createModel three-legal-values contract appears in the STOP section, again under 'Available Providers and Models', and again in Best Practice #3; the preflight and 'never assume a model is enabled' rules each reappear in Best Practices #1–2. The duplication goes beyond the 'minor instances' of a 4, fitting the 'mostly efficient but could be tightened' anchor.

3 / 5

Actionability

Guidance is fully executable: exact MCP calls with service/action/params, explicit pass conditions ('postpayPackageId starts with pkg_tcb_tokencredits_', 'status NOT in [3, 4]'), a ready-to-surface purchase message, the complete UpdateAIModel payload with the full-replacement Models[] caveat, runnable init code, and concrete timeout ranges. Copy-paste-ready material covers the common cases.

5 / 5

Workflow Clarity

The two-step preflight is a clearly numbered sequence with explicit validation checkpoints and feedback loops: pack missing → stop and surface the purchase link; target model missing → DescribeManagedAIModelList for the exact spelling → UpdateAIModel → proceed; plus a final self-verify step (Best Practice #13: real invocation, usage.total_tokens > 0). This matches the top anchor with recovery paths throughout.

5 / 5

Progressive Disclosure

The body keeps decision-critical gating logic inline and pushes bulk detail to two real, clearly signaled, one-level-deep files — [api-reference.md](references/api-reference.md) behind an on-demand section ('read … before writing code') and [custom-onboarding.md](references/custom-onboarding.md) — with a closing reference index listing both. Both files exist, and neither nests further references, so navigation is easy.

5 / 5

Total

18

/

20

Passed

Description

100%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

An excellent description: concrete capabilities, comprehensive natural trigger terms, explicit what/when guidance, and explicit negative routing to sibling skills. Every phrase carries information, so the length is density rather than padding.

DimensionReasoningScore

Specificity

The description names many concrete, verifiable actions and surfaces: '@cloudbase/node-sdk (>=3.16.0) — cloud functions, CloudRun, Express/Koa/NestJS, serverless APIs, scheduled jobs, LLM proxies, agent orchestration', 'ai.createImageModel + generateImage', and model ids placed 'in the `model` field of generateText/streamText'. Coverage is comprehensive with no meaningful gaps, matching the anchor for multiple specific concrete actions rather than the 'minor gaps' of a 4.

5 / 5

Completeness

It explicitly answers both questions: what the skill does (Node.js backend AI via the SDK, text and image generation, exact createModel/model-field contract) and when to use it ('Use this skill for…' plus negative routing 'NOT for browser/Web (use ai-model-web) or Mini Program (use ai-model-wechat)'). Both what and when are explicit with concrete triggers, matching the top anchor.

5 / 5

Trigger Term Quality

Natural trigger phrases span runtimes ('cloud functions', 'CloudRun', 'serverless APIs'), frameworks ('Express/Koa/NestJS'), workloads ('scheduled jobs', 'LLM proxies', 'agent orchestration'), and concrete model families ('deepseek-v4-flash', 'glm-5', 'kimi-k2.6'), giving comprehensive coverage including synonyms. It does not sit at 4 because essentially no natural term a backend user would say is missing.

5 / 5

Distinctiveness Conflict Risk

The niche is tightly pinned to one SDK on one runtime plane, and it explicitly routes adjacent cases away ('NOT for browser/Web (use ai-model-web) or Mini Program (use ai-model-wechat)'), so conflict risk with sibling AI skills is minimal — the clear-niche top anchor.

5 / 5

Total

20

/

20

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository

TencentCloudBase/CloudBase-AI-Toolkit

Path

plugin/cloudbase/skills/ai-model-nodejs/SKILL.md

Reviewed

Table of Contents

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