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ai-model-wechat

Use this skill for WeChat Mini Program AI via wx.cloud.extend.AI (小程序, 企业微信小程序, wx.cloud apps). Features generateText and streamText with callbacks (onText, onEvent, onFinish). Models via wx.cloud.extend.AI.createModel with groups hunyuan-exp (小程序成长计划), cloudbase (main managed), or custom-*. Model IDs (deepseek-v4-flash, deepseek-v3.2, hunyuan-2.0-instruct-20251111, glm-5, kimi-k2.6) go in the data wrapper model field. API differs from JS/Node SDK — streamText needs data wrapper, generateText returns raw response. MUST run two-step preflight before code — see body. Keywords: Mini Program AI, wx.cloud.extend.AI, 小程序成长计划, ai_miniprogram_inspire_plan, Token Credits 资源包, generateText, streamText, createModel, hunyuan-exp, TokenHub, Hunyuan, DeepSeek, GLM, Kimi, MiniMax. NOT for browser/Web (use ai-model-web), Node.js backend (use ai-model-nodejs), or image generation (use ai-model-nodejs).

72

Quality

90%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

SecuritybySnyk

Passed

No findings from the security scan

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

SKILL.md
Quality
Evals
Security

Quality

Content

81%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.

The body is highly actionable with a rigorous, well-validated two-step workflow and concrete executable examples throughout. Its main weakness is conciseness — critical warnings are restated many times — and a monolithic structure that could offload some reference material into separate files.

Suggestions

Consolidate the repeated createModel-argument warning into one canonical section and cross-reference it instead of restating the full wrong-example list in Available Providers and Best Practices.

Extract the type definitions and the API-comparison table into a reference file (e.g. references/api-types.md) and link to it from SKILL.md to reduce the monolithic footprint and improve progressive disclosure.

Collapse the repeated 'no model is enabled by default' reminders into a single prerequisite note referenced from each code example.

DimensionReasoningScore

Conciseness

Per-section content is efficient and assumes Claude's competence, but the same warnings are repeated across several sections — the createModel('deepseek') mistake appears in the STOP block, Available Providers, and Best Practices, and 'no model is enabled by default' is restated five times — which is padded and could be tightened.

3 / 5

Actionability

Fully executable, copy-paste-ready code blocks for generateText, streamText, createModel, and the callCloudApi calls (DescribeActivityInfo, DescribeAIModels, UpdateAIModel, CreateAIModel), with concrete model IDs, PascalCase params, and real documentation/purchase URLs.

5 / 5

Workflow Clarity

The Mandatory Two-Step Preflight is a clearly sequenced workflow (① eligibility → ② group readiness) with explicit validation checkpoints, hit/miss criteria, error-recovery guidance ('do NOT silently fall back'), and a self-verify checklist in Best Practice #13.

5 / 5

Progressive Disclosure

Well-organized into clearly headed sections with sibling-skill and external-doc links, but the 445-line SKILL.md is monolithic with no bundle files; content such as the type definitions and detailed preflight tables is inlined rather than split into one-level-deep reference files.

4 / 5

Total

17

/

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.

The description is specific, trigger-rich, and complete, clearly stating what the skill does and when to use it while distinguishing it from sibling runtimes. It uses imperative third-person-style phrasing consistent with the good examples and avoids vague fluff.

DimensionReasoningScore

Specificity

Lists multiple concrete actions and surfaces — generateText, streamText with onText/onEvent/onFinish callbacks, createModel with three named groups, and model IDs placed in the data wrapper — giving comprehensive coverage rather than vague abstraction.

5 / 5

Completeness

Explicitly answers both 'what' (features, model groups, API differences, data wrapper) and 'when' via an explicit 'Use this skill for...' clause plus a 'NOT for browser/Web / Node.js / image generation' exclusion list with concrete trigger phrases.

5 / 5

Trigger Term Quality

Comprehensive natural and technical keywords including synonyms and identifiers — Mini Program AI, wx.cloud.extend.AI, 小程序成长计划, generateText, streamText, createModel, hunyuan-exp, TokenHub, Hunyuan, DeepSeek, GLM, Kimi, MiniMax — covering both English and Chinese phrases a user would say.

5 / 5

Distinctiveness Conflict Risk

A clear WeChat-Mini-Program-only niche with explicit redirection to sibling skills (ai-model-web, ai-model-nodejs, http-api-cloudbase), minimizing the risk of triggering for the wrong runtime.

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.

Validation15 / 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
Reviewed

Table of Contents

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