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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

88%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

SecuritybySnyk

Low

Low-risk findings worth noting

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

SKILL.md
Quality
Evals
Security

Quality

Content

77%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 thorough, highly actionable skill with excellent workflow sequencing and validation checkpoints, but it is monolithic — everything lives in SKILL.md with no reference files — and key warnings are repeated verbatim across sections, inflating token cost.

Suggestions

Extract the Type Definitions and the Available Providers/Models tables into a reference file (e.g. references/models.md) and link to it from the body, leaving only the essential decision tree and a few key examples inline.

Consolidate the repeated 'Mandatory Two-Step Preflight' prerequisite blockquote: state it once in the preflight section and reference it with a short pointer ('Preflight completed — see above') before each code example instead of repeating the full paragraph.

Merge the three detailed 'do NOT write createModel("deepseek")...' warning blocks (STOP section, Available Providers section, Best Practices #2) into a single canonical anti-pattern list referenced from the other locations.

DimensionReasoningScore

Conciseness

Mostly efficient domain detail with no basic-concept padding, but the same warnings repeat across sections — the 'Mandatory Two-Step Preflight' prerequisite blockquote appears verbatim before generateText, streamText, and Error Handling, and the 'createModel takes only three values / don't write createModel("deepseek")' warning is detailed three times — which could be consolidated.

3 / 5

Actionability

Fully executable, copy-paste-ready code blocks for generateText, streamText, and every callCloudApi action (DescribeActivityInfo, DescribeEnvPostpayPackage, DescribeAIModels, UpdateAIModel, CreateAIModel) with concrete model IDs, field names, and type definitions covering both billing branches.

5 / 5

Workflow Clarity

The two-step preflight is explicitly sequenced ('① eligibility → ② group readiness. Do not swap the two') with hit/miss validation criteria, error-recovery feedback loops ('On miss: do NOT silently fall back...'), and a self-verify checklist in Best Practices #13 (build, run in simulator, confirm onText/[DONE]/no errors).

5 / 5

Progressive Disclosure

Good in-file structure with clear section headers, but for a ~440-line skill with no bundle files, content that could live in separate reference files (Type Definitions, the long Available Providers/Models tables, the detailed preflight code) is all inlined, and there are no one-level-deep reference files for the skill's own detailed material.

3 / 5

Total

16

/

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.

A highly specific, well-triggered description that cleanly answers what, when, and what-not, with strong sibling-skill disambiguation. Its only weakness is density: the explicit 'Keywords:' enumeration borders on keyword stuffing, though every term is relevant.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — 'Features generateText and streamText with callbacks (onText, onEvent, onFinish)', 'Models via wx.cloud.extend.AI.createModel with groups hunyuan-exp... cloudbase... or custom-*', and 'streamText needs data wrapper, generateText returns raw response' — giving comprehensive coverage of the API surface.

5 / 5

Completeness

Explicitly answers both what ('Features generateText and streamText...') and when ('Use this skill for WeChat Mini Program AI... NOT for browser/Web... Node.js backend... or image generation'), with concrete positive and negative trigger phrases.

5 / 5

Trigger Term Quality

Comprehensive keyword coverage with synonyms — 'WeChat Mini Program AI', '小程序', '企业微信小程序', 'wx.cloud apps', plus brand terms (Hunyuan, DeepSeek, GLM, Kimi, MiniMax) and technical identifiers users would name when reaching for this skill.

5 / 5

Distinctiveness Conflict Risk

Clear niche (WeChat Mini Program AI via wx.cloud.extend.AI) with explicit disambiguation — 'NOT for browser/Web (use ai-model-web), Node.js backend (use ai-model-nodejs), or image generation (use ai-model-nodejs)' — minimizing overlap with sibling skills.

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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