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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?

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SecuritybySnyk

High

Do not use without reviewing

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.

A dense, highly actionable reference for a quirky Mini Program AI API with excellent executable examples and a well-sequenced, validated preflight workflow. Its main weakness is redundancy: the createModel-provider warning and preflight prerequisite are repeated across several sections and could be consolidated to save tokens.

Suggestions

Consolidate the 'createModel(provider) is not a vendor/model name' warning — currently stated in the STOP section, the Available Providers table, the section-B callout, and Best Practice #2 — into one canonical location and cross-reference it instead of repeating full prose.

State the Mandatory Two-Step Preflight prerequisite once near the top of the API-example sections rather than restating the full 'for the resource pack branch, swap…' preamble before the generateText, streamText, and error-handling examples.

Consider moving the Type Definitions and the exhaustive provider/model catalog into a reference file under references/ so the SKILL.md body stays a lean overview, which would also raise progressive_disclosure toward a 5.

DimensionReasoningScore

Conciseness

Mostly efficient because the API gotchas (data wrapper, raw return value, three legal provider values) are genuinely non-obvious, but the createModel-vendor-name warning, 'no model is enabled by default', and the preflight prerequisite are restated across 4+ sections — more than minor padding that could be tightened. Not a 4 because the redundancy is noticeable rather than incidental; not a 2 because the core content is not generic concept explanation.

3 / 5

Actionability

Fully executable, copy-paste-ready code blocks for generateText, streamText with callbacks, async iteration, error handling, and the callCloudApi preflight/onboarding calls, with placeholders (e.g. <envId>, <YOUR_ENV_ID>) explicitly flagged as user-specific values to fill. Specific examples cover the common cases.

5 / 5

Workflow Clarity

The Mandatory Two-Step Preflight is a fixed-order sequence (① eligibility → ② group readiness) with explicit hit/miss validation criteria, feedback loops on miss (enroll / purchase / retry), and Best Practice #13 provides a self-verify checklist (build, run in DevTools, confirm onText/[DONE]/no errors) — matching the anchor's validation + feedback + checklist pattern.

5 / 5

Progressive Disclosure

No bundle files exist, so all content lives in one ~446-line file (above the 50-line simple-skill exception), but it is well-organized into clearly headed sections and sibling/doc references are clearly signaled at the top. Good structure with minor gaps — the type definitions and provider catalog could optionally be split out — but not buried or nested; not a 5 because there is no file-level progressive disclosure.

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.

A highly specific, trigger-rich description that names concrete capabilities, covers natural keywords bilingually, and cleanly distinguishes itself from sibling skills via explicit exclusions. No vague fluff or over-claims.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — 'generateText and streamText with callbacks (onText, onEvent, onFinish)', 'createModel with groups hunyuan-exp / cloudbase / custom-*', model IDs in the data wrapper, and a mandatory two-step preflight — giving comprehensive coverage rather than vague language.

5 / 5

Completeness

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

5 / 5

Trigger Term Quality

Comprehensive natural-term coverage with explicit synonyms in both English and Chinese ('Mini Program AI', 'wx.cloud.extend.AI', '小程序成长计划', 'ai_miniprogram_inspire_plan', 'Token Credits 资源包') plus vendor/model names (Hunyuan, DeepSeek, GLM, Kimi, MiniMax).

5 / 5

Distinctiveness Conflict Risk

Clear niche (WeChat Mini Program wx.cloud.extend.AI) with explicit cross-references to distinct sibling skills (ai-model-web, ai-model-nodejs) for the adjacent use cases it excludes, minimizing conflict risk.

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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