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ai-model-wechat

Use this skill for WeChat Mini Program AI via wx.cloud.extend.AI (小程序, wx.cloud apps). Covers generateText and streamText with callbacks (onText, onEvent, onFinish); streamText needs a data wrapper, generateText returns the raw response. Also covers multimodal image input (image_url content blocks, OCR and form extraction) — vision-capable models only. Models via wx.cloud.extend.AI.createModel with groups cloudbase (main managed, also 小程序成长计划 with `hy4-preview` / `hy3` / `deepseek-v4.1-flash`), the legacy hunyuan-v3 / hunyuan-exp, or custom-*; model id goes in the `model` field. MUST run two-step preflight before code — see body. NOT for browser/Web (use ai-model-web), Node.js backend (use ai-model-nodejs), or image generation (use ai-model-nodejs).

Quality

86%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

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.

An exceptionally actionable, well-sequenced skill body: the mandatory two-step preflight with hit criteria, miss handling, feedback loops, and a self-verify checklist sets a high bar for workflow clarity, and all code is copy-paste ready. The weaknesses are token efficiency (critical warnings repeated 4-5 times across sections) and structure (a 557-line monolith with type definitions and catalog tables inlined rather than split into references/ files).

Suggestions

Deduplicate the createModel legal-values rule and the 'no model is enabled by default' warning — each is stated 4-5 times across the STOP section, Available Providers, API-section prerequisites, and Best Practices; state each once and cross-reference it.

Move the Type Definitions section and the provider/billing catalog tables into references/ files (e.g. references/types.md, references/providers.md) linked from the body, shrinking SKILL.md to an overview.

Replace the repeated 'Prerequisite' blockquotes in the generateText, streamText, and Error Handling sections with a single one-line pointer back to the Mandatory Two-Step Preflight section.

DimensionReasoningScore

Conciseness

The body is dense and free of beginner-concept padding (no 'what is a Mini Program' fluff; every section carries API specifics), but the same critical facts are repeated many times: "No model is enabled by default" appears in Preflight ②, section B, the generateText/streamText/error-handling prerequisites, and Best Practice #3; the four legal createModel values appear in the STOP table, the Providers section, the decision procedure, and Best Practice #2. This fits the 3 anchor (mostly efficient but could be tightened) — more deduplication opportunity than the 'minor instances' of anchor 4, but the repetition is targeted error-prevention rather than the padded/known-concept explanations of anchor 2. Time-sensitive details (base library 3.15.1+, the 2026-05-31 offline date) are at least framed as legacy/deprecated guidance.

3 / 5

Actionability

Fully executable, copy-paste-ready guidance throughout: complete callCloudApi snippets with exact params (DescribeActivityInfo, DescribeEnvPostpayPackage, DescribeAIModels, UpdateAIModel with the full-replacement Models[] warning), spelled-out hit criteria ("postpayPackageId starts with pkg_tcb_tokencredits_ ... status ∉ [3, 4]"), purchase URLs with replace-the-placeholder instructions, working generateText/streamText/multimodal code, and a defensive JSON-parse fallback that retains the draft on failure. This matches the 5 anchor — concrete examples cover the common cases with no meaningful gaps.

5 / 5

Workflow Clarity

The two-step preflight is a fixed, explicitly ordered sequence ("① eligibility → ② group readiness. Do not swap the two") with explicit hit criteria and on-miss actions per billing branch, a numbered decision procedure for user-named models, error-recovery feedback loops (InvalidParameter casing retry, Markdown-fence JSON parse retry with draft retention), a checklist-style self-verify step (Best Practice #13 with (a)/(b)/(c) confirmation), and an explicit eligibility-error vs call-failure distinction (#10). This is the 5 anchor: clear sequence, explicit validation steps, feedback loops, and checklists.

5 / 5

Progressive Disclosure

The ~557-line body is a monolith with no bundle files at all: the Type Definitions section (~70 lines of interfaces) and the provider/billing catalog tables are inlined content that clearly belongs in separate references/ files, and there are no external references to signal. Section headers and the up-front STOP section give it real structure, so it sits at the 3 anchor (some structure, content that should be separate is inline) rather than 2 (minimal structure), but the absence of any file split keeps it below 4.

3 / 5

Total

16

/

20

Passed

Description

92%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 clearly delimits what the skill covers (generateText/streamText contracts, callbacks, multimodal OCR, model provisioning) and when to use it, with explicit negative boundaries redirecting browser/Node/image-generation contexts to sibling skills. The only weakness is that the model-id enumeration in the middle is API jargon rather than natural user phrasing, and a few common trigger phrasings are missing.

DimensionReasoningScore

Specificity

The description lists multiple concrete actions with comprehensive coverage: "Covers generateText and streamText with callbacks (onText, onEvent, onFinish)", "multimodal image input (image_url content blocks, OCR and form extraction)", and "Models via wx.cloud.extend.AI.createModel with groups cloudbase ... hunyuan-v3 / hunyuan-exp, or custom-*". It matches the 5 anchor (multiple specific concrete actions, comprehensive) rather than 4, since it covers text generation, streaming with named callbacks, multimodal OCR/form extraction, and model provisioning with per-call contract details ("streamText needs a data wrapper, generateText returns the raw response").

5 / 5

Completeness

It explicitly answers 'when' with a concrete trigger clause ("Use this skill for WeChat Mini Program AI via wx.cloud.extend.AI (小程序, wx.cloud apps)") and 'what' with a specific capability list, plus explicit negative boundaries ("NOT for browser/Web (use ai-model-web), Node.js backend (use ai-model-nodejs), or image generation"). This matches the 5 anchor — both what and when clearly and explicitly stated with concrete triggers — not 4, where the 'when' would be less specific.

5 / 5

Trigger Term Quality

Good natural-keyword coverage with bilingual synonyms — "WeChat Mini Program AI via wx.cloud.extend.AI (小程序, wx.cloud apps)", "OCR and form extraction", "image generation" — but the middle of the description enumerates internal model ids ("hy4-preview / hy3 / deepseek-v4.1-flash") which are API jargon rather than user phrasing, and common user phrasings like "chatbot", "receipt scanning", or "AI chat" are absent. This fits the 4 anchor (good coverage, a few natural terms missing) better than 5 (comprehensive synonyms) and better than 3 (the core triggers are well covered, not just 'some relevant keywords').

4 / 5

Distinctiveness Conflict Risk

A clear niche (AI calls from the WeChat Mini Program runtime via wx.cloud.extend.AI) with minimal conflict risk: it names the exact namespace and explicitly redirects adjacent contexts to sibling skills ("use ai-model-web", "use ai-model-nodejs", "use http-api-cloudbase"). This is the 5 anchor (clear niche with distinct triggers), not 4 — there is no meaningful overlap risk with related skills.

5 / 5

Total

19

/

20

Passed

Validation

87%

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

Validation — 14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (557 lines); consider splitting into references/ and linking

Warning

frontmatter_unknown_keys

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

Warning

Total

14

/

16

Passed

Repository

TencentCloudBase/CloudBase-AI-Toolkit

Path

config/source/skills/ai-model-wechat/SKILL.md

Reviewed

Table of Contents

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