SKILLEMALL.ai

AD linkpix-shipinhao-viral-video

视频号 爆款带货视频生成。帮助微信私域运营、直播带货团队、中老年受众商家通过青虎AI完成“视频号 爆款带货视频生成”:可调用可灵Kling 3.0、阿里Wanx 3.0、MiniMax H3、Seedance 2.0、Seedance 2.5、HappyHorse 1.1等大模型,生成情感故事、好物分享、知识口播、直播引流等视频类型,精准适配视频号的社交裂变逻辑和熟人推荐机制。适用于微信视频号、公众号嵌入、朋友圈广告等私域流量转化场景。Use this skill for 视频号爆款视频, 私域流量, 情感故事, 好物分享, 知识口播, 直播引流, 微信生态, 社交裂变, 转化率, 阿里Wanx, HappyHorse, 可灵Kling, AIGC视频生成。通过青虎AI统一接入,支持素材上传、任务轮询和结果下载。 当用户要求做视频号 爆款视频生成或视频号带货/种草/投放短视频时必须触发。关键词:LinkPix、qhkit、青虎、视频号爆款视频、微信私域、情感故事、好物分享、知识口播、直播引流、朋友圈广告、社交裂变。

ClawHub Agent Skills author: AutoAGC v0.1.1 MIT-0 2 files body ≈ 1 530 tokens Open the sourceclawhub.ai analyzed 3 d ago

视频号 爆款带货视频生成。帮助微信私域运营、直播带货团队、中老年受众商家通过青虎AI完成“视频号 爆款带货视频生成”:可调用可灵Kling 3.0、阿里Wanx 3.0、MiniMax H3、Seedance 2.0、Seedance 2.5、HappyHorse…

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureMedia and videoInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
83
Run on models
none yet
Process rating
D
46/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 0

    ✓ No critical or high findings

    Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "homepage"

    Process rating: all ten parameters 46/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (bash, python, node) that frontmatter does not declare
    • 100Steps. 25 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1530 tokens
    • 100Running it twice. No mutating operations

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 464: enough signal without eating the budget
    • +4Structure: 7 headings
    • +3Step-by-step instructions: 25 items
    • +4Has examples (4 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.

    External checks

    ClawHub: suspicious
    The skill matches its stated video-generation purpose, but it asks the agent to install unpinned tools and handle an API key in ways that could expose the user's account or machine.
    LLM: suspicious (high) · 8 Sept 2026