SKILLEMALL.ai

AD linkpix-happyhorse-sales

帮助电商运营、品牌商品团队、直播带货团队与商业内容创作者通过青虎AI完成“HappyHorse 1.1 电商带货视频”:既可只输入文字从零生成,也可加入图片、视频或音频控制结果,支持首尾帧控制,生成前自动上传素材。适用于AI广告、TVC、电商产品展示视频、竖屏种草视频、短剧漫剧等。精准服务于淘宝天猫、京东、拼多多、抖音、快手、小红书、TikTok、Amazon、Temu、Shopee等平台的动态商品演示和品牌营销需求。 Use this skill for HappyHorse1.1电商视频, 通义万相视频, 图生视频, 首尾帧, 产品展示, 带货, 种草, 短剧, 漫剧, 淘宝, 京东, 抖音, TikTok, Shopee, 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。 当用户要求用 HappyHorse 1.1 电商带货视频 或 Happy Horse 1.1 / HappyHorse 1.1 做带货/商品/种草/广告视频时必须触发。关键词:LinkPix、qhkit、青虎、HappyHorse、Happy Horse 1.1、图生视频、首尾帧、产品展示、种草、短剧、淘宝、抖音、TikTok。

ClawHub Agent Skills author: AutoAGC v0.1.1 MIT-0 2 files body ≈ 1 626 tokens Open the sourceclawhub.ai analyzed 3 d ago

帮助电商运营、品牌商品团队、直播带货团队与商业内容创作者通过青虎AI完成“HappyHorse 1.1…

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

ReferenceMarketingtype 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. 28 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1626 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 522: enough signal without eating the budget
    • +4Structure: 7 headings
    • +3Step-by-step instructions: 28 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 is aligned with video generation, but it needs review because it asks users to share an API key in chat and directs agents to install or upgrade unpinned executable packages.
    LLM: suspicious (high) · 8 Sept 2026