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小红书 爆款带货视频生成。帮助小红书种草运营、买手、博主通过青虎AI完成“小红书 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,擅长制作Vlog种草、开箱测评、沉浸式体验、图文转视频、氛围感短片等视频类型,精准拿捏小红书用户对真实感、审美和情绪价值的偏好。适用于小红书商品笔记、视频笔记、直播间引流等场景,高效提升种草率。Use this skill for 小红书爆款视频, Vlog种草, 开箱测评, 沉浸式体验, 图文转视频, 氛围感短片, 买手电商, 种草笔记, 引流, 真实感,。通过青虎AI统一接入,支持素材上传、任务轮询和结果下载。 当用户要求做小红书 爆款视频生成或小红书带货/种草/投放短视频时必须触发。关键词:LinkPix、qhkit、青虎、小红书爆款视频、Vlog种草、开箱测评、沉浸式体验、图文转视频、氛围感、商品笔记、视频笔记。

ClawHub Agent Skills author: AutoAGC v0.1.1 MIT-0 2 files body ≈ 1 528 tokens Open the sourceclawhub.ai analyzed 3 d ago

小红书 爆款带货视频生成。帮助小红书种草运营、买手、博主通过青虎AI完成“小红书 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

ReferenceMedia 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 1528 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 447: 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 has a coherent video-generation purpose, but it asks the agent to install mutable third-party tools and handle a reusable API key, so it belongs in Review before installation.
    LLM: suspicious (high) · 8 Sept 2026