SKILLEMALL.ai

AB multishot-ugc

Generate 10 perspective/angle variations from a single image for multi-shot UGC videos. ✅ USE WHEN: - Have a hero image and need camera angle variations - Creating multi-scene UGC videos (need different shots) - Want close-ups, wide shots, side angles from one source - Building a video with scene changes ❌ DON'T USE WHEN: - Don't have a hero image yet → use morpheus-fashion-design first - Need completely different scenes/locations → use Morpheus multiple times - Just need one image → skip this step - Want to edit images manually → use nano-banana-pro INPUT: Single image (person with product) OUTPUT: 10 PNG variations with different perspectives TYPICAL PIPELINE: Morpheus → multishot-ugc → select best 4 → veed-ugc each → Remotion edit

ClawHub Agent Skills author: Paul de Lavallaz v1.0.1 4 files body ≈ 568 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process B 70/100 · Nearly there — weak spots: failures and branches, running it twice, progress reporting

GeneratorMedia and videoInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
98/100
safety, quality, tests
Safety 60%
100
Quality 40%
94
Run on models
none yet
Process rating
B
70/100
Nearly there
Failures and branches w 10
0
Progress reporting w 2
0
Running it twice w 4
30
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: 4. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 70/100

    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 4 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 60Result and completion. Output format stated, no completion criterion
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. No external tools needed
    • 100Steps. 8 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 568 tokens

    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
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 747: enough signal without eating the budget
    • +4Structure: 10 headings
    • +3Step-by-step instructions: 8 items
    • +3Output format is stated explicitly
    • +4Has examples (5 code blocks)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 94.

    External checks

    ClawHub: suspicious
    This skill appears to do the advertised cloud image-generation job, but it under-explains third-party uploads and does not safely contain downloaded filenames.
    LLM: suspicious (high) · VirusTotal: suspicious · 28 May 2026