SKILLEMALL.ai

AB amazon-product-analysis

Turn an Amazon product link into an evidence-based short-form video script — extract listing selling points, mine real buyer language from reviews, then produce a shot-by-shot script once the user picks a content direction (product-demo / narrative / direct-response). Trigger this skill when the user drops an amazon.com or amzn.to product link and mentions short video, UGC/ad video, social content, or asks 'what kind of video would work for this product' / 'write me a video script', even if they never say the word 'skill'. This version focuses on listing + review insight and does not scrape competing viral videos for teardown (see 'Known limitations' below), and does not generate the video itself (video synthesis is a future iteration — if asked to 'just produce the video' or 'analyze similar viral videos', explain the current scope instead of forcing it).

ClawHub Agent Skills author: chengyu-xixihaha v1.0.0 MIT-0 3 files body ≈ 3 606 tokens Open the sourceclawhub.ai analyzed 3 d ago

Turn an Amazon product link into an evidence-based short-form video script — extract listing selling points, mine real buyer language from reviews, then…

As a process B 74/100 · Nearly there — weak spots: inputs and preconditions

AnalyzerMedia and videotype and topics are labelled automatically from the skill text
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
84
Run on models
none yet
Process rating
B
74/100
Nearly there
Inputs and preconditions w 11
0
Tools and files w 18
60
Result and completion w 14
60
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: 3. 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 74/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 85Steps. 24 steps, 2 vague phrases
    • 100When it triggers. States when to use and when not to
    • 100Failures and branches. 2 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3606 tokens
    • 100Running it twice. Mutating operations check current state
    • 100Progress reporting. Reports progress

    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)
    • +3Description length 868: 120–800 characters recommended
    • +1No license
    • +2Single-language instructions
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 24 items
    • +3Output format is stated explicitly
    • +4Has examples (0 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.

    External checks

    ClawHub: clean
    This skill is a coherent Amazon listing and review analysis helper for producing marketing video scripts, with only a minor routing concern from one broad trigger phrase.
    LLM: benign (high) · VirusTotal: · 1 Sept 2026