SKILLEMALL.ai

AC wrong-item-talking

Turn a user-supplied wrong-item script and authorized stills into one wrong item talking clip per still. This mistake explanation talking video studio writes a speakable wrong-item explanation talking clip for each photo, then animates a 2 to 15s homework error talking clip. Use it for error explanation talking pack and wrong-item script talking clip work that stays one photo, one clip.

ClawHub Agent Skills author: beatra-ai v0.1.3 MIT-0 14 files body ≈ 2 522 tokens Open the sourceclawhub.ai analyzed 2 d ago

Turn a user-supplied wrong-item script and authorized stills into one wrong item talking clip per still.

As a process C 52/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

GeneratorInfrastructureMedia 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%
85
Run on models
none yet
Process rating
C
52/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
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: 14. 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 52/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 6 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 100Steps. 31 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2522 tokens
    • low The skill ranks results itself: that belongs to the system behind the tool, not the model

    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
    • -32 of 3 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +3Description length 389: enough signal without eating the budget
    • +4Structure: 10 headings
    • +3Step-by-step instructions: 31 items
    • +4Has examples (8 code blocks)
    • +4Reference files are cited in the instructions (8 of 8)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.

    External checks

    ClawHub: suspicious
    The skill appears to be a real Beatra media-generation integration, but it uses broader account authority than the stated clip workflow needs and can silently replace its own installed code.
    LLM: suspicious (high) · 6 Sept 2026