SKILLEMALL.ai

AC composite-scene

Merge several real images into one coherent picture without manual cut-out or masking. Use when the user says "put this product into that scene", "combine these two photos", "place my subject on this background", "drop the watch onto the table", "make these into one image", or wants a product, a subject, and a backdrop fused with matching light and perspective. The inputs are multiple images. To edit a single image, use edit-image. To keep one character identical across new scenes, use character-consistency.

ClawHub Agent Skills author: runware v1.0.0 MIT-0 3 files body ≈ 1 570 tokens Open the sourceclawhub.ai analyzed 2 d ago

Merge several real images into one coherent picture without manual cut-out or masking.

As a process C 60/100 · Has gaps — weak spots: result and completion, running it twice, progress reporting

GeneratorAI and agentsFinancetype and topics are labelled automatically from the skill text
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
89
Run on models
none yet
Process rating
C
60/100
Has gaps
Result and completion w 14
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: 0. 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 60/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 2 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 85Steps. 35 steps, 1 vague phrases
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1570 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

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 5 example trigger phrases
    • +3Description length 513: enough signal without eating the budget
    • +4Structure: 8 headings
    • +3Step-by-step instructions: 35 items
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.

    External checks

    ClawHub: clean
    This skill provides clear image-compositing instructions, with its external image-model use and multi-image inputs disclosed and aligned with its purpose.
    LLM: benign (high) · VirusTotal: · 18 Jul 2026