SKILLEMALL.ai

AC controlled-generation

Lock the composition of a generated image to a structural guide while you change everything else. Use when the user says "same pose, new character", "keep this layout, restyle it", "match this sketch", "turn my floor plan into a render", "same product silhouette, different finish", or wants interior redesign, sketch-to-render, or controlled asset variations. A control map (pose, edges, depth, segmentation) holds the structure, the prompt brings the new look. To keep a character's identity consistent rather than just its structure, use character-consistency.

ClawHub Agent Skills author: runware v1.0.0 MIT-0 3 files body ≈ 1 761 tokens Open the sourceclawhub.ai analyzed 2 d ago

Lock the composition of a generated image to a structural guide while you change everything else.

As a process C 62/100 · Has gaps — weak spots: result and completion, running it twice, progress reporting

GeneratorAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
97/100
safety, quality, tests
Safety 60%
100
Quality 40%
93
Run on models
none yet
Process rating
C
62/100
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 62/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 4 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
    • 100Steps. 35 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1761 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
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 5 example trigger phrases
    • +3Description length 563: enough signal without eating the budget
    • +4Structure: 8 headings
    • +3Step-by-step instructions: 35 items
    • +4Has examples (1 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 93.

    External checks

    ClawHub: clean
    This skill is a coherent image-generation guide that uses disclosed Runware workflows to transform user-provided images while preserving structure.
    LLM: benign (high) · VirusTotal: · 18 Jul 2026