SKILLEMALL.ai

AC bring-your-own-model

Upload and use your own LoRA or checkpoint on Runware. Use when the user says "import my LoRA", "upload my checkpoint", "host my own model", "I trained a model elsewhere, use it here", "bring my own weights", or wants to run their custom safetensors through the Runware API like any catalog model. You give it an AIR you control, then generate with it everywhere. To create a new LoRA from images on Runware rather than upload an existing one, use train-style-model.

ClawHub Agent Skills author: runware v1.0.0 MIT-0 2 files body ≈ 1 543 tokens Open the sourceclawhub.ai analyzed 2 d ago

Upload and use your own LoRA or checkpoint on Runware.

As a process C 63/100 · Has gaps — weak spots: result and completion, progress reporting

ProcedureInfrastructureAI and agentsData and analyticstype 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
63/100
Has gaps
Result and completion w 14
0
Progress reporting w 2
0
Failures and branches w 10
50
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: 2. 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 63/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash, web) 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. 32 steps, 1 vague phrases
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1543 tokens
    • 100Running it twice. Mutating operations check current state
    • high The skill tells the model to perform an irreversible action with no human approval

    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 466: enough signal without eating the budget
    • +4Structure: 8 headings
    • +3Step-by-step instructions: 32 items

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

    External checks

    ClawHub: clean
    This skill is a straightforward Runware model-import guide, but users should protect any proprietary model file they upload.
    LLM: benign (high) · VirusTotal: · 18 Jul 2026