SKILLEMALL.ai

AB dpp-pipeline

Use when you need to turn a local source video and one product image into a single-product placement video with the DPP pipeline.

ClawHub Agent Skills author: kotot v1.0.2 MIT-0 41 files · 7 scripts body ≈ 1 142 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process B 69/100 · Nearly there — weak spots: result and completion, running it twice, progress reporting

ProcedureInfrastructureMedia and videotype and topics are labelled automatically from the skill text
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
95
Quality 40%
96
Run on models
none yet
Process rating
B
69/100
Nearly there
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

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Broad scope medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.

For the author

Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.

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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • medium Broad scope meta-broad-allowed-tools SKILL.md:1
      Broad tool permissions pre-approved: Bash
      allowed-tools: Bash Read Glob Grep

    Files scanned: 41. 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 69/100

    • 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
    • 50When it triggers. No condition that starts the skill
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 70Failures and branches. 5 branches
    • 100Tools and files. Tools declared in frontmatter
    • 100Steps. 42 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1142 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

    • +5Description has no quoted example phrases that should trigger the skill
    • +3Output format is not stated: the model decides each time
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 129: enough signal without eating the budget
    • +4Structure: 8 headings
    • +3Step-by-step instructions: 42 items
    • +4Has examples (3 code blocks)
    • +4Reference files are cited in the instructions (1 of 3)
    • +3All 7 scripts are documented
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 96.

    External checks

    ClawHub: suspicious
    The skill mostly matches a remote video-generation workflow, but it ships under-scoped storage utilities and persistent API logging that users should review before installing.
    LLM: suspicious (medium) · VirusTotal: · 29 May 2026