SKILLEMALL.ai
GitHub ↗ RU Sign in

AC pressure-wash-video

Generate vertical satisfying pressure-wash shorts (WeryAI): text-to-video or dirty-surface image to rinse motion and a moving clean/dirty line. Use when you need pressure washing satisfying video, power-wash reveal clips, or users ask for moss blasted off pavers, wall refresh, garage grime. SEO: pressure wash video; pressure wash video generation.

ClawHub Agent Skills author: parallel world v1.0.1 MIT-0 5 files · 1 script body ≈ 3 485 tokens Open the sourceclawhub.ai↗ analyzed 7 d ago

Generate vertical satisfying pressure-wash shorts (WeryAI): text-to-video or dirty-surface image to rinse motion and a moving clean/dirty line.

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

GeneratorMedia and videotype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
87
Run on models
none yet
Process rating
C
63/100
Has gaps
Progress reporting w 2
0
Running it twice w 4
30
Result and completion w 14
40
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: 5. 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

    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 7 mutating operations with no state check
    • 40Result and completion. Does not say what the result is
    • 50When it triggers. No condition that starts the skill
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash, web, node) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 85Steps. 43 steps, 1 vague phrases
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3485 tokens
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 11 top-level sections: this looks like several domains in one skill

    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
    • +1No license
    • +2Single-language instructions
    • +3Description length 349: enough signal without eating the budget
    • +4Structure: 14 headings
    • +3Step-by-step instructions: 43 items
    • +4Has examples (3 code blocks)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.

    External checks

    ClawHub: suspicious
    The skill is mostly coherent for WeryAI video generation, but its helper script can upload any readable local file path passed as an image, which is broader than users should accept without review.
    LLM: suspicious (high) · VirusTotal: · 14 Sept 2026