SKILLEMALL.ai

AC smart-cart-command-planner

Convert Chinese or English natural-language requests for an OpenClaw-powered omnidirectional smart cart into conservative, structured motion plans. Use for command understanding, task decomposition, waypoint planning, obstacle-aware movement, emergency-stop handling, and JSON control-plan generation for carts that support forward, backward, lateral movement, turning, sensing, waiting, and stopping.

ClawHub Agent Skills author: jason15336804 v1.0.0 MIT-0 5 files body ≈ 594 tokens Open the sourceclawhub.ai analyzed 2 d ago

Convert Chinese or English natural-language requests for an OpenClaw-powered omnidirectional smart cart into conservative, structured motion plans.

As a process C 55/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, failures and branches

GeneratorInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
97/100
safety, quality, tests
Safety 60%
99
Quality 40%
94
Run on models
none yet
Process rating
C
55/100
Has gaps
Inputs and preconditions w 11
0
Failures and branches w 10
0
Progress reporting w 2
0
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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Risky intent intent-offensive-security SKILL.md:3
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      description: Convert Chinese or English natural-language requests for an OpenClaw-powered omnidirectional smart cart into conservative, structured motion plans. Use for command understanding, task dec

    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 55/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 100Steps. 18 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 594 tokens
    • 100Running it twice. No mutating operations
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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)
    • +1No license
    • +2Single-language instructions
    • +3Description length 401: enough signal without eating the budget
    • +4Structure: 5 headings
    • +3Step-by-step instructions: 18 items
    • +3Output format is stated explicitly
    • +4Has examples (0 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 94.

    External checks

    ClawHub: clean
    This skill creates cautious smart-cart movement plans and includes a local validator, with no evidence of hidden network access, credential use, or direct hardware control.
    LLM: benign (high) · VirusTotal: · 22 Aug 2026