SKILLEMALL.ai

AB nomtiq

Nomtiq 小饭票 is a personalized restaurant finder with local dining memory. Use when the user asks where to eat, wants nearby restaurant recommendations, date-night, business, family or solo dining, says 找餐厅、推荐餐厅、今晚吃什么、附近有什么好吃的, or wants to record a restaurant visit and update a local taste profile. Searches Amap in mainland China and Google Maps via Serper elsewhere, then gives 2+1 recommendations based on taste, budget, location, party size and occasion. Memory is limited to local restaurant preferences and feedback. Do not use for recipes, groceries, calorie tracking or food delivery.

ClawHub Agent Skills author: OakcoderX v0.5.2 MIT-0 19 files body ≈ 1 100 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process B 68/100 · Nearly there — weak spots: inputs and preconditions, running it twice

ProcedureInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
88
Run on models
none yet
Process rating
B
68/100
Nearly there
Inputs and preconditions w 11
0
Running it twice w 4
30
Tools and files w 18
60
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: 19. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "homepage"

    Process rating: all ten parameters 68/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 5 mutating operations with no state check
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 65Failures and branches. 3 branches
    • 85Steps. 23 steps, 1 vague phrases
    • 100When it triggers. States when to use and when not to
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1100 tokens
    • 100Progress reporting. Reports progress
    • 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
    • -310 of 13 scripts are never mentioned in SKILL.md
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 591: enough signal without eating the budget
    • +4Structure: 9 headings
    • +3Step-by-step instructions: 23 items
    • +3Output format is stated explicitly
    • +4Has examples (2 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.

    External checks

    ClawHub: clean
    This restaurant recommendation skill is mostly coherent and purpose-aligned, with some transparency gaps around optional search providers and local profile handling.
    LLM: benign (medium) · VirusTotal: · 17 Jul 2026