SKILLEMALL.ai

AB expo-mobile-dev

Opinionated, step-by-step workflow for starting a new React Native + Expo mobile app — gathers the app's name, purpose, and target region (China mainland vs international), scaffolds with `pnpm create expo-app --template default@sdk-55`, installs a hand-picked stack (better-auth, sonner-native, TanStack Query, TanStack Form, Zustand), and installs the official Expo + TanStack AI development skills so future work has expert guidance loaded. Use whenever the user mentions building, scaffolding, starting, or bootstrapping a React Native, Expo, iOS, Android, or "mobile app" — even casually ("我想做一个 app", "make me an app", "start a mobile project"). Also use when the user references files like `app.json`, `app.config.ts`, `eas.json`, the `app/` router directory, or Expo-specific imports. Default to this skill for mobile work unless the user explicitly asks for native Swift, Kotlin, or Flutter.

ClawHub Agent Skills author: tenshowinnovation v0.1.0 MIT-0 9 files body ≈ 4 704 tokens Open the sourceclawhub.ai analyzed 4 d ago

As a process B 67/100 · Nearly there — weak spots: result and completion, running it twice, progress reporting

ProcedureInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
91
Run on models
none yet
Process rating
B
67/100
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

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: 9. 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 67/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 40 mutating operations with no state check
    • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 70Execution cost. Instruction body is 4704 tokens
    • 85Steps. 42 steps, 1 vague phrases
    • 100When it triggers. States when to use and when not to
    • 100Failures and branches. 7 branches, has a failure section
    • 100Consistency. Name and required fields are in place

    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)
    • +3Description length 900: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +2Single-language instructions
    • +5Description quotes 4 example trigger phrases
    • +4Structure: 21 headings
    • +3Step-by-step instructions: 42 items
    • +4Has examples (13 code blocks)
    • +4Reference files are cited in the instructions (6 of 6)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.

    External checks

    ClawHub: suspicious
    This looks like a legitimate Expo mobile-app workflow, but it is broad enough to trigger on casual mobile requests and can persistently install many third-party agent skills.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026