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AC agentroam-travel-connectivity

Buy travel eSIM data plans, gift cards, and mobile airtime with crypto (USDC, USDT, BTC, ETH, SOL and 17+ more) through AgentRoam. Use when a user wants an eSIM for a trip, a travel or brand gift card, or to top up a prepaid mobile number — paid from their own wallet, delivered by email. Trigger on "eSIM for <country>", "data plan for my trip", "buy a <brand> gift card with crypto", "top up a <carrier> number", "pay with USDC/USDT/BTC".

ClawHub Agent Skills author: adiny v0.1.0 MIT-0 2 files body ≈ 1 095 tokens Open the sourceclawhub.ai↗ analyzed 23 h ago

Buy travel eSIM data plans, gift cards, and mobile airtime with crypto (USDC, USDT, BTC, ETH, SOL and 17+ more) through AgentRoam.

As a process C 52/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

ProcedureAI and agentsMarketingtype 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
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
89
Run on models
none yet
Process rating
C
52/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
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 · 0

    ✓ No critical or high findings

    Files scanned: 0. 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 52/100

    • 0Result and completion. Does not say what the result is
    • 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
    • 30Running it twice. 7 mutating operations with no state check
    • 40Consistency. Frontmatter name (agentroam-travel-connectivity) differs from the folder (agentroam-skill)
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. No external tools needed
    • 100Steps. 13 steps
    • 100Execution cost. Instruction body is 1095 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

    • +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
    • +5Description quotes 5 example trigger phrases
    • +3Description length 440: enough signal without eating the budget
    • +4Structure: 7 headings
    • +3Step-by-step instructions: 13 items
    • +4Has examples (1 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.

    External checks

    ClawHub: clean
    This skill is a disclosed AgentRoam shopping workflow for eSIMs, gift cards, and mobile top-ups, with an explicit approval gate before any real order is created.
    LLM: benign (high) · VirusTotal: · 9 Jul 2026