SKILLEMALL.ai

BC apify-x402-agentic-wallet

Discover, pay for, and run any Apify Actor by paying USDC on Base over the x402 protocol with a Coinbase Agentic Wallet (awal) — no Apify account or API key. You buy one small, spend-capped prepaid Apify token, then run as many Actors as the request needs with it. Use when the user wants to use Apify tools without signing up, pay per use with crypto / USDC, set up an agentic wallet, mentions "x402", "awal", "agentic wallet", "Coinbase wallet", "pay with USDC", "no API key", or asks to pull live web data (social media, search, maps, marketplaces, news) while paying on-chain per use.

apify/awesome-skills Agent Skills author: apify 1 file body ≈ 2 617 tokens Open the sourcegithub.com↗ analyzed 4 d ago

Discover, pay for, and run any Apify Actor by paying USDC on Base over the x402 protocol with a Coinbase Agentic Wallet (awal) — no Apify account or API key.

As a process C 62/100 · Has gaps — weak spots: result and completion, inputs and preconditions, progress reporting

IntegrationAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
92/100
safety, quality, tests
Safety 60%
94
Quality 40%
88
Run on models
none yet
Process rating
C
62/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Exfiltration medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The instructions or scripts send data out: environment variables, keys, file contents, chat history. You may never notice, because the agent performs the upload as "part of the task".

For the author

If the upload is not needed for the task, remove it: catalogs flag such skills and delist them. If it is needed, name the destination explicitly, say what leaves the machine, and give the user a switch.

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 · 2

    ✓ No critical or high findings

    Medium and low: 2
    • medium Exfiltration net-credential-use SKILL.md:135
      Credential used in a network call (verify the destination is the intended service)
      curl -s -H "Authorization: Bearer $TOKEN" \
    • low Exfiltration net-credential-use SKILL.md:170
      Credential used in a network call (verify the destination is the intended service) (the skill's own vendor host)
      curl -s "https://agi.apify.com/prepaid-tokens/balance" -H "Authorization: Bearer $TOKEN"
      vendor-host

    Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "author_url"

    Process rating: all ten parameters 62/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 14 steps
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2617 tokens
    • 100Running it twice. Mutating operations check current state
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low The response is described with custom markup (4 tags): a typed call is more reliable

    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 4 example trigger phrases
    • +3Description length 588: enough signal without eating the budget
    • +4Structure: 10 headings
    • +3Step-by-step instructions: 14 items
    • +4Has examples (12 code blocks)

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