AC veradata-latam-compliance
Verified Latin American compliance data for autonomous AI agents. Use when an agent needs OFAC/UN/EU/UK sanctions screening, KYB entity verification (Colombia, Mexico, Brazil, Chile, Peru), LATAM central bank rates (TRM, TIIE, Selic, UF, dólar blue), business registry lookup (RUES/CNPJ/RFC), or AI-powered market intelligence for LATAM markets. Payment via x402 v2 micropayments in USDC on Base or Solana — no API key, no subscription, no account required. Single POST request per call. Use this skill whenever a task involves sanctions compliance, KYC/KYB, LATAM financial data, corporate registry, or regulatory screening for Latin American jurisdictions.
Verified Latin American compliance data for autonomous AI agents.
As a process C 62/100 · Has gaps — weak spots: result and completion, inputs and preconditions, progress reporting
How to improve
- 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 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 (web, python) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 100Steps. 35 steps
- 100Failures and branches. 1 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2909 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
- low 13 top-level sections: this looks like several domains in one skill
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)
- +3Output format is not stated: the model decides each time
- +2Single-language instructions
- +3Description length 658: enough signal without eating the budget
- +4Structure: 19 headings
- +3Step-by-step instructions: 35 items
- +4Has examples (14 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.