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AC didit-aml-screening

Integrate Didit AML Screening standalone API to screen individuals or companies against global watchlists. Use when the user wants to perform AML checks, screen against sanctions lists, check PEP status, detect adverse media, implement KYC/AML compliance, screen against OFAC/UN/EU watchlists, calculate risk scores, or perform anti-money laundering screening using Didit. Supports 1300+ databases, fuzzy name matching, configurable scoring weights, and continuous monitoring.

modbender/skill-library-mcp Agent Skills author: modbender MIT 2 files body ≈ 2 425 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Integrate Didit AML Screening standalone API to screen individuals or companies against global watchlists.

As a process C 63/100 · Has gaps — weak spots: inputs and preconditions, running it twice, progress reporting

IntegrationInfrastructureSecuritytype 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%
90
Run on models
none yet
Process rating
C
63/100
Has gaps
Inputs and preconditions w 11
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: 2. 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 63/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 1 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 13 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2425 tokens
    • low 12 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)
    • +1No license
    • +2Single-language instructions
    • +3Description length 476: enough signal without eating the budget
    • +4Structure: 21 headings
    • +3Step-by-step instructions: 13 items
    • +3Output format is stated explicitly
    • +4Has examples (7 code blocks)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 90.