OpenAgentSkill
GitHub200+

Laporan audit skill

didit-aml-screening Laporan audit.

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.

Diblokir · BlokirPerlu ditinjauDihasilkan 13 Okt 2026Audit metadata heuristik
68
Audit
61
Kepercayaan
55
Kualitas
68
Keamanan
88
Maintain
92
Pasang

Trust Score OpenAgentSkill

61
Tinjauan manual

Trust Score OpenAgentSkill

The Trust Score helps an agent decide whether a skill is safe enough to shortlist before installation.

Adopsi GitHub

Peringatan

48

26 star GitHub

Aktivitas star/fork

Peringatan

43

26 star dan 4 fork; aktivitas issue tidak tersedia dalam metadata saat ini

Pemeliharaan terbaru

Lulus

88

2 bulan sejak push

Kejelasan lisensi

Lulus

86

MIT

Kelengkapan README/SKILL.md

Lulus

86

Metadata memuat konteks penggunaan dan alur kerja yang cukup

Risiko dependensi/runtime

Gagal

36

command execution surface, credential or environment access

Ketersediaan pemasangan

Lulus

92

npx skills add didit-protocol/skills --skill didit-aml-screening

Keamanan perintah pemasangan

Lulus

92

Jalur pemasangan paket atau runtime standar

Cakupan izin

Gagal

22

secrets or environment access, shell or command execution

Bukti repositori

Lulus

86

https://github.com/didit-protocol/skills/tree/main/skills/didit-aml-screening

Status peninjauan

Info

66

Data tinjauan AI tersedia

Hasil terbukti Agent

Info

54

Belum ada data hasil Agent

Pemeriksaan

Tinjauan pemasangan dan adopsi

6 Lulus · 18 Perlu ditinjau

Jalur pemasangan

92

Lulus

npx skills add didit-protocol/skills --skill didit-aml-screening

Repositori

88

Lulus

https://github.com/didit-protocol/skills/tree/main/skills/didit-aml-screening

Lisensi

86

Lulus

MIT

Pemeliharaan

88

Lulus

2 bulan sejak push

Tinjauan AI

55

Periksa

The utility script `screen_aml.py` uses `hit.get('name')` and `hit.get('categories')` but the API response uses `caption` and `datasets` respectively, which may cause incorrect output formatting.

Kelengkapan README/SKILL.md

86

Lulus

Usable description available

Risiko dependensi

36

Perbaiki

command execution surface, credential or environment access

Keamanan perintah pemasangan

92

Lulus

Jalur pemasangan paket atau runtime standar

Cakupan izin

22

Perbaiki

secrets or environment access, shell or command execution

Aktivitas star/fork

43

Perbaiki

26 star dan 4 fork; aktivitas issue tidak tersedia dalam metadata saat ini

Adopsi

42

Periksa

26 star GitHub

Financial decision safety

58

Periksa

Research-only use: do not treat output as financial advice or execute a position without human approval.

Peringatan

  • Dependency or permission surface needs review
  • Permission surface may require sandboxing
  • Financial research output is not financial advice; require human review before any live investment decision
  • The utility script `screen_aml.py` uses `hit.get('name')` and `hit.get('categories')` but the API response uses `caption` and `datasets` respectively, which may cause incorrect output formatting.
  • The script does not handle cases where the `aml` key is missing from the response, which could lead to a KeyError.
  • Low GitHub adoption signal
  • Financial research output is not financial advice; require human review before any live investment decision.
  • Quality score needs review
  • Permission surface needs review: secrets or environment access, shell or command execution
  • GitHub adoption: 26 GitHub stars
  • Stars/forks activity: 26 stars, 4 forks; issue activity unavailable in current metadata
  • Dependency/runtime risk: command execution surface, credential or environment access

Metode

This report combines public metadata, AI review output, repository freshness, install readiness, OpenAgentSkill events, quality scoring, trust checks, and the agent safety gate. It is not a full source-code security review.

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