visit / day / tainan — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited visit / day / tainan (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
General SOP for common requests related to payment, ecompensa, companies.
Follow this SOP (replace specifics with placeholders like <PROJECT>/<ENV>/<VERSION>): 1) Offline OpenAI-format conversation source. 2) Title: cf953c4204c9eef558ce8beb44f68f96.json#conv_1 3) Use the user questions below as the PRIMARY extraction evidence. 4) Use the full conversation below as SECONDARY context reference. 5) In the full conversation section, assistant/model replies are reference-only and not skill evidence. 6) Primary User Questions (main evidence): 7) multilateral netting algorithm to clear payments 8) multilateral netting algorithm python or java 9) privacy-preserving netting algorithm 10) algorithm to reduce debts between companies
For each step, include: action, checks, and failure rollback/fallback plan. Output format: for each step number, provide status/result and what to do next.
Input:
Break this into best-practice, executable steps.
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.