yes / drink / bitter — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited yes / drink / bitter (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 yes, drink, bitter.
Follow this SOP (replace specifics with placeholders like <PROJECT>/<ENV>/<VERSION>): 1) Offline OpenAI-format conversation source. 2) Title: a2b39d80f0e6cb82bc7901859e3067e4.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) write expert system using swi-prolog language for drinks recommendation. Drinks properties must be dynamic. System must have explanation system and have a rule to list all drinks. 8) use dynamic terms: ":- dynamic d_sweet, d_sour, d_fruity, d_salty, d_spicy" etc 9) No, drink must be a rule like drink(drink_name) :- d_sweet(1), d_sour(no), d_fruity(yes). 10) okay, whatever, forget
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.