remove-hot-job — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited remove-hot-job (Agent Skill) and scored it 96/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 1 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 1 flagged
A bulleted imperative like {match} tells the agent to never reveal, disclose, or mention something to the user. Used adversarially it can instruct the agent to hide its tool calls or lie about what it did — stripping the transparency a user relies on to trust the agent.
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.
Manage the hot jobs list in daily_hot_jobs.json. Hot jobs are sticky LinkedIn listings shown at the top of the daily email, 5 per category (or 8 for Tech Lead / AI categories).
[company, title] pairs. Blocklisted jobs never reappear in that category.Senior JavaBackend JavaProduct OwnerAssistant Project ManagerTech Lead / Lead DeveloperAI / GenAI EngineerRemove a single job (applied or dismissed) — remove it from current_jobs[category]. If dismissed (not applied), also add [company.lower(), title.lower()] to blocklist.
Clear all jobs in a category — set current_jobs[category] = []. For each job, blocklist it unless the user says they applied to it (or it's already in the tracker).
Remove from blocklist — find and delete the matching [company, title] pair from the blocklist array.
List current hot jobs for a category — read and print current_jobs[category].
daily_hot_jobs.json.[company.lower().strip(), title.lower().strip()] — lowercase, no leading/trailing spaces.~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.