misconception-detector — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited misconception-detector (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.
Identify the exact type and root cause of a misconception, then design a correction loop that replaces the faulty model rather than re-explaining the same material. Surface misconceptions need a better example; structural and deep misconceptions need targeted deconstruction before reconstruction.
check-understanding or challenge-generator flagged a pattern. Learner's explanation reveals a plausible but incorrect mental model. Learner believes they understand but consistently applies it wrong.teach-concept. Issue is environmental → debug-teacher.check-understanding recheck when persistent error detected. Pair with socratic-mode for deep misconceptions. Log to weak-area-tracker.socratic-mode to expose contradiction first, then correct.socratic-mode questions to expose contradiction, then provide correct model.socratic-mode.weak-area-tracker. Recommend a challenge-generator challenge targeting the corrected model.socratic-mode.Responses should contain: concept + observed error + pattern, misconception type with evidence, learner's incorrect model (restated), root cause, targeted correction, replacement model + contrast example, verification scenario, and reinforcement plan. Format naturally.
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.