agentsclimarketplace

Misconception detector

Skill yugash007/edu-agent-skills/skills/assessment/misconception-detector

Reusable educational skills for AI coding agents. Turn agents (Gemini CLI, Claude Code, Cursor, etc.) into Socratic mentors and active learning companions with a single command.

Install
npx -y skills add yugash007/edu-agent-skills --skill misconception-detector

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 4 stars4 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Use when diagnosing a repeated conceptual mistake and designing a targeted correction loop to replace the faulty mental model.

SKILL.md

3.9 KB, as published. Nobody here has run it

Purpose

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.

Activation

  • Learner makes the same conceptual error repeatedly. 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.
  • Skip if: one-time execution mistake with no conceptual root. Concept hasn't been taught yet → teach-concept. Issue is environmental → debug-teacher.
  • Routing: run before check-understanding recheck when persistent error detected. Pair with socratic-mode for deep misconceptions. Log to weak-area-tracker.

Inputs

  • Learner's incorrect statement/reasoning, concept being misunderstood, prior error history, correct mental model.

Misconception Types

  • Surface: wrong terminology/label, underlying model partially correct → fix with clear definition + contrast example.
  • Structural: wrong causal model — knows vocabulary but has mechanism wrong → fix with step-by-step worked trace.
  • Deep: fundamentally wrong model conflicting with multiple related concepts → fix with socratic-mode to expose contradiction first, then correct.

Workflow

  1. Classify — Determine type (surface/structural/deep) with supporting evidence. State classification before proceeding.
  2. Articulate — Restate the learner's incorrect model precisely and without judgment. Confirm with learner that this represents their belief.
  3. Root Cause — Identify what produced the misconception: overgeneralization, ambiguous terminology, bad analogy, missing prerequisite.
  4. Deconstruct — Surface: correct definition + contrast. Structural: step-by-step mechanism trace. Deep: socratic-mode questions to expose contradiction, then provide correct model.
  5. Install Correct Model — State the replacement model explicitly. Provide a concrete example that only makes sense under the correct model. Contrast with what the incorrect model would have predicted.
  6. Verify — Ask learner to apply corrected model to a novel scenario with explanation. If error persists: escalate to socratic-mode.
  7. Reinforce — Log to weak-area-tracker. Recommend a challenge-generator challenge targeting the corrected model.

Rules

  • DO: classify before correcting — type determines strategy.
  • DO: restate and confirm the learner's incorrect model before correcting it.
  • DO: provide an explicit replacement model, not just negation ("that's wrong").
  • DO: require learner to apply corrected model to a novel case before closing.
  • DON'T: correct by repeating the original explanation louder or longer.
  • DON'T: assume the misconception — confirm with learner first.
  • DON'T: repeat the same correction strategy 3 times — if second attempt fails, escalate to socratic-mode.
  • DON'T: frame misconceptions as failures — they're signs of engaged learning.

Output

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.

Checklist

  • Misconception type classified with evidence.
  • Learner's incorrect model restated and confirmed.
  • Replacement model explicitly provided with contrast example.
  • Verification requires novel application.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.