Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add yugash007/edu-agent-skills --skill misconception-detectorgit clone --depth 1 https://github.com/yugash007/edu-agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/yugash007/edu-agent-skills/misconception-detector)<a href="https://agentmods.dev/skills/yugash007/edu-agent-skills/misconception-detector"><img src="https://agentmods.dev/badge/skills/yugash007/edu-agent-skills/misconception-detector/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/yugash007/edu-agent-skills/misconception-detector"><img src="https://agentmods.dev/badge/skills/yugash007/edu-agent-skills/misconception-detector.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00026 | $0.00790 |
| Opus 5 | $0.00013 | $0.00395 |
| Sonnet 5 | $0.00005 | $0.00158 |
| Haiku 4.5 | $0.00003 | $0.00079 |
Grade A, and why
misconception-detector scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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-understandingorchallenge-generatorflagged 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-understandingrecheck when persistent error detected. Pair withsocratic-modefor deep misconceptions. Log toweak-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-modeto expose contradiction first, then correct.
Workflow
- Classify — Determine type (surface/structural/deep) with supporting evidence. State classification before proceeding.
- Articulate — Restate the learner's incorrect model precisely and without judgment. Confirm with learner that this represents their belief.
- Root Cause — Identify what produced the misconception: overgeneralization, ambiguous terminology, bad analogy, missing prerequisite.
- Deconstruct — Surface: correct definition + contrast. Structural: step-by-step mechanism trace. Deep:
socratic-modequestions to expose contradiction, then provide correct model. - 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.
- Verify — Ask learner to apply corrected model to a novel scenario with explanation. If error persists: escalate to
socratic-mode. - Reinforce — Log to
weak-area-tracker. Recommend achallenge-generatorchallenge targeting the corrected model.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 62 lines · 26 tokens per session scan A fd1ca57efaf0
misconception-detector is a skill published in the GitHub repository yugash007/edu-agent-skills (7 stars, last pushed 3mo ago), licensed MIT. It adds 26 tokens to every session and 790 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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