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 simplify-topicgit 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/simplify-topic)<a href="https://agentmods.dev/skills/yugash007/edu-agent-skills/simplify-topic"><img src="https://agentmods.dev/badge/skills/yugash007/edu-agent-skills/simplify-topic.svg" alt="Measured on agentmods" 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 | $0.00028 | $0.00820 |
| Opus 5 | $0.00014 | $0.00410 |
| Sonnet 5 | $0.00006 | $0.00164 |
| Haiku 4.5 | $0.00003 | $0.00082 |
Grade A, and why
simplify-topic 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 8d 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 — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Reframe a concept at a lower abstraction so the learner builds accurate intuition before encountering technical complexity. Simplification is choosing the right abstraction for the learner's mental model, then re-injecting complexity once the foundation is stable.
Activation
- Learner says "I don't understand" or "explain like I'm new."
check-understandingreveals prior explanation was too abstract/jargon-heavy. Cross-domain learner needs an analogy bridge.teach-conceptwas used and learner still can't state the core intuition. - Skip if: learner understands basics and needs depth →
deep-dive. Confusion is from a misconception →misconception-detector. Concept is simple enough already. - Routing: after successful simplification, use
teach-conceptto re-introduce correct technical terminology. Never leave learner permanently at simplified level.
Inputs
- Concept to simplify, learner's background domain, abstraction level where understanding broke, specific unclear aspect.
Abstraction Levels
- Level 0 (Everyday): household/daily life analogy, zero technical vocab → absolute beginner.
- Level 1 (Domain-Adjacent): analogy from learner's known field → practitioner switching domains.
- Level 2 (Simplified Technical): correct terms, simplified mechanism → beginner with some background.
- Level 3 (Full Technical): precise mechanism with edge cases → standard
teach-conceptlevel.
Workflow
- Detect — Ask one question to identify where understanding breaks (terminology? mechanism? motivation?). Identify learner's domain for analogy selection. Select starting level (0, 1, or 2).
- Build Analogy — One analogy from the learner's known domain. State explicitly where it holds AND where it breaks down — oversimplified analogies create new misconceptions.
- Simplified Explanation — Deliver at selected level using the analogy as scaffold. One core idea per step. No technical vocabulary at level 0; introduce terms one at a time at level 2.
- Intuition Check — Ask learner to restate in their own words using the analogy. If wrong: identify failure point and rebuild with a different analogy.
- Complexity Re-Injection — Introduce one layer of technical accuracy on top of confirmed intuition. Replace analogy language with correct terms, one at a time. Confirm at each step.
- Handoff — When learner articulates using correct vocabulary: hand off to
teach-conceptordeep-dive.
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.
- 8d ago First seen · 61 lines · 28 tokens per session scan A 46631bd45b5f
simplify-topic is a skill published in the GitHub repository yugash007/edu-agent-skills (7 stars, last pushed 3mo ago), licensed MIT. It adds 28 tokens to every session and 820 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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