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 JayRHa/AgentSkills --skill concept-explainergit clone --depth 1 https://github.com/JayRHa/AgentSkillsWrote 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/jayrha/agentskills/concept-explainer)<a href="https://agentmods.dev/skills/jayrha/agentskills/concept-explainer"><img src="https://agentmods.dev/badge/skills/jayrha/agentskills/concept-explainer/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/jayrha/agentskills/concept-explainer"><img src="https://agentmods.dev/badge/skills/jayrha/agentskills/concept-explainer.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 | $0.00115 | $0.00969 |
| Opus 5.5 | $0.00046 | $0.00388 |
| Sonnet 5.5 | $0.00023 | $0.00194 |
| Haiku 4.5 | $0.00012 | $0.00097 |
Grade A, and why
concept-explainer 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 1mo 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 — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Concept Explainer
Overview
Make hard ideas click. Calibrate to the learner's level, build from what they already know, use a vivid analogy, then layer in precision and a concrete example. End by checking understanding.
Keywords: explain, ELI5, teach, understand, intuition, analogy, mental model, break down, simplify, deep dive, learning, tutoring.
Workflow
- Calibrate the level. Infer or ask the audience level: child / curious novice / practitioner / expert. Match vocabulary and assumed background. When unstated, default to "smart person new to this field" and offer to go deeper.
- Anchor to prior knowledge. Start from something the learner already understands and bridge to the new idea ("you know how X works? This is like that, except…").
- Lead with one strong analogy. Pick a single, accurate analogy (see techniques in
references/techniques.md). State where the analogy holds and, crucially, where it breaks down — bad analogies that aren't bounded create misconceptions. - Give the precise definition after the intuition, not before. Intuition first, formality second.
- Show a concrete worked example. Make the abstract tangible with real numbers, code, or a scenario.
- Layer depth progressively. Structure as: one-sentence gist → short paragraph → deeper mechanics. Let the reader stop at the depth they need.
- Address the common misconception for the topic head-on ("A lot of people think X — actually…").
- Check understanding. End with a quick question, a "you'd now be able to…" statement, or an invitation to go deeper.
Decision Framework
| Audience | Style |
|---|---|
| Child (ELI5) | Everyday analogy, no jargon, short sentences, concrete objects |
| Curious novice | Analogy + plain definition + one example; define each term once |
| Practitioner | Less analogy, more mechanics, trade-offs, when to use it |
| Expert | Skip basics; focus on nuance, edge cases, precise terminology |
Worked Example (explaining "database index", novice level)
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.
- 1mo ago First seen · 66 lines · 115 tokens per session scan A 13dd58690d02
concept-explainer is a skill published in the GitHub repository JayRHa/AgentSkills (12 stars, last pushed 2mo ago), licensed MIT. It adds 115 tokens to every session and 969 once invoked, about $0.0005 per session on Opus 5.5. 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.
Other skills, from other repositories
code-explainer
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ailmanac
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student-learning
Use when the user wants to learn or study a topic, understand a concept, prepare for an exam, or be tutored. Teaches with Socratic questioning and teach-back so the user actually learns — it does not just hand over answers to copy.
canvas
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rwkv-architecture
RNN+Transformer hybrid with O(n) inference. Linear time, infinite context, no KV cache. Train like GPT (parallel), infer like RNN (sequential). Linux Foundation AI project. Production at Windows, Office, NeMo. RWKV-7 (March 2025). Models up to 14B parameters.
build-interactive-explainers
A guide for building interactive explainers, calculators, and simulations driven by an executable model. Users change inputs, steps, states, or events to understand a rule or see how a process develops over time.