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 teach-conceptgit 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/teach-concept)<a href="https://agentmods.dev/skills/yugash007/edu-agent-skills/teach-concept"><img src="https://agentmods.dev/badge/skills/yugash007/edu-agent-skills/teach-concept/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/teach-concept"><img src="https://agentmods.dev/badge/skills/yugash007/edu-agent-skills/teach-concept.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 | $0.00024 | $0.00573 |
| Opus 5 | $0.00012 | $0.00287 |
| Sonnet 5 | $0.00005 | $0.00115 |
| Haiku 4.5 | $0.00002 | $0.00057 |
Grade A, and why
teach-concept 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 12d 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 — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Explain technical concepts correctly, incrementally, and adaptively. Build from definition to application in one teaching arc.
Activation
- User asks for explanation of a concept, pattern, algorithm, or system behavior.
- User asks "why" or "how" questions requiring conceptual grounding.
- User is struggling to connect theory to code.
- Skip if: user wants only a command/answer with no teaching, or explicitly declines teaching.
- Routing: run
repo-understandfirst if repo context exists but isn't mapped. Hand off tocheck-understandingafter major explanations.
Inputs
- Target concept, learner level signals, prior conversation context, relevant repo/project context.
Workflow
- Calibrate — Infer learner level from prompt and prior turns. State assumptions; keep first explanation conservative.
- Anchor — Give a short definition and one core intuition sentence.
- Concrete Example — One practical code or system example before any abstraction. Prefer the learner's current project/repo.
- Deepen — Add one layer of complexity at a time. Introduce terminology only when needed. Explain tradeoffs and failure cases, not just happy path.
- Recall Check — Ask a reasoning question requiring explanation, not repetition. If confusion appears, simplify and reframe with a new analogy.
- Transfer — Give one small implementation or debugging task to apply the concept.
Rules
- DO: one core idea per step; example before abstraction; verify understanding before escalating.
- DO: ground examples in the learner's project/repo when possible.
- DO: explain tradeoffs and failure cases alongside happy-path behavior.
- DON'T: dump multiple concepts at once — cap each response to one primary concept plus one extension.
- DON'T: end without an active-recall question and a transfer task.
- DON'T: skip the concrete example — no abstract-only teaching.
- DON'T: keep teaching at the same level if learner asks repeated clarifications — run a calibration checkpoint and reduce depth.
What ships with it
1 file 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.
- 12d ago First seen · 56 lines · 24 tokens per session scan A 55d4809b901e
teach-concept is a skill published in the GitHub repository yugash007/edu-agent-skills (7 stars, last pushed 3mo ago), licensed MIT. It adds 24 tokens to every session and 573 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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