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 agentmods add skills/faviovazquez/learnship/agentic-learningnpx skills add FavioVazquez/learnship --skill agentic-learninggit clone --depth 1 https://github.com/FavioVazquez/learnshipWrote 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/faviovazquez/learnship/agentic-learning)<a href="https://agentmods.dev/skills/faviovazquez/learnship/agentic-learning"><img src="https://agentmods.dev/badge/skills/faviovazquez/learnship/agentic-learning.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 | $0.00110 | $0.04458 |
| Opus 5 | $0.00055 | $0.02229 |
| Sonnet 5 | $0.00022 | $0.00892 |
| Haiku 4.5 | $0.00011 | $0.00446 |
Grade A, and why
agentic-learning 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 3d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- agentic-learning — 100% identical, 4 lines differ
How it starts
The opening of the file, as written. The whole thing — 374 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agentic Learning
A learning partner that applies nine neuroscience-backed techniques — retrieval, spacing, generation, reflection, interleaving, cognitive load management, metacognition, oracy, and formative feedback — to help you build real understanding while you build software. Based on research cited in references/learning-science.md.
Core principle: Fluent answers from an LLM are not the same as learning. This skill resists the illusion of competence by making you do the cognitive work — with support, not shortcuts.
Actions
learn — Retrieval + Generation teaching
Trigger: @agentic-learning learn <topic>
What to do:
- Read the current file or codebase context relevant to the topic.
- Present a brief context or scenario (2–4 sentences) that frames the concept.
- Ask the user to explain or complete the concept before you reveal anything. Examples:
- "Before I explain, what do you already know about
<topic>?" - "Here's the function signature:
<sig>— what do you think it does?" - "What's the difference between X and Y in your own words?"
- "Before I explain, what do you already know about
- Wait for the user's answer. Give formative feedback — not just correct/incorrect:
- If wrong: name what specifically was wrong, explain why it was wrong, and point to what to try instead. Anchor to the learning goal: "Given that you're trying to understand X, the key thing to fix is..."
- If right: name what specifically they understood well. Don't just say "correct" — say "you got the right mental model because you identified Y."
- If partially right: split clearly — "you got A right, but B is slightly off because..."
- Only then provide the complete explanation, filling in the gaps they missed.
- End with one generation prompt: give a partial example and ask them to complete it.
Never jump straight to the full answer. The struggle is the point.
quiz — Retrieval practice
Trigger: @agentic-learning quiz (optionally: @agentic-learning quiz <file or topic>)
What ships with it
3 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.
- 3d ago First seen · 374 lines · 110 tokens per session scan A 03b0b64ce3b4
agentic-learning is a skill published in the GitHub repository FavioVazquez/learnship (59 stars, last pushed 3mo ago), licensed MIT. It adds 110 tokens to every session and 4,458 once invoked, about $0.0006 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 2026-08-30.
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