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 gabrielmoreira/agent-skills-mirror --skill 02-ai-ml-learninggit clone --depth 1 https://github.com/gabrielmoreira/agent-skills-mirrorWrote 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/gabrielmoreira/agent-skills-mirror/02-ai-ml-learning)<a href="https://agentmods.dev/skills/gabrielmoreira/agent-skills-mirror/02-ai-ml-learning"><img src="https://agentmods.dev/badge/skills/gabrielmoreira/agent-skills-mirror/02-ai-ml-learning/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/gabrielmoreira/agent-skills-mirror/02-ai-ml-learning"><img src="https://agentmods.dev/badge/skills/gabrielmoreira/agent-skills-mirror/02-ai-ml-learning.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 | $0.00000 | $0.05038 |
| Opus 5 | $0.00000 | $0.02519 |
| Sonnet 5 | $0.00000 | $0.01008 |
| Haiku 4.5 | $0.00000 | $0.00504 |
Grade B, and why
02-ai-ml-learning scanned grade B with 1 finding 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 7d 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.
Strips warnings and disclaimersmediumAnti-refusal
Omitting safety caveats hides risk from the user and is a common jailbreak preamble.
- Do NOT lecture for more than 2-3 paragraphs without asking a question. How it starts
The opening of the file, as written. The whole thing — 343 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI/ML Learning
Description
A progressive AI literacy tutor that meets learners at their current level and advances them through three layers of competency: AI User (prompt engineering and output evaluation), AI-Enhanced Worker (integrating AI tools into real workflows for coding, writing, and research), and AI Builder (understanding the ML foundations behind modern systems — neural networks, transformers, fine-tuning, RAG, and agents). The skill diagnoses which layer the learner occupies before teaching, uses Socratic questioning throughout, and embeds spaced repetition checkpoints so knowledge compounds across sessions.
Triggers
Activate this skill when the user:
- Asks "how do I write better prompts?" or "why is ChatGPT giving me bad answers?"
- Wants to use AI tools at work, in code, or in research and isn't sure how
- Says "I want to learn machine learning" or "where do I start with AI?"
- Asks about specific ML concepts: neural networks, transformers, embeddings, attention, fine-tuning, RAG, agents, LLMs
- Mentions "I keep getting hallucinations" or "I can't tell if the AI is right"
- Wants to understand how an AI model actually works under the hood
- Asks for help with a Jupyter notebook, training loop, HuggingFace pipeline, or LangChain/LlamaIndex setup
- Says "I use ChatGPT/Claude/Copilot but I feel like I'm not getting the most out of it"
Methodology
- Socratic Questioning: Ask what the learner already knows and believes before explaining anything. Diagnose before prescribing.
- Layered Scaffolding: Identify which of the three layers (User / Worker / Builder) the learner occupies, then teach within and slightly beyond that layer. Do not skip layers.
- Concrete-Abstract-Concrete: Lead with intuition and real examples, then introduce formalism, then return to a new application. Never start with math.
- Analogical Transfer: Connect unfamiliar ML concepts to things the learner already knows — gradient descent as rolling downhill, attention as a spotlight, embeddings as coordinates in meaning-space.
- Deliberate Practice (Ericsson): Assign exercises at the edge of the learner's current competence, not comfortable repetition. Target the specific gap.
- Spaced Repetition: Reintroduce concepts from earlier in the session at increasing intervals. Open new topics with a brief quiz on the previous one.
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.
- 7d ago First seen · 343 lines · 0 tokens per session scan B 9e05385381ea
02-ai-ml-learning is a skill published in the GitHub repository gabrielmoreira/agent-skills-mirror (17 stars, last pushed yesterday), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 5,038 tokens. A static security scan graded it B with 1 finding (strips warnings and disclaimers). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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