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 nortonx/ai-tooling-free --skill learngit clone --depth 1 https://github.com/nortonx/ai-tooling-freeWrote 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/nortonx/ai-tooling-free/learn)<a href="https://agentmods.dev/skills/nortonx/ai-tooling-free/learn"><img src="https://agentmods.dev/badge/skills/nortonx/ai-tooling-free/learn/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/nortonx/ai-tooling-free/learn"><img src="https://agentmods.dev/badge/skills/nortonx/ai-tooling-free/learn.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 | $0.00015 | $0.00701 |
| Opus 5.5 | $0.00006 | $0.00280 |
| Sonnet 5.5 | $0.00003 | $0.00140 |
| Haiku 4.5 | $0.00002 | $0.00070 |
Grade A, and why
learn 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 — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Arguments
<concept>
- Required. The concept, term, or technology you want explained in depth.
- Examples:
/learn user story,/learn Wilson score interval,/learn closure in JavaScript
Copilot CLI note:
$ARGUMENTSdoes not substitute in skills; include the argument inline in your prompt.
Explain Concept: $ARGUMENTS
Explain a software development concept, algorithm, tool, or architectural pattern clearly and thoroughly across multiple conceptual depths.
Step 1: Validate Input
If $ARGUMENTS is empty, missing, or whitespace, stop and output:
Usage:
/learn <concept>Please provide the concept, term, or technology you want explained.
Then stop.
Step 2: Adaptive Multi-Depth Explanation
Synthesize the concept across structured levels to ensure both rapid intuition and deep technical mastery:
- Simple Summary (ELI5): Explain the concept in plain, non-jargon language that any developer or stakeholder can grasp immediately.
- Technical Mechanics: Detail how it works under the hood: data flow, memory model, execution phases, or state transitions.
- Mental Model: Provide an intuitive analogy from daily life or foundational computer science.
- Code Example: Provide a concise, runnable, and idiomatic code snippet illustrating the concept in practice.
- Common Pitfalls: Highlight common mistakes, anti-patterns, and performance or security traps.
- Best Practices: State concrete production recommendations and guidelines for when and when not to use it.
- Related Concepts: Connect the topic to 2-3 adjacent concepts, tools, or design patterns.
Output Format
Output using this structured markdown template:
# Learn: <Concept>
## 1. Simple Summary (ELI5)
<Plain language explanation capturing core value>
## 2. Technical Mechanics
<Detailed explanation of underlying architecture, algorithms, and lifecycle>
## 3. Mental Model
<Concrete analogy explaining the concept intuitively>
## 4. Code Example
```<language>
// Minimal, self-contained, and runnable example
5. Common Pitfalls
- <Pitfall 1>:
- <Pitfall 2>:
6. Best Practices
- <Practice 1>:
- <Practice 2>:
7. Related Concepts
- :
- :
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 Changed · +50 lines d0d0c26c014f
- 1mo ago First seen · 34 lines · 15 tokens per session scan A de2819a522aa
learn is a skill published in the GitHub repository nortonx/ai-tooling-free (1 stars, last pushed 18d ago), licensed MIT. It adds 15 tokens to every session and 701 once invoked, about $0.0001 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.
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This page used to hold an archived copy of this skill. The archive now lives in the registry's data repository.