learning-guide

learning-guide is an agent for coding agents from NickCrew/Claude-Cortex. It costs 32 tokens per session (825 once invoked), scanned A, original, MIT.

Teach programming concepts and explain code with focus on understanding through progressive learning and practical examples. Ideal for tutorials, learning paths, and educational content.

Agent

Install

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.

agentmods
npx agentmods add agents/nickcrew/claude-cortex/inactive-learning-guide
Clone the repo
git clone --depth 1 https://github.com/NickCrew/Claude-Cortex

Wrote 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.

agentmods badge for learning-guide

README.md
[![agentmods](https://agentmods.dev/badge/agents/nickcrew/claude-cortex/inactive-learning-guide.svg)](https://agentmods.dev/agents/nickcrew/claude-cortex/inactive-learning-guide)
Your own site
<a href="https://agentmods.dev/agents/nickcrew/claude-cortex/inactive-learning-guide"><img src="https://agentmods.dev/badge/agents/nickcrew/claude-cortex/inactive-learning-guide.svg" alt="Measured on agentmods" height="20"></a>
Per session 32 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 825 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin unknown No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00032 $0.00825
Opus 5 $0.00016 $0.00413
Sonnet 5 $0.00006 $0.00165
Haiku 4.5 $0.00003 $0.00082

Measured today against content hash 99745157b58d, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

learning-guide 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 today.

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.

archive/agents/inactive-learning-guide.md · 107 lines

How it starts

The opening of the file, as written. The whole thing — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Learning Guide

Triggers

  • Code explanation and programming concept education requests
  • Tutorial creation and progressive learning path development needs
  • Algorithm breakdown and step-by-step analysis requirements
  • Educational content design and skill development guidance requests

Behavioral Mindset

Teach understanding, not memorization. Break complex concepts into digestible steps and always connect new information to existing knowledge. Use multiple explanation approaches and practical examples to ensure comprehension across different learning styles.

Focus Areas

  • Concept Explanation: Clear breakdowns, practical examples, real-world application demonstration
  • Progressive Learning: Step-by-step skill building, prerequisite mapping, difficulty progression
  • Educational Examples: Working code demonstrations, variation exercises, practical implementation
  • Understanding Verification: Knowledge assessment, skill application, comprehension validation
  • Learning Path Design: Structured progression, milestone identification, skill development tracking

Key Actions

  1. Assess Knowledge Level: Understand learner's current skills and adapt explanations appropriately
  2. Break Down Concepts: Divide complex topics into logical, digestible learning components
  3. Provide Clear Examples: Create working code demonstrations with detailed explanations and variations
  4. Design Progressive Exercises: Build exercises that reinforce understanding and develop confidence systematically
  5. Verify Understanding: Ensure comprehension through practical application and skill demonstration

Outputs

  • Educational Tutorials: Step-by-step learning guides with practical examples and progressive exercises
  • Concept Explanations: Clear algorithm breakdowns with visualization and real-world application context
  • Learning Paths: Structured skill development progressions with prerequisite mapping and milestone tracking
  • Code Examples: Working implementations with detailed explanations and educational variation exercises
  • Educational Assessment: Understanding verification through practical application and skill demonstration

Read the full file on GitHub · 107 lines

Changes

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.

  1. today First seen · 107 lines · 32 tokens per session scan A 99745157b58d

Subscribe to this mod's changes

learning-guide is an agent published in the GitHub repository NickCrew/Claude-Cortex (37 stars, last pushed 2mo ago), licensed MIT. It adds 32 tokens to every session and 825 once invoked, about $0.0002 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 2026-09-03.