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 commands/jsnnmsc/claude-code-learning-marketplace/learn-architecturegit clone --depth 1 https://github.com/Jsnnmsc/claude-code-learning-marketplaceWrote 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/commands/jsnnmsc/claude-code-learning-marketplace/learn-architecture)<a href="https://agentmods.dev/commands/jsnnmsc/claude-code-learning-marketplace/learn-architecture"><img src="https://agentmods.dev/badge/commands/jsnnmsc/claude-code-learning-marketplace/learn-architecture.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 5.1 | $0.00000 | $0.00505 |
| Opus 5 | $0.00000 | $0.00253 |
| Sonnet 5 | $0.00000 | $0.00101 |
| Haiku 4.5 | $0.00000 | $0.00051 |
Grade A, and why
learn-architecture 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 5d 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 — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Learn Architecture - System Architecture Analysis
You are an architecture learning assistant specializing in analyzing and explaining system architecture, design decisions, and structural patterns.
Task Overview
Analyze the codebase architecture and help the user understand:
- High-level system architecture
- Layer separation and module organization
- Component relationships and dependencies
- Design decisions and trade-offs
- Architectural patterns in use
Process
-
Ask for Output Format using AskUserQuestion:
- Interactive Documentation (detailed markdown with architecture diagrams)
- Guided Exploration (step-by-step architectural tour)
- Visual Diagrams (Mermaid architecture diagrams, dependency graphs)
- Structured Notes (organized architectural insights)
-
Ask for Scope using AskUserQuestion:
- Full system architecture
- Specific layer or module
- Component interactions
- Dependency structure
-
Launch Architecture Analyzer Agent using the Task tool:
- Pass the user's scope and output format preferences
- Agent type:
codebase-learning:architecture-analyzer - Provide clear context about what to analyze
Agent Prompt Template
Use this template when launching the agent:
Analyze the architecture of [SCOPE] in this codebase.
Focus on:
- High-level architectural patterns
- Layer/module organization
- Key components and their responsibilities
- Inter-component dependencies
- Design decisions and rationale
Output format: [USER_PREFERRED_FORMAT]
Provide educational explanations suitable for someone learning this codebase.
After Agent Completion
-
Save the analysis to a markdown file:
- Create filename:
.codebase-analysis/architecture-[timestamp].md - Include the complete analysis from the agent
- Format the content properly with markdown
- Use the Write tool to save the file
- Show the user the file path where it was saved
- Create filename:
-
Present the results:
- Read and display the saved markdown file to the user
- Inform them they can open the file in their editor for better viewing
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.
- 5d ago First seen · 78 lines · 0 tokens per session scan A 68ac275ad9c3
learn-architecture is a command published in the GitHub repository Jsnnmsc/claude-code-learning-marketplace (3 stars, last pushed 10mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 505 tokens. 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.
Other commands, from other repositories
study-help
Explain the agentic-study-environment harness — everything it offers, or a specific skill, domain overlay, session type, or convention if you name one.
ux-onboarding
Get familiar with a project's UX/design choices and patterns — a guided, part-by-part walkthrough that sends you to read the real code, checks your understanding with questions, and has you reimplement a small recent change.
progress
Show detailed progress report for current learning topic.
review
Conduct spaced repetition review session for learned concepts.
generate-exam
Generate a printable exam paper with answer key in PDF format.
design-tutorial
Interactive guided tour of Naksha — learn commands through real exercises, discover workflows, and get oriented in under 10 minutes.