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/dotclaude/marketplace/learn_architecturegit clone --depth 1 https://github.com/dotclaude/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/dotclaude/marketplace/learn_architecture)<a href="https://agentmods.dev/commands/dotclaude/marketplace/learn_architecture"><img src="https://agentmods.dev/badge/commands/dotclaude/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 | $0.00010 | $0.02952 |
| Opus 5 | $0.00005 | $0.01476 |
| Sonnet 5 | $0.00002 | $0.00590 |
| Haiku 4.5 | $0.00001 | $0.00295 |
Grade A, and why
learn_architecture scanned grade A 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 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- Examine actual API endpoints with curl or Postman How it starts
The opening of the file, as written. The whole thing — 309 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Architectural Learning System
Guide systematic architecture understanding through progressive complexity building, pattern recognition development, and hands-on exploration with adaptive scaffolding. Transform complex system architecture into accessible learning journeys that build deep understanding through guided discovery and practical investigation.
Learning Objective Framework
Comprehension Level (Understanding existing architecture)
[Extended thinking: Focus on understanding decisions already made, components already in place, and relationships already established in existing systems.]
Learning Goals:
- Component Understanding: Identify and understand individual system components and their responsibilities
- Relationship Mapping: Understand how components interact and depend on each other
- Decision Rationale: Comprehend why specific architectural choices were made
- Pattern Recognition: Identify common architectural patterns and their applications
- Trade-off Awareness: Understand benefits and costs of current architectural decisions
Exploration Methods:
- System documentation analysis with guided comprehension
- Component deep-dive investigation with scaffolded complexity
- Data flow tracing with step-by-step pathway exploration
- Interface examination with interaction pattern analysis
- Historical evolution study with decision context understanding
Analysis Level (Evaluating architectural trade-offs)
[Extended thinking: Develop critical evaluation skills for assessing architectural decisions, comparing alternatives, and understanding implications.]
Learning Goals:
- Trade-off Evaluation: Analyze benefits and costs of architectural decisions
- Alternative Assessment: Compare different approaches and understand their implications
- Quality Attribute Analysis: Evaluate architecture against performance, security, maintainability criteria
- Scalability Assessment: Understand how architecture handles growth and change
- Risk Identification: Recognize potential architectural vulnerabilities and limitations
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 · 309 lines · 10 tokens per session scan A c22e5602d8c0
learn_architecture is a command published in the GitHub repository dotclaude/marketplace (43 stars, last pushed 5mo ago), licensed MIT. It adds 10 tokens to every session and 2,952 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
lrn
Execute the /vibeguard:learn command. $ARGUMENTS.
review
Cold re-quiz on code that already shipped — your own session commits, not the change in front of you.
annex-a-deep-dive
Deep dive analysis of ISO 27001 Annex A control domains with implementation guidance.
start-10-1
Command "start-10-1" from minicoohei/ai-agent-camp, covering 🎓 lesson 10-1: clasp基本・gasプロジェクト管理, 📍 このセッションでやること, 🎯 準備チェック, 🚀 step 1: claspのインストールと apps script api の確認 and 🚀 step 2: google認証.
start-13-4.en
Welcome to Lesson 13-4: Landing Page Implementation!
learn-story-flow
Learn story-flow concepts with interactive guidance for junior developers.