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 skills/matteocervelli/llms/architecture-plannernpx skills add matteocervelli/llms --skill architecture-plannergit clone --depth 1 https://github.com/matteocervelli/llmsWrote 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/matteocervelli/llms/architecture-planner)<a href="https://agentmods.dev/skills/matteocervelli/llms/architecture-planner"><img src="https://agentmods.dev/badge/skills/matteocervelli/llms/architecture-planner.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 | $0.00024 | $0.03338 |
| Opus 5 | $0.00012 | $0.01669 |
| Sonnet 5 | $0.00005 | $0.00668 |
| Haiku 4.5 | $0.00002 | $0.00334 |
Grade A, and why
architecture-planner 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 — 529 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
The architecture-planner skill provides comprehensive guidance for designing component architecture and module structure in feature implementations. This skill helps the Architecture Designer agent plan clean, layered architectures following established patterns such as Layered Architecture, Hexagonal Architecture (Ports and Adapters), and Clean Architecture principles.
This skill emphasizes:
- Clear component boundaries with single responsibilities
- Layer separation between interfaces, business logic, and data access
- Dependency injection for testability and flexibility
- Extension points for future enhancements
- Design patterns that improve maintainability
The architecture-planner skill is essential for creating implementations that are easy to test, maintain, and extend over time.
When to Use
This skill auto-activates when the agent describes:
- "Plan component architecture for..."
- "Design module structure with..."
- "Separate concerns into layers..."
- "Structure the codebase with..."
- "Organize components using..."
- "Define interfaces between..."
- "Create extension points for..."
- "Apply architectural pattern..."
Provided Capabilities
1. Component Identification and Boundaries
What it provides:
- Identification of distinct components based on responsibilities
- Clear component boundaries following Single Responsibility Principle
- Component naming conventions and file organization
- Determination of component granularity (not too large, not too small)
Guidance:
- Each component should have ONE primary responsibility
- Components should be independently testable
- Components should have minimal coupling with others
- Use descriptive names that reflect the component's purpose
Example:
# Good: Clear, focused components
components = [
{
"name": "FeatureProcessor",
"responsibility": "Process feature requests according to business rules",
"file": "src/core/processor.py"
},
{
"name": "DataValidator",
"responsibility": "Validate input data against schemas",
"file": "src/core/validator.py"
},
{
"name": "ResultFormatter",
"responsibility": "Format processing results for output",
"file": "src/core/formatter.py"
}
]
# Bad: Too broad, multiple responsibilities
components = [
{
"name": "FeatureHandler",
"responsibility": "Process, validate, format, store, and log features",
"file": "src/feature_handler.py" # Too many responsibilities!
}
]
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 529 lines · 24 tokens per session scan A a52d5483f00d
architecture-planner is a skill published in the GitHub repository matteocervelli/llms (25 stars, last pushed 3mo ago), licensed MIT. It adds 24 tokens to every session and 3,338 once invoked, about $0.0001 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 2026-09-01.
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