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/bdiasti/maestro-bundle-cli/clean-architecturenpx skills add bdiasti/maestro-bundle-cli --skill clean-architecturegit clone --depth 1 https://github.com/bdiasti/maestro-bundle-cliWrote 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/bdiasti/maestro-bundle-cli/clean-architecture)<a href="https://agentmods.dev/skills/bdiasti/maestro-bundle-cli/clean-architecture"><img src="https://agentmods.dev/badge/skills/bdiasti/maestro-bundle-cli/clean-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 | $0.00039 | $0.01628 |
| Opus 5 | $0.00019 | $0.00814 |
| Sonnet 5 | $0.00008 | $0.00326 |
| Haiku 4.5 | $0.00004 | $0.00163 |
Grade A, and why
clean-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 — 192 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Clean Architecture
Structure code into Domain, Application, and Infrastructure layers with strict dependency rules, ensuring business logic is isolated from frameworks and external services.
When to Use
- Starting a new module or microservice
- Refactoring code that mixes business logic with infrastructure
- Creating entities, value objects, use cases, or repositories
- Reviewing code for layer violations
- Setting up project structure for a new feature
Available Operations
- Define domain entities with business rules and events
- Create application use cases that orchestrate domain logic
- Implement infrastructure adapters (repositories, HTTP clients)
- Set up dependency injection wiring
- Validate layer dependencies (no inward violations)
Multi-Step Workflow
Step 1: Set Up the Layer Structure
Create the directory structure following the dependency rule: outer layers depend on inner layers, never the reverse.
mkdir -p src/domain/entities src/domain/repositories src/domain/value_objects src/domain/events
mkdir -p src/application/use_cases src/application/dtos
mkdir -p src/infrastructure/persistence src/infrastructure/http
mkdir -p src/api/controllers
+------------------------------+
| API / CLI | <- Controllers, Routers
+------------------------------+
| APPLICATION | <- Use Cases, DTOs
+------------------------------+
| DOMAIN | <- Entities, VOs, Events, Repos (interface)
+------------------------------+
| INFRASTRUCTURE | <- DB, HTTP clients, Frameworks
+------------------------------+
Dependency Rule: arrows point INWARD (infra -> domain)
Domain NEVER imports from infrastructure
Step 2: Build the Domain Layer
Domain contains entities with business rules, value objects, domain events, and repository interfaces (ports).
# domain/entities/demand.py
class Demand:
def __init__(self, id: DemandId, description: str):
self._id = id
self._description = description
self._status = DemandStatus.CREATED
self._events: list[DomainEvent] = []
def decompose(self, planner: TaskPlanner) -> list[Task]:
if self._status != DemandStatus.CREATED:
raise DemandAlreadyDecomposedException(self._id)
tasks = planner.plan(self._description)
self._status = DemandStatus.PLANNED
self._events.append(DemandDecomposed(self._id, [t.id for t in tasks]))
return tasks
@property
def pending_events(self) -> list[DomainEvent]:
return list(self._events)
# domain/repositories/demand_repository.py (PORT -- interface only)
from abc import ABC, abstractmethod
class DemandRepository(ABC):
@abstractmethod
def find_by_id(self, id: DemandId) -> Demand: ...
@abstractmethod
def save(self, demand: Demand) -> None: ...
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 · 192 lines · 39 tokens per session scan A bd0ca04d860b
clean-architecture is a skill published in the GitHub repository bdiasti/maestro-bundle-cli (21 stars, last pushed 5mo ago), licensed MIT. It adds 39 tokens to every session and 1,628 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 2026-08-30.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…
chronicle
Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…