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 skills add j4flmao/agent-skills --skill microservicesgit clone --depth 1 https://github.com/j4flmao/agent-skillsWrote 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/j4flmao/agent-skills/microservices)<a href="https://agentmods.dev/skills/j4flmao/agent-skills/microservices"><img src="https://agentmods.dev/badge/skills/j4flmao/agent-skills/microservices/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/j4flmao/agent-skills/microservices"><img src="https://agentmods.dev/badge/skills/j4flmao/agent-skills/microservices.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00747 |
| Opus 5 | $0.00012 | $0.00374 |
| Sonnet 5 | $0.00005 | $0.00149 |
| Haiku 4.5 | $0.00002 | $0.00075 |
Grade A, and why
microservice-design-patterns 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 8d 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 — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Advanced Microservice Architecture Patterns
Distributed systems demand rigorous data consistency models and scalable communication pipelines. This reference explores advanced patterns for transaction management and state propagation in highly decoupled topologies.
1. The SAGA Pattern: Distributed Transactions
In microservices, traditional ACID transactions (2PC/Two-Phase Commit) are antipatterns due to synchronous blocking and lock contention. SAGA mitigates this by decomposing a distributed transaction into a sequence of local ACID transactions.
If a local transaction fails, the SAGA executes compensating transactions to rollback the preceding steps, achieving eventual consistency.
Choreography vs. Orchestration
- Choreography (Event-Driven): Services publish domain events upon completing their local transactions. Other services subscribe to these events and trigger their respective local transactions. There is no centralized controller.
- Pros: Highly decoupled, no single point of failure.
- Cons: Emergent complexity; difficult to trace the lifecycle of a complex transaction.
- Orchestration (Command-Driven): A centralized orchestrator (e.g., an AWS Step Function or Camunda engine) manages the transaction lifecycle. It issues commands to participant services and handles failure logic.
- Pros: Centralized observability, straightforward compensation logic.
- Cons: The orchestrator can become a god-object and a bottleneck.
2. CQRS: Command Query Responsibility Segregation
CQRS separates the data modification (Command) and data retrieval (Query) pipelines, acknowledging that read and write workloads scale asymmetrically and require different storage paradigms.
- Command Model: Highly normalized, focuses on business rules, validation, and transaction boundaries. Often backed by a relational database or event store.
- Query Model: Highly denormalized, materialized views optimized for specific UI read operations. Often backed by NoSQL, Elasticsearch, or Redis.
- Synchronization: The Command side emits events upon state changes. The Query side projects these events into its read-optimized data stores. This introduces an eventual consistency window.
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.
- 8d ago First seen · 62 lines · 24 tokens per session scan A 9d841a0fd426
microservice-design-patterns is a skill published in the GitHub repository j4flmao/agent-skills (23 stars, last pushed 5d ago), licensed MIT. It adds 24 tokens to every session and 747 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 2026-09-03.
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