microservices
Deep microservices workflow—service boundaries, data ownership, synchronous vs async integration, contracts, deployment independence, and operational complexity. Use when splitting a monolith, reviewing service boundaries, or debugging distributed failures.
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/clawkk/microservicesWhat This Skill Does
The microservices skill provides a structured framework for architects and developers to design, decompose, and manage distributed systems. It acts as an expert consultant for microservices adoption, prioritizing data isolation, ownership boundaries, and operational reliability over simple code organization. The skill guides users through six rigorous stages: defining goals and constraints, establishing service boundaries, selecting integration patterns (synchronous vs. asynchronous), enforcing contract versioning, implementing reliability patterns (like circuit breakers), and setting up operational governance. It ensures that the transition from a monolithic architecture to microservices is justified by actual business needs like independent deployment cadences or specific scaling requirements rather than architectural trends.
Installation
You can install this skill directly via the OpenClaw command-line interface using the following command:
clawhub install openclaw/skills/skills/clawkk/microservices
Use Cases
This skill is ideal for:
- Teams evaluating whether to split a growing monolith into smaller, independently deployable services.
- Architects needing to review service boundaries to eliminate hidden couplings like shared databases.
- Debugging distributed systems where latency cascades, partial failures, or contract mismatches are becoming recurring blockers.
- Designing reliable integration patterns using sagas or outbox patterns for multi-step business transactions.
Example Prompts
- "We are struggling with a massive monolith where one team's deployment breaks another's functionality. Walk me through Stage 2 of the microservices workflow to help us identify better service boundaries."
- "I need to design a system for handling order processing across three separate services. Suggest the best integration patterns for handling distributed consistency using the microservices skill."
- "Our system keeps suffering from latency cascades when the payment service slows down. Can you review our architecture against the Reliability Patterns stage of the microservices workflow?"
Tips & Limitations
- Microservices architecture assumes high organizational maturity. If your team lacks automated testing or continuous delivery pipelines, implementing microservices will likely increase your operational burden without providing the expected benefits.
- Always be wary of shared databases between services; this is a 'hidden coupling' anti-pattern that must be refactored into domain-specific data stores.
- The network is inherently unreliable. Every inter-service communication must be designed to handle partial failures gracefully using timeouts, retries, and circuit breakers.
- For small teams, strongly consider a modular monolith approach first to maintain code simplicity while preserving clear boundaries until the operational scale truly demands a distributed approach.
Metadata
Not sure this is the right skill?
Describe what you want to build — we'll match you to the best skill from 16,000+ options.
Find the right skillPaste this into your clawhub.json to enable this plugin.
{
"plugins": {
"official-clawkk-microservices": {
"enabled": true,
"auto_update": true
}
}
}Tags(AI)
Related Skills
data-move
Deep data migration workflow—scope, mapping, validation, batching and ordering, dual-write and cutover, rollback, and reconciliation. Use when moving tenants, bulk backfills, or changing stores without losing trust in data correctness.
data-model
Deep data modeling workflow—grain, facts and dimensions, keys, slowly changing dimensions, normalization trade-offs, and analytics query patterns. Use when designing warehouse/analytics models or reviewing star/snowflake schemas.
guard
Deep AI safety guardrails workflow—policy definition, input/output filtering, monitoring, escalation, and false-positive handling. Use when reducing harmful outputs, misuse, or policy violations in LLM products.
prompts
Deep prompt engineering workflow—task spec, constraints, examples, evaluation sets, iteration protocol, regression testing, and safety alignment. Use when improving LLM outputs, shipping prompt changes, or building reusable prompt templates.
cost-opt
Cloud cost review: rightsizing, reservations, waste. Use when reducing infra spend.