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Grok Bot+18 agentsSpring Boot Production Readiness
Production readiness audit for Spring Boot apps with P0-P2 severity scoring and prioritized remediation plans.
$14
Spring Boot Production Readiness
Example session with this skill installed
Run a production readiness review on this Spring Boot 3.2 project. Focus on security and the data layer.
- Read your context and instructions
- Compiled the spring boot production
I have completed the production readiness review for the project. The analysis covered your build configuration, application properties, and JPA entities.
Production Readiness — Payment-Gateway (Spring Boot 3.2.4) @ 2024-05-20
Verdict: NOT READY — Score: 52/100
Score breakdown — P0: 2 | P1: 3 | P2: 1
Findings (P0 first)
[P0] SB-01 — Actuator /env publicly exposed
- Evidence:
src/main/resources/application.yml:12-management.endpoints.web.exposure.include: "*"
Incident Pattern: Sensitive environment variables, including database credentials and API keys, are leaked via the /actuator/env endpoint.
- How to verify: Attempt to access
/actuator/envfrom an unauthenticated browser session.
Suggested fix: Explicitly list required endpoints and ensure management.endpoints.web.base-path is protected by Spring Security. (effort: S)
[P0] SB-07 — Default HikariCP pool sizing in high-concurrency env
- Evidence:
src/main/resources/application.yml- Missingspring.datasource.hikari.maximum-pool-size
Incident Pattern: Connection pool exhaustion during traffic spikes leading to SQLTransientConnectionException and service downtime.
- How to verify: Run a load test with 50+ concurrent users; check for connection acquisition timeouts.
- Suggested fix: Set
maximum-pool-sizebased on(2 * core_count) + effective_spindle_count. (effort: S)
[P1] SB-10 — Missing Structured Logging
- Evidence:
pom.xml- No JSON logging provider found.
Incident Pattern: Log aggregation tools like ELK/Splunk fail to parse multiline stack traces, making production debugging difficult.
- How to verify: Check logs for plain-text vs JSON format.
- Suggested fix: Add
logstash-logback-encoderand configure a JSON appender. (effort: M)
Remediation plan by sprint
- Current Sprint (P0): Fix Actuator exposure and configure HikariCP pool limits.
- Next Sprint (P1): Implement structured logging and add graceful shutdown properties.
- Backlog (P2): Optimize Docker image layers in Maven build.
Out of scope
- Runtime secret verification in HashiCorp Vault.
- Deep JPA N+1 analysis (Run JPA N+1 & Query Smell Detector for full scan).
Reviewed with Spring Boot Production Readiness Review v1.0.0
Connects securely to your tools. The creator never sees your data.
About this skill
Launching a Spring Boot application without a rigorous pre-flight check often leads to preventable production outages, security breaches, or performance bottlenecks. This skill performs a systematic production readiness review for Spring Boot 3.x and 4.x applications, moving beyond generic linting to identify specific architectural and configuration risks. It evaluates your security baseline, data layer efficiency, and observability stack against industry standards.
What it does
- Security Audit reviews Actuator exposure, JWT rotation, login lockout mechanisms, and credential management.
- Data Layer Analysis checks HikariCP pool sizing, JPA
@Transactionalboundaries, and basic N+1 query patterns. - Resilience Verification inspects graceful shutdown settings, timeout configurations, and retry logic.
- Observability Sweep validates structured logging, health check endpoints, and micrometer metrics integration.
- Infrastructure Review examines Docker/Maven hygiene and multi-device session handling.
How it works
- Reconnaissance scans build files, YAML/properties configurations, and infrastructure code like Dockerfiles.
- Systematic Checklist runs a severity-ordered sweep from P0 to P2, mapping every failure to a specific production incident pattern.
- Scoring Engine calculates a numerical readiness score and issues a final verdict of Ready, Ready with Conditions, or Not Ready.
- Remediation Roadmap generates a prioritized plan categorized by effort (S/M/L) to help plan your next sprints.
Frameworks & tools
Specifically designed for Spring Boot 3.x and 4.x, Spring Security, Spring Data JPA, HikariCP, Actuator, Micrometer, Maven, Gradle, and Docker.
Why this beats prompting it yourself
Generic prompts often miss deep configuration traps like incorrect management endpoint exposure or silent N+1 JPA issues. This skill enforces a strict scoring logic where any P0 failure triggers a "Not Ready" status, ensuring critical risks are never overlooked during a manual chat.
Use cases
- Pre-launch Check for a final audit before moving a new microservice to production.
- Incident Post-mortem to identify configuration gaps after a service failure.
- Technical Debt Assessment for legacy Spring applications undergoing modernization.
Known limitations
Static analysis only. It cannot detect runtime environment variables or secrets injected by CI/CD pipelines at deploy time. Requires access to the source repository.
How to use
Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.
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