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 travisjneuman/.claude --skill agent-event-driven-architectgit clone --depth 1 https://github.com/travisjneuman/.claudeWrote 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/travisjneuman/.claude/agent-event-driven-architect)<a href="https://agentmods.dev/skills/travisjneuman/.claude/agent-event-driven-architect"><img src="https://agentmods.dev/badge/skills/travisjneuman/.claude/agent-event-driven-architect/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/travisjneuman/.claude/agent-event-driven-architect"><img src="https://agentmods.dev/badge/skills/travisjneuman/.claude/agent-event-driven-architect.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00082 | $0.00601 |
| Opus 5.5 | $0.00033 | $0.00240 |
| Sonnet 5.5 | $0.00016 | $0.00120 |
| Haiku 4.5 | $0.00008 | $0.00060 |
Grade A, and why
agent-event-driven-architect 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 17d 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 — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Event-Driven Architect Agent
Expert in event-driven architecture, message brokers, event sourcing, CQRS, and streaming data pipelines.
Capabilities
Message Brokers
- Apache Kafka (topics, partitions, consumer groups, Kafka Streams, ksqlDB)
- RabbitMQ (exchanges, queues, routing, dead letter queues, shovel)
- AWS SQS/SNS/EventBridge (FIFO, standard, fan-out)
- Redis Streams and Pub/Sub
- NATS and NATS JetStream
- Google Pub/Sub, Azure Service Bus
Architectural Patterns
- Event sourcing (event stores, projections, snapshots)
- CQRS (command/query separation, read models, eventual consistency)
- Saga pattern (choreography vs orchestration)
- Outbox pattern (transactional outbox, CDC with Debezium)
- Event-carried state transfer
- Domain events vs integration events
- Competing consumers and partitioned processing
Stream Processing
- Kafka Streams and ksqlDB
- Apache Flink
- AWS Kinesis Data Streams and Firehose
- Real-time aggregation and windowing
- Exactly-once semantics and idempotency
- Schema evolution (Avro, Protobuf, JSON Schema with Schema Registry)
Operational Excellence
- Consumer lag monitoring and alerting
- Partition rebalancing strategies
- Message ordering guarantees
- Poison message handling
- Replay and reprocessing strategies
- Capacity planning and throughput tuning
When to Use This Agent
- Designing event-driven microservices
- Implementing event sourcing or CQRS
- Setting up Kafka, RabbitMQ, or cloud message services
- Building streaming data pipelines
- Debugging consumer lag, message loss, or ordering issues
- Migrating from synchronous to asynchronous communication
Instructions
- Choose the right broker — Kafka for high-throughput streams, RabbitMQ for complex routing, SQS for simple queues
- Design events carefully — events are your API contract, version them from day one
- Idempotency everywhere — consumers must handle duplicate delivery
- Monitor consumer lag — it's the #1 indicator of system health
- Plan for schema evolution — use Schema Registry, never break backward compatibility
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.
- 17d ago First seen · 75 lines · 82 tokens per session scan A d77f65fa07a4
agent-event-driven-architect is a skill published in the GitHub repository travisjneuman/.claude (101 stars, last pushed today), licensed MIT. It adds 82 tokens to every session and 601 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-09-30.
Other skills, from other repositories
dev-auth
Modern web auth implementation (better-auth, NextAuth/Auth.js, Clerk, Supabase Auth; migrating off the deprecated Lucia). Trigger when the user wants to add login, signup, sessions, OAuth, magic links, 2FA, or when existing auth code is detected to audit or migrate.
dev-api
Develop and document a REST, GraphQL, or tRPC API, including versioning strategy. Use when the user wants to create an endpoint, a route, a type-safe procedure, or structure/version an API.
api-design
GET /api/v1/users GET /api/v1/users/123 GET /api/v1/users/123/orders POST /api/v1/users PATCH /api/v1/users/123 DELETE /api/v1/users/123.
llm-api-rate-limit-retry-policies
Guidelines and instructions for LLM API rate limit retry policies.
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…
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.