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/ai-888/06-aether/rocketmqnpx skills add AI-888/06-Aether --skill rocketmqgit clone --depth 1 https://github.com/AI-888/06-AetherWrote 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/ai-888/06-aether/rocketmq)<a href="https://agentmods.dev/skills/ai-888/06-aether/rocketmq"><img src="https://agentmods.dev/badge/skills/ai-888/06-aether/rocketmq.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 | $0.00015 | $0.05947 |
| Opus 5 | $0.00008 | $0.02974 |
| Sonnet 5 | $0.00003 | $0.01189 |
| Haiku 4.5 | $0.00002 | $0.00595 |
Grade A, and why
rocketmq scanned grade A with 1 finding 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 6d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
metadata: {"nanobot":{"emoji":"🌤️","requires":{"bins":["curl"]}}} How it starts
The opening of the file, as written. The whole thing — 376 lines — stays where its author put it; the contents beside it link to each section on GitHub.
一、技能概述
本技能适配 Apache RocketMQ ，为AI Agent提供多类细分异常排查能力，覆盖消息发送、消费、堆积、连接等全场景，可自动识别异常问题、定位核心原因，并输出标准化处理建议，助力快速解决 RocketMQ 生产/消费异常。
核心适配场景：RocketMQ 消息发送失败、消费失败、消息堆积、客户端连接异常、权限认证异常等各类常见问题，基于 RocketMQ 原生自助排查流程，实现标准化、可执行的排查逻辑，每个技能聚焦一类异常，提升排查精准度。
二、核心检查器汇总表（统一整理）
以下为所有检查器的统一汇总，各细分技能中仅引用检查器名称，详细信息可参考本表，确保检查器定义统一、引用规范：
| 检查器名称 | 功能描述 | 所属分组 | 关联技能 |
|---|---|---|---|
| TOPIC_VALIDITY | Topic 有效性检查 | TOPIC_CHECK, INPUT_PARAM | 消息发送异常诊断、服务端异常诊断 |
| CONSUMER_GROUP_VALIDITY | 消费者组有效性检查 | CONSUMER_GROUP_CHECK, INPUT_PARAM | 消息消费异常诊断 |
| CONSUME_GROUP_SUBSCRIPTION_CONSISTENCY | 消费者组订阅一致性检查 | CLIENT_CONFIG | 消息消费异常诊断 |
| MESSAGE_CONSUMER_GROUP_VISIBILITY | 消息对消费者组的可见性检查 | CLIENT_UP_TIME | 消息消费异常诊断、网络与连接异常诊断 |
| MESSAGE_TRACE | 消息链路追踪检查 | MESSAGE_TRACE | 消息链路与有效性诊断 |
| MESSAGE_VALIDITY | 消息有效性检查 | INPUT_PARAM | 消息发送异常诊断、消息链路与有效性诊断 |
| RETRY_MESSAGE_LAG | 重试消息积压检查 | RETRY_MESSAGE_CHECK | 消息堆积异常诊断 |
| MESSAGE_LAG | 消息积压检查 | MESSAGE_LAG_CHECK | 消息堆积异常诊断 |
| MESSAGE_RETRY_AWAIT | 消息重试等待检查 | RETRY_MESSAGE_CHECK | 消息堆积异常诊断 |
| CONSUMER_PARTIAL_LAG | 消费者部分积压检查 | CONSUMER_REASON_ANALYSIS | 消息堆积异常诊断 |
| CONSUMER_ALL_LAG_RESULT | 消费者全部积压结果 | - | 消息堆积异常诊断 |
| CONSUMER_MESSAGE_QUEUE_BALANCE | 消费者队列负载均衡检查 | CONSUMER_GROUP_CHECK, QUEUE_CONSUMER_MATCH | 消息堆积异常诊断 |
| TOPIC_ROUTE_CONSISTENCY | Topic 路由一致性检查 | TOPIC_CHECK, INPUT_PARAM | 消息发送异常诊断、网络与连接异常诊断、服务端异常诊断 |
| TOPIC_ACROSS_MULTIPLE_CLUSTER | Topic 跨集群检查 | TOPIC_CHECK | 消息发送异常诊断、服务端异常诊断 |
| TOPIC_MESSAGE_BALANCE | Topic 消息分布均衡检查 | TOPIC_CHECK, QUEUE_MESSAGE_DISTRIBUTION_ANALYSIS | 消息发送异常诊断、消息堆积异常诊断 |
| MESSAGE_SUBSCRIPTION_CONSISTENCY | 消息订阅一致性检查 | CLIENT_CONFIG | 消息消费异常诊断 |
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.
- 6d ago First seen · 376 lines · 15 tokens per session scan A 0c79ed812754
rocketmq is a skill published in the GitHub repository AI-888/06-Aether (11 stars, last pushed 4mo ago), licensed MIT. It adds 15 tokens to every session and 5,947 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
debug-optimize-lcp
Guides debugging and optimizing Largest Contentful Paint (LCP) using Chrome DevTools MCP tools. Use this skill whenever the user asks about LCP performance, slow page loads, Core Web Vitals optimization, or wants to understand why their page's main content takes too long to appear. Also use when the user mentions…
systematic-debugging
Use when debugging a failing test, build error, or runtime issue that isn't immediately obvious. Guides a 4-phase root cause analysis instead of random fix attempts.
diagnose
Trace from a reproduced symptom to the source code that causes it. Pin the specific file and approximate line, rate confidence in the cause and clarity of the fix independently, and always propose a concrete fix.
azsdk-common-pipeline-analysis
Analyze Azure SDK CI/CD pipeline failures into a structured diagnosis, and define the required output format. Load this skill before calling azsdkanalyzepipeline, which returns raw failure data that this skill interprets and formats. USE FOR: "pipeline failed", "build failure", "CI check failing", "tests failing in…
repro-admin
Reproduce an EmDash admin UI bug. Attach a container, start the demo dev server, drive the admin with agent-browser using the dev-bypass session, and capture the reproduction as screenshots plus a replayable transcript.
log-error-digest
Analyze log files to troubleshoot errors, identify peak error periods, and produce error clustering, frequency statistics, and time distribution reports. Supports JSON, syslog, and Nginx formats with automatic detection. Use when a user uploads a .log file and asks to analyze errors, find patterns, debug issues, or…