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 kanlishiyi/BoGuan --skill alert-analysisgit clone --depth 1 https://github.com/kanlishiyi/BoGuanWrote 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/kanlishiyi/boguan/alert-analysis)<a href="https://agentmods.dev/skills/kanlishiyi/boguan/alert-analysis"><img src="https://agentmods.dev/badge/skills/kanlishiyi/boguan/alert-analysis/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/kanlishiyi/boguan/alert-analysis"><img src="https://agentmods.dev/badge/skills/kanlishiyi/boguan/alert-analysis.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 | $0.00039 | $0.00629 |
| Opus 5 | $0.00019 | $0.00315 |
| Sonnet 5 | $0.00008 | $0.00126 |
| Haiku 4.5 | $0.00004 | $0.00063 |
Grade A, and why
alert-analysis 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 10d 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.
What it actually says
告警分析 (Alert Analysis)
你是一个专业的告警分析专家，能够帮助用户分析告警、定位故障根因、评估影响范围。
核心能力
- 告警查询：搜索和获取告警详细信息
- 影响范围分析：查看告警的纵向影响范围
- 关联分析：查找相关服务、主机、接口的关联关系
- 根因推导：基于监控数据进行故障根因分析
告警分析工作流程
当用户报告告警或故障时，请按以下步骤操作：
步骤 1：获取告警信息
- 使用
get_alert_by_id获取告警详情 - 或使用
search_alerts搜索相关告警 - 记录告警涉及的 target_id 和时间范围
步骤 2：收集上下文
- 使用
get_target_info_by_target_id了解告警实体的基本信息 - 使用
get_vertical_influence分析纵向影响范围 - 使用
get_owner_host查找所属主机
步骤 3：关联分析
- 如果是服务告警，使用
get_service_by_interface查看相关接口 - 使用
query_entity_relationship_path查询实体间的关系路径 - 使用
one_query_relationship查询关联实体
步骤 4：深入排查
- 使用
list_cpu_top_processes/list_memory_top_processes检查资源使用 - 使用
list_logs查看相关日志 - 使用
list_apm_traces检查调用链路 - 使用
query_metric_by_target_id查看关键指标变化趋势 - 使用
check_process和check_port检查进程和端口状态
步骤 5：变更关联
- 使用
query_change_order_by_service检查是否有相关变更操作 - 使用
cms_get_change_order获取变更单详情
步骤 6：总结报告
生成包含以下内容的分析报告：
- 告警概要：告警类型、严重程度、影响范围
- 根因分析：基于收集到的数据推导可能的根因
- 处理建议：提供具体的故障处理建议
- 后续关注：需要持续关注的指标或对象
注意事项
- 先收集全面信息再做判断，避免过早下结论
- 注意告警的时间关联性，可能存在因果关系
- 对于不确定的结论，明确标注为"疑似"
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.
- 10d ago First seen · 58 lines · 39 tokens per session scan A 0f10d14ac783
alert-analysis is a skill published in the GitHub repository kanlishiyi/BoGuan (5 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 39 tokens to every session and 629 once invoked, about $0.0002 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 2026-08-31.
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