bytedance/agentkit-samples is a collection of examples and tutorials for Volcengine AgentKit, an AI-agent development platform for building, deploying, and operating agent applications. Developers use the samples to learn agent creation, multi-agent collaboration, memory, retrieval, MCP integrations, media generation, customer service, and other workflows. The catalogue skills provide agent workflows based on these examples.
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 bytedance/agentkit-samples --skill byted-volcengine-alert-inspection-analyzergit clone --depth 1 https://github.com/bytedance/agentkit-samplesWrote 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/bytedance/agentkit-samples/byted-volcengine-alert-inspection-analyzer)<a href="https://agentmods.dev/skills/bytedance/agentkit-samples/byted-volcengine-alert-inspection-analyzer"><img src="https://agentmods.dev/badge/skills/bytedance/agentkit-samples/byted-volcengine-alert-inspection-analyzer/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/bytedance/agentkit-samples/byted-volcengine-alert-inspection-analyzer"><img src="https://agentmods.dev/badge/skills/bytedance/agentkit-samples/byted-volcengine-alert-inspection-analyzer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00051 | $0.01693 |
| Opus 5 | $0.00026 | $0.00847 |
| Sonnet 5 | $0.00010 | $0.00339 |
| Haiku 4.5 | $0.00005 | $0.00169 |
Grade A, and why
byted-volcengine-alert-inspection-analyzer 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 7d 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 — 192 lines — stays where its author put it; the contents beside it link to each section on GitHub.
byted-volcengine-alert-inspection-analyzer
把“飞书群告警消息拉取 + 过滤 + 去重 + 事件聚合 + 风险分析 + 报告输出”固化为标准技能。
适用场景
当用户提出以下类型需求时使用本技能：
- 查询某个飞书群某天/某时间段内的云告警通知
- 拉取指定机器人、Bot、应用发送的告警消息
- 对告警消息做巡检分析、日报、复盘或稳定性判断
- 需要导出结构化 JSON、Markdown 报告或压缩包
- 需要判断告警是否恢复、是否存在未闭环事件、是否为噪声告警
输入要求
至少需要以下三类信息：
chat_id：飞书群 ID，例如oc_xxxtime：时间范围- 相对时间：
today、yesterday、last_24_hours、this_week - 或绝对时间：
start_time+end_time，ISO 8601 格式，例如2026-06-05T00:00:00+08:00
- 相对时间：
bot：机器人标识- 优先使用
bot_sender_id/ app id，例如cli_xxx - 如果只有机器人名称，先按时间范围拉取消息后从 sender 信息识别；同时说明精确度风险
- 优先使用
可选信息：
output_dir：输出目录，默认output/alert_inspection_<日期>_<chat_id>/report_format：json、markdown，默认同时生成keyword：内容关键词过滤，例如“火山引擎云监控告警通知”
工作流程
1. 校验输入
- 缺少
chat_id、time或bot时，先向用户补齐。 - 时间必须二选一：相对时间，或
start_time + end_time。 - 对中文日期（如“6月5日”）默认使用当前年份和北京时间，除非用户另有说明。
2. 拉取飞书消息
优先使用 feishu_im_user_get_messages 工具：
{
"chat_id": "oc_xxx",
"start_time": "2026-06-05T00:00:00+08:00",
"end_time": "2026-06-05T23:59:59+08:00",
"page_size": 50,
"sort_rule": "create_time_asc"
}
分页规则：
- 每页最多 50 条。
- 如果返回
has_more=true，继续使用page_token拉取下一页。 - 合并所有分页结果后按
message_id去重。
3. 机器人过滤
过滤优先级：
- 如果用户提供
bot_sender_id：按sender.id精确过滤。 - 如果只有机器人名称：
- 先从返回消息中识别
sender.sender_type == app的候选 sender id。 - 结合消息卡片标题、内容关键词和告警语义做保守筛选。
- 一旦确认 sender id，后续分析中记录为
filter_mode=identified_sender_id。
- 先从返回消息中识别
- 如果无法确认 sender id：
- 用内容关键词过滤。
- 在报告中明确标注
filter_mode=keyword_only，说明可能不完整或不精确。
4. 标准化消息
使用 scripts/alert_inspection.py 对原始消息 JSON 做标准化。标准字段：
message_idcreate_timestatus：警告/已恢复/严重/未知policyresourcemetriccurrent_valuecontentsender
从飞书卡片内容中尽量提取：
- 告警策略
- 告警级别
- 项目
- 地域
- 告警时间
- 云产品
- 资源 ID
- 当前值
- 告警详情链接
5. 分析告警
分析维度：
- 总消息数、警告数、恢复数、未知数
- 按策略统计
- 按指标统计
- 按资源统计
- 按小时统计
- 告警事件闭环：连续警告到恢复为一个事件周期
- 未恢复事件识别
- 持续时间、最长事件、重复事件
- 风险等级：低 / 中 / 高
- 可能原因与处置建议
风险判断建议：
- 低风险：少量告警，全部快速恢复，无重复集中模式。
- 中风险：同一资源/指标反复告警，全部恢复但频率较高。
- 高风险：存在未恢复告警、严重级别告警、长时间持续、核心指标异常或高频集中爆发。
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 7d ago First seen · 192 lines · 51 tokens per session scan A d80a283184a6
byted-volcengine-alert-inspection-analyzer is a skill published in the GitHub repository bytedance/agentkit-samples (450 stars, last pushed 6d ago), licensed Apache-2.0. It adds 51 tokens to every session and 1,693 once invoked, about $0.0003 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 2026-09-03.
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