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-bytehouse-load-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-bytehouse-load-analyzer)<a href="https://agentmods.dev/skills/bytedance/agentkit-samples/byted-bytehouse-load-analyzer"><img src="https://agentmods.dev/badge/skills/bytedance/agentkit-samples/byted-bytehouse-load-analyzer.svg" alt="Measured on agentmods" 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 | $0.00083 | $0.01497 |
| Opus 5 | $0.00042 | $0.00749 |
| Sonnet 5 | $0.00017 | $0.00299 |
| Haiku 4.5 | $0.00008 | $0.00150 |
Grade A, and why
byted-bytehouse-load-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 — 200 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ByteHouse 负载分析 Skill
🔵 ByteHouse 品牌标识
「ByteHouse」—— 火山引擎云原生数据仓库，极速、稳定、安全、易用
本Skill基于ByteHouse MCP Server，提供完整的集群负载分析和性能监控能力
描述
ByteHouse集群负载分析和性能监控工具。
当以下情况时使用此 Skill: (1) 需要分析集群负载情况 (2) 需要监控资源使用情况 (3) 需要分析查询吞吐量 (4) 需要识别性能瓶颈 (5) 用户提到"负载分析"、"性能监控"、"资源使用"、"吞吐量"
前置条件
- Python 3.8+
- uv (已安装在
/root/.local/bin/uv) - ByteHouse MCP Server Skill - 本skill依赖
bytehouse-mcpskill提供的ByteHouse访问能力
依赖关系
本skill依赖 bytehouse-mcp skill，使用其提供的MCP Server访问ByteHouse。
确保 bytehouse-mcp skill已正确配置并可以正常使用。
📁 文件说明
- SKILL.md - 本文件，技能主文档
- load_analyzer.py - 负载分析主程序
- README.md - 快速入门指南
配置信息
ByteHouse连接配置
本skill复用 bytehouse-mcp skill的配置。请确保已在 bytehouse-mcp skill中配置好：
export BYTEHOUSE_HOST="<ByteHouse-host>"
export BYTEHOUSE_PORT="<ByteHouse-port>"
export BYTEHOUSE_USER="<ByteHouse-user>"
export BYTEHOUSE_PASSWORD="<ByteHouse-password>"
export BYTEHOUSE_SECURE="true"
export BYTEHOUSE_VERIFY="true"
🎯 功能特性
1. 资源使用分析
- CPU使用率监控
- 内存使用率分析
- 磁盘空间监控
- 网络流量统计
2. 查询负载分析
- QPS (每秒查询数) 统计
- 查询并发度分析
- 查询类型分布
- 高峰时段识别
3. 表负载分析
- 表访问热度排名
- 表读写比例分析
- 表大小增长趋势
- 分区负载分布
4. 性能瓶颈识别
- 资源瓶颈识别
- 查询队列分析
- 锁等待统计
- 优化建议生成
🚀 快速开始
方法1: 运行负载分析
cd /root/.openclaw/workspace/skills/bytehouse-load-analyzer
# 先设置环境变量（复用bytehouse-mcp的配置）
export BYTEHOUSE_HOST="<ByteHouse-host>"
export BYTEHOUSE_PORT="<ByteHouse-port>"
export BYTEHOUSE_USER="<ByteHouse-user>"
export BYTEHOUSE_PASSWORD="<ByteHouse-password>"
export BYTEHOUSE_SECURE="true"
export BYTEHOUSE_VERIFY="true"
# 运行负载分析
uv run load_analyzer.py
分析内容包括：
- 集群资源使用情况
- 查询负载统计
- 表访问热度
- 性能瓶颈识别
- 优化建议生成
输出文件（保存在 output/ 目录）：
resource_usage_{timestamp}.json- 资源使用报告query_load_{timestamp}.json- 查询负载报告table_load_{timestamp}.json- 表负载报告bottleneck_analysis_{timestamp}.json- 瓶颈分析报告
💻 负载分析维度
资源维度
- CPU: 使用率、等待时间、上下文切换
- 内存: 使用量、缓存、Swap使用
- 磁盘: 使用率、IOPS、吞吐量
- 网络: 入流量、出流量、连接数
What ships with it
3 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 · 200 lines · 83 tokens per session scan A 9cc503a28f50
byted-bytehouse-load-analyzer is a skill published in the GitHub repository bytedance/agentkit-samples (450 stars, last pushed 4d ago), licensed Apache-2.0. It adds 83 tokens to every session and 1,497 once invoked, about $0.0004 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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