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-cluster-diagnosticsgit 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-cluster-diagnostics)<a href="https://agentmods.dev/skills/bytedance/agentkit-samples/byted-bytehouse-cluster-diagnostics"><img src="https://agentmods.dev/badge/skills/bytedance/agentkit-samples/byted-bytehouse-cluster-diagnostics/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-bytehouse-cluster-diagnostics"><img src="https://agentmods.dev/badge/skills/bytedance/agentkit-samples/byted-bytehouse-cluster-diagnostics.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Privilege Escalation · line 32 Commands invoke sudo or root privileges. Verify this elevated access is necessary and justified.Fix: Avoid sudo/root unless strictly required. Prefer least-privilege patterns. If elevation is needed, document the justification and scope.
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 | $0.00116 | $0.02030 |
| Opus 5 | $0.00058 | $0.01015 |
| Sonnet 5 | $0.00023 | $0.00406 |
| Haiku 4.5 | $0.00012 | $0.00203 |
Grade B, and why
byted-bytehouse-cluster-diagnostics scanned grade B 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 8d 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.
Asks for rootmediumPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
- `jq`（`export_config.sh` 依赖，用于解析 JSON 配置。未安装时可使用 `brew install jq`、`sudo apt install jq` 或 `sudo yum install jq`） How it starts
The opening of the file, as written. The whole thing — 127 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ByteHouse 诊断集群 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 Matrix Agent，提供完整的集群诊断和健康检查能力
描述
ByteHouse 集群诊断、健康检查、慢查询分析和负载分析的一站式工具。所有诊断能力都通过 scripts/diagnostics.py 转发到 ByteHouse Matrix Agent（一个由 ByteHouse 官方维护的诊断专用 Agent），Skill 本身不直接连接集群、不执行 SQL、不做本地分析。
当以下情况时使用此 Skill：
- 需要检查 ByteHouse 集群健康状态，诊断集群问题与异常。
- 需要识别、分析慢查询并给出性能优化建议。
- 需要分析负载情况、计算组资源使用情况（CPU、内存、并发等）。
- 需要分析查询吞吐量、识别性能瓶颈、定位热点。
- 用户提到 "集群诊断"、"健康检查"、"慢查询"、"查询优化"、"性能分析"、"负载分析"、"资源使用"、"吞吐量" 等关键词。
前置条件
- Python 3.8+
- Python 依赖：
requests（未安装时使用pip install requests或python3 -m pip install requests） jq（export_config.sh依赖，用于解析 JSON 配置。未安装时可使用brew install jq、sudo apt install jq或sudo yum install jq）- 有效的 ByteHouse 集群连接信息（保存于
~/.bytehouse_config.json）
📁 文件说明
- SKILL.md — 本文件，技能主文档。
- scripts/diagnostics.py — 诊断主程序，负责把用户的诊断问题转发到 ByteHouse Matrix Agent。
- scripts/export_config.sh — 从
~/.bytehouse_config.json加载配置到环境变量（依赖jq）。
🧭 智能体使用指引（重要）
Agent 在接手集群诊断类任务时，按以下流程调度本 Skill：
- 确认配置就绪：
- 检查
~/.bytehouse_config.json是否存在且包含BYTEHOUSE_HOST和BYTEHOUSE_PASSWORD。 - 如果不存在或字段缺失，主动向用户索要连接信息，并按下方 JSON 结构补齐后再执行。可以把 ByteHouse 连接信息获取指引 发给用户。
- 加载配置：
source scripts/export_config.sh。
- 检查
- 精化诊断问题：结合用户原始问题、告警内容、时间范围（例如 "最近 1 小时"、"过去 24 小时"），拼装一个自然语言诊断问题（中文即可）。参考 常见诊断问题范例。
- 执行诊断：
python3 scripts/diagnostics.py "你的诊断问题"，透传 Matrix Agent 的流式响应。 - 失败兜底：若诊断脚本返回连接错误、超时、401/403 等，参考 失败兜底建议 处理，切勿绕过脚本自行连接集群或执行 SQL。
- 禁止越权操作：本 Skill 仅允许通过
scripts/diagnostics.py发送诊断问题，严禁自行连接 ByteHouse 集群执行 SQL 或做本地分析。所有分析、优化建议都必须来自 Matrix Agent 的返回结果。
配置说明
配置保存在 ~/.bytehouse_config.json。如果该文件存在且非空，直接使用文件中的配置；如果不存在，则向用户索要连接信息（可以把这个文档发给用户以便查阅：https://www.volcengine.com/docs/6517/1121919?lang=zh）。拿到用户提供的信息后，写入 JSON 文件，避免重复询问。当用户切换 ByteHouse 集群时，一并更新该文件。
{
"BYTEHOUSE_HOST": "<ByteHouse-host>",
"BYTEHOUSE_PASSWORD": "<ByteHouse-password>"
}
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.
- 8d ago First seen · 127 lines · 116 tokens per session scan B dfa1b73bae56
byted-bytehouse-cluster-diagnostics is a skill published in the GitHub repository bytedance/agentkit-samples (450 stars, last pushed 5d ago), licensed Apache-2.0. It adds 116 tokens to every session and 2,030 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it B with 1 finding (asks for root). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
fix-failing-tests
Diagnose a failing test in the googleapis/mcp-toolbox repo and land a fix by reasoning from the actual error: read the failure, reproduce it, shrink it until the cause is forced into the open, then fix the cause. Use this whenever a test or CI job is red, a build breaks after a change, many packages fail at once, or a…
triage-issues
Triage GitHub issues in the googleapis/mcp-toolbox repo: propose the correct labels (type / priority / product / status), check for duplicates, verify a bug has enough info to act on, and draft a triage comment. Use whenever a maintainer asks you to triage, label, categorize, prioritize, or "look at" an issue (or a…
doris-debug-cloud
Use for Doris storage-compute separation (cloud mode) issues: meta-service latency, cache miss storms, object store throughput, and shared-nothing config conflicts in compute groups.
doris-debug-data-lake
Use for Doris external catalog issues: Hive/Iceberg/Paimon/Hudi query failures, metadata refresh, filesystem S3/HDFS connectivity, external MV rewrite misses.
doris-debug-deployment
Use for Doris FE/BE startup failures, port conflicts, prioritynetworks misrouting, metadir corruption, and ADD/DROP BACKEND issues.
doris-debug-materialized-view
Use for Doris sync MV (rollup) miss or async MTMV refresh/rewrite issues. Commands: SHOW ALTER TABLE MATERIALIZED VIEW, mvinfos(), EXPLAIN, REFRESH MATERIALIZED VIEW.