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-multimodal-searchgit 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-multimodal-search)<a href="https://agentmods.dev/skills/bytedance/agentkit-samples/byted-bytehouse-multimodal-search"><img src="https://agentmods.dev/badge/skills/bytedance/agentkit-samples/byted-bytehouse-multimodal-search/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-multimodal-search"><img src="https://agentmods.dev/badge/skills/bytedance/agentkit-samples/byted-bytehouse-multimodal-search.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 31 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 | $0.00141 | $0.02222 |
| Opus 5 | $0.00071 | $0.01111 |
| Sonnet 5 | $0.00028 | $0.00444 |
| Haiku 4.5 | $0.00014 | $0.00222 |
Grade B, and why
byted-bytehouse-multimodal-search 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 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.
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，解析 JSON 配置。安装方式：`brew install jq` 或 `sudo apt install jq`） How it starts
The opening of the file, as written. The whole thing — 180 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ByteHouse 多模态检索 Skill
描述
本 Skill 基于 ByteHouse 的向量检索能力和火山引擎方舟多模态向量化模型（默认 doubao-embedding-vision），提供从向量化 → 建表 → 写入 → 检索的一整套多模态检索参考实现。核心能力：
- 多模态向量化：文本 / 图片 / 视频 → 统一维度向量。
- 向量表创建：支持 HNSW、IVF_FLAT、IVF_PQ 等索引，可选启用全文倒排索引。
- 文档写入：单条 / 批量插入，附带
content_type、title、metadata。 - 检索能力：向量检索、混合检索（向量 + 关键词）、以文搜图、以图搜图 / 视频。
当以下情况时使用此 Skill：
- 用户希望在 ByteHouse 中搭建多模态向量检索服务。
- 用户需要对已有多模态数据（文本、图片、视频）进行向量化后写入 ByteHouse。
- 用户提到 "向量检索"、"多模态检索"、"混合检索"、"以文搜图"、"以图搜视频" 等关键词。
前置条件
- Python 3.8+
- Python 依赖：
clickhouse-connect、clickhouse-driver（TCP 场景）、volcengine-python-sdk[ark]、numpypip install clickhouse-connect clickhouse-driver "volcengine-python-sdk[ark]" numpy jq（export_config.sh依赖，解析 JSON 配置。安装方式：brew install jq或sudo apt install jq）- 有效的 ByteHouse 连接信息与方舟 API Key（保存于
~/.bytehouse_config.json）
📁 文件说明
- SKILL.md — 本文件，技能主文档。
- scripts/embedding.py — 多模态向量化模块（
MultimodalEmbedding）。 - scripts/search_client.py — ByteHouse 多模态检索客户端（
ByteHouseMultimodalSearch）。 - scripts/examples.py — 常见调用示例（初始化、建表、写入、检索）。
- scripts/init.py — Python 包入口，暴露
MultimodalEmbedding与ByteHouseMultimodalSearch。 - 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
- 确认配置：
source scripts/export_config.sh，让BYTEHOUSE_*、BH_ARK_*等环境变量就绪。 - 确认场景：向用户澄清是要 "建表 + 写入" 还是 "只做检索"，以及数据模态（纯文本 / 文本 + 图片 / 视频等）。
- 选择索引策略：
- 数据量 < 100 万：
HNSW+COSINE（默认）。 - 数据量较大或希望更省内存：
HNSW_SQ/IVF_FLAT/IVF_PQ。
- 数据量 < 100 万：
- 建表 + 写入：调用
ByteHouseMultimodalSearch.create_multimodal_table(...)、insert_document(...)或batch_insert(...)。 - 检索：优先使用
vector_search(...)或hybrid_search(...)，返回结果会自动过滤掉零向量、异常数据。 - 安全提示：
BH_ARK_API_KEY、BYTEHOUSE_PASSWORD等凭据只写入~/.bytehouse_config.json，不要打印到日志或消息里。
配置说明
配置保存在 ~/.bytehouse_config.json。如果文件存在且非空，直接使用；否则，让用户提供 ByteHouse 连接信息与方舟 API Key（可以把 ByteHouse 连接信息获取指引 一并发给用户），拿到后写入 JSON 文件，避免重复询问。当用户切换 ByteHouse 集群或方舟账号时，一并更新该文件。
What ships with it
6 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.
- 10d ago First seen · 180 lines · 141 tokens per session scan B 1223ab8a6c2e
byted-bytehouse-multimodal-search is a skill published in the GitHub repository bytedance/agentkit-samples (450 stars, last pushed 6d ago), licensed Apache-2.0. It adds 141 tokens to every session and 2,222 once invoked, about $0.0007 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.
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