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-hybrid-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-hybrid-search)<a href="https://agentmods.dev/skills/bytedance/agentkit-samples/byted-bytehouse-hybrid-search"><img src="https://agentmods.dev/badge/skills/bytedance/agentkit-samples/byted-bytehouse-hybrid-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-hybrid-search"><img src="https://agentmods.dev/badge/skills/bytedance/agentkit-samples/byted-bytehouse-hybrid-search.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 | $0.00079 | $0.01510 |
| Opus 5 | $0.00039 | $0.00755 |
| Sonnet 5 | $0.00016 | $0.00302 |
| Haiku 4.5 | $0.00008 | $0.00151 |
Grade A, and why
byted-bytehouse-hybrid-search 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 11d 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 — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ByteHouse 混合检索 Skill
🚀 快速开始
环境准备
pip install clickhouse-connect volcengine-python-sdk[ark] numpy scipy
配置说明
配置保存在 ~/.bytehouse_config.json ，如果该文件存在且非空，则直接使用文件中的配置。如果不存在，则让用户提供ByteHouse连接信息（ 把这个文档也发给客户，文档里面介绍了如何获取主机地址和密码：https://www.volcengine.com/docs/6517/1121919?lang=zh ）。用户提供信息后，保存到json文件，避免重复向用户请求连接信息。当用户切换ByteHouse集群时，一并修改该文件。
{
"BYTEHOUSE_HOST": "<ByteHouse-host>",
"BYTEHOUSE_PORT": "8123",
"BYTEHOUSE_USER": "bytehouse",
"BYTEHOUSE_PASSWORD": "<ByteHouse-password>",
"BYTEHOUSE_SECURE": true,
"BYTEHOUSE_VERIFY": true,
"BH_ARK_API_KEY": "<火山引擎方舟API密钥>",
"BH_ARK_BASE_URL": "https://ark.cn-beijing.volces.com/api/v3",
"BH_EMBEDDING_MODEL": "doubao-embedding-vision-251215"
}
其中BYTEHOUSE_HOST（主机地址）和BYTEHOUSE_PASSWORD（密码）必须由用户提供。BH_ARK_API_KEY为可选配置，仅在embedding时使用，用户初次使用时可忽略。其余配置固定。
执行 scripts/export_config.sh 把配置信息导入环境变量中
source scripts/export_config.sh
📚 核心能力
1. 文本向量化
基于豆包文本向量化模型生成文本向量，支持任意长度中文文本。
2. 双索引构建
| 索引类型 | 说明 | 适用场景 |
|---|---|---|
| 全文倒排索引 | 基于BM25算法的全文检索，支持关键词匹配 | 精准关键词召回 |
| 向量索引 | 基于HNSW的向量相似度检索，支持语义匹配 | 语义相似召回 |
3. 核心功能
| 功能 | 方法 | 说明 |
|---|---|---|
| 全文检索 | fulltext_search() |
基于BM25的全文检索，返回BM25分数 |
| 向量检索 | vector_search() |
基于余弦相似度的向量检索，返回相似度分数 |
| 混合检索+RRF重排 | hybrid_search() |
双路召回后使用RRF算法重排，返回最终结果 |
| 自动生成向量 | insert_document()/batch_insert_documents() |
插入文档时自动生成向量并存储，无需手动处理 |
| 单个文档向量更新 | update_document_embedding() |
为单个文档重新生成并更新向量 |
| 批量补全缺失向量 | batch_update_missing_embeddings() |
自动扫描表中所有缺少向量的文档，批量生成并补全向量 |
4. RRF重排算法
Reciprocal Rank Fusion 算法，综合全文检索和向量检索的排名结果，公式：
score = Σ 1 / (k + rank)
默认k=60，可自定义调整。
📖 代码实现
完整示例代码实现位于 scripts/ 目录：
scripts/embedding.py- 文本向量化模块scripts/hybrid_search_client.py- ByteHouse 混合检索客户端scripts/examples.py- 使用示例scripts/export_config.sh- 把配置文件中的信息导入环境变量
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.
- 11d ago First seen · 134 lines · 79 tokens per session scan A 569b3420708d
byted-bytehouse-hybrid-search is a skill published in the GitHub repository bytedance/agentkit-samples (450 stars, last pushed yesterday), licensed Apache-2.0. It adds 79 tokens to every session and 1,510 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.
Other skills, from other repositories
local-embedding
Run embedding on-device with ONNX Runtime. Build from source, model selection, offline mode. Use when setting up local embedding without an API key.
azure-horizondb
Expert knowledge for Azure Horizondb development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using azureai SQL/embeddings, pgvector tuning, Apache AGE graphs, hybrid…
azure-documentdb
Expert knowledge for Azure DocumentDB development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using DocumentDB search (BM25/vector), Data API, MongoDB compatibility, change…
chroma
Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source…
vector-db-ops
Use when vector database operations — Pinecone, Weaviate, Qdrant, ChromaDB. Indexing, querying, filtering, and managing vector embeddings for RAG and similarity search. Use when working with vector db ops.
qdrant-vector-search
High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.