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 agentmods add skills/bytedance/agentkit-samples/byted-data-labelnpx skills add bytedance/agentkit-samples --skill byted-data-labelgit 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-data-label)<a href="https://agentmods.dev/skills/bytedance/agentkit-samples/byted-data-label"><img src="https://agentmods.dev/badge/skills/bytedance/agentkit-samples/byted-data-label.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00190 | $0.01879 |
| Opus 5 | $0.00095 | $0.00940 |
| Sonnet 5 | $0.00038 | $0.00376 |
| Haiku 4.5 | $0.00019 | $0.00188 |
Grade A, and why
byted-data-label 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 4d 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 — 158 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Seederive 非结构化打标平台
你是 Seederive 平台的操作助手。所有 Seederive 操作从这里开始。
什么是 Seederive
Seederive 用 LLM 对文本/语音/图片数据做情感分析、标签分类、观点提取等批量处理。
认证配置
使用前需要设置 AK/SK 环境变量：
| 环境变量 | 说明 | 必填 |
|---|---|---|
VOLCENGINE_ACCESS_KEY |
Access Key | 是 |
VOLCENGINE_SECRET_KEY |
Secret Key | 是 |
验证连通性
设置好环境变量后，执行以下命令验证：
python3 ${SKILL_DIR}/scripts/seederive.py task list --page-size 1
如果返回 "code": 0 表示连通成功。如果返回认证错误，请检查 AK/SK 是否正确。
执行命令的方式
python3 ${SKILL_DIR}/scripts/seederive.py <子命令和参数>
第一步：判断用户意图
阅读用户的需求，对照下表确定属于哪个场景：
| 场景 | 用户说了什么（示例） | 下一步 |
|---|---|---|
| A. 快速试效果 | "帮我分析这几条评论" / "试一下情感分析" / "看看这些文本的标签" | → 直接用 quick-preview，见下方「场景 A」 |
| B. 创建批量任务 | "帮我对这个数据表做情感分析" / "建一个打标任务" | → 读取 ${SKILL_DIR}/references/task.md 获取详细指引 |
| C. 需要标签体系 | "按我们的标签分类" / "建一个标签库" / "主体识别" | → 读取 ${SKILL_DIR}/references/tag-base.md 获取详细指引 |
| D. 优化效果 | "效果不好" / "帮我优化" / "上传错题" / "换个模型" | → 读取 ${SKILL_DIR}/references/optimize.md 获取详细指引 |
| E. 不确定 | "我有一批数据想处理" / "能做什么" | → 先问用户数据是什么、想得到什么结果，再回到本表判断 |
重要：场景 B/C/D 的具体操作步骤、参数说明、JSON 格式都在对应的参考文件中。你必须用 Read 工具读取对应文件后再执行，本文件不包含这些细节。
场景 A：快速试效果（唯一可以直接执行的场景）
这是最轻量的路径，无需创建任务，传几条文本就能看结果。
支持的分析类型
| 分析类型 | nodeType 值 | 输出 | 额外参数 |
|---|---|---|---|
| 情感分析 | EMOTION_DETECTION |
正面/负面/中性 + 原因 | 无 |
| 营销水军识别 | SHILL_DETECTION |
是/否 + 原因 | 无 |
| 观点提取 | OPINION_SUMMARY |
核心观点 + 理由 | 无 |
| 内容评分 | CONTENT_SCORING |
质量/原创/有用/合规评分 | 无 |
| 翻译 | TRANSLATION |
翻译结果 | --target-language |
| 标签分类 | TAG_DETECTION |
多级标签 | --tag-base-id（需要先建标签库，见场景 C） |
| 主体识别 | SUBJECT_DETECTION |
多级主体 | --tag-base-id（需要先建标签库，见场景 C） |
| 自定义分析 | CUSTOM_APPLICATION |
自定义 | --prompt + --output-fields |
执行方式
方式一：直接传文本（推荐，最快）
python3 ${SKILL_DIR}/scripts/seederive.py task quick-preview \
--raw-data '["文本1", "文本2", "文本3"]' \
--node-type EMOTION_DETECTION \
--input-column "评论内容"
方式二：上传文件
What ships with it
5 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.
- 4d ago First seen · 158 lines · 190 tokens per session scan A dae993778e28
byted-data-label is a skill published in the GitHub repository bytedance/agentkit-samples (446 stars, last pushed today), licensed Apache-2.0. It adds 190 tokens to every session and 1,879 once invoked, about $0.0010 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
vector-and-embedding-weaknesses
Hunt vector / embedding weaknesses (OWASP LLM08:2025) — adversarial inputs against the RAG / similarity layer that cause cross-tenant leak, embedding-inversion privacy loss, semantic confusion, and retriever-driven prompt injection.
llm-redteam-overview
LLM red team category — full AATMF v3 tactic coverage (T01–T15). Routing skill: read this first to identify which tactic applies, then load the matching sub-skill. Maps to MITRE ATLAS where overlap exists.
glycobiology
Glycosylation site prediction and glycobiology analysis. N-glycosylation motif finding, O-glycosylation hotspot prediction, glycan structure resources. Lightweight, pure Python. For protein function queries use uniprot-database; for structure analysis use alphafold-database.
shap
Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing…
cellxgene-census
Query the CELLxGENE Census (61M+ cells) programmatically. Use when you need expression data across tissues, diseases, or cell types from the largest curated single-cell atlas. Best for population-scale queries, reference atlas comparisons. For analyzing your own data use scanpy or scvi-tools.
aeon
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard…