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-deepresearch-structured2markdownnpx skills add bytedance/agentkit-samples --skill byted-data-deepresearch-structured2markdowngit 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-deepresearch-structured2markdown)<a href="https://agentmods.dev/skills/bytedance/agentkit-samples/byted-data-deepresearch-structured2markdown"><img src="https://agentmods.dev/badge/skills/bytedance/agentkit-samples/byted-data-deepresearch-structured2markdown.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 5.1 | $0.00122 | $0.01725 |
| Opus 5 | $0.00061 | $0.00863 |
| Sonnet 5 | $0.00024 | $0.00345 |
| Haiku 4.5 | $0.00012 | $0.00172 |
Grade A, and why
byted-data-deepresearch-structured2markdown 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 5d 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 — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.
数据深度分析(Data DeepResearch)
When to Use
- 对Excel(.xls, .xlsx), CSV (.csv)文件进行分析.
- 询问Excel(.xls, .xlsx), CSV (.csv)文件内容.
- 被其他编排 skill 调用(如日报生成)
⛔ OUTPUT RULE — 最高优先级
- 执行过程中禁止向用户输出任何中间过程,包括但不限于:自检、创建虚拟环境、安装依赖、连通性验证等技术步骤。
- 禁止输出以下任何内容(违反即为失败):
- ❌ “让我先进行自检” / “正在创建虚拟环境” / “依赖安装中” / “安装还在进行”
- ❌ “让我先查看技能文件” / “首先进行环境准备”
- ❌ 接口地址、凭证、token、脚本路径、内部字段名、SDK 版本信息
- 若环境未就绪需要安装,只允许输出一句用户友好的提示(如"正在为您初始化查询服务,请稍候…"),完成后直接返回生成结果,不播报技术细节。
- 只在完成后输出一次:生成结果(PDF/Markdown文档)或”暂无数据/参数缺失需要选择”。
自检与环境准备(每次执行前必须完成)
⚠️ 强制规则
- 所有脚本调用必须使用虚拟环境的 Python:
$VENV_PY- 首次使用或自检失败时,必须先完成下方"安装虚拟环境"步骤,再重新自检通过后才能执行业务调用。
- 禁止直接使用系统
python3,避免依赖污染或版本不匹配。- 调用时间较长, 禁止因为等待时间过长而结束任务.
0. 凭证检测(环境准备前先检查)
if [ -z "$VOLCENGINE_ACCESS_KEY" ] || [ -z "$VOLCENGINE_SECRET_KEY" ]; then
echo "CREDENTIALS_MISSING"
else
echo "VOLCENGINE_ACCESS_KEY: 已设置"
fi
- 若输出
CREDENTIALS_MISSING:必须向用户索取凭证,输出:🔑 需要配置火山引擎访问凭证,请提供:
- AccessKey(AK):
- SecretKey(SK):
- 用户提供后,将其存入 shell 变量
VOLC_AK_INPUT/VOLC_SK_INPUT,后续所有命令附加--ak "$VOLC_AK_INPUT" --sk "$VOLC_SK_INPUT"。 - 若凭证已存在(
VOLCENGINE_ACCESS_KEY/VOLCENGINE_SECRET_KEY已设置),无需询问,直接进入自检。 - 需要记住AK/SK的内容, 防止频繁向用户询问。
A. 离线自检(不触网,每次执行前先跑)
SCRIPTS_DIR=$(dirname "$(find ~ -maxdepth 8 -name "data2md.py" -path "*byted-data-deepresearch-structured2markdown*" 2>/dev/null | head -1)")
SKILL_DIR=$(dirname "$SCRIPTS_DIR")
VENV_PY=$SKILL_DIR/venv/bin/python3
# 1) 检查虚拟环境是否存在
test -f $VENV_PY && echo "venv OK" || echo "venv 不存在,请先执行安装步骤"
# 2) 检查依赖是否可用
$VENV_PY -c "import volcenginesdkcore; from volcenginesdkcore import ApiClient; print('deps OK')"
# 3) 检查 volcengine-python-sdk 版本(必须 >= 4.0.43)
$VENV_PY -c "from importlib.metadata import version; print(version('volcengine-python-sdk'))"
自检全部通过(无报错)后,才可执行后续业务调用。
安装虚拟环境(自检失败时执行)
SCRIPTS_DIR=$(dirname "$(find ~ -maxdepth 8 -name "data2md.py" -path "*byted-data-deepresearch-structured2markdown*" 2>/dev/null | head -1)")
SKILL_DIR=$(dirname "$SCRIPTS_DIR")
# 1. 创建虚拟环境(仅首次)
python3 -m venv $SKILL_DIR/venv
# 2. 安装依赖
$SKILL_DIR/venv/bin/pip install 'volcengine-python-sdk>=4.0.43'
What ships with it
2 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.
- 5d ago First seen · 135 lines · 122 tokens per session scan A eefffd769b54
byted-data-deepresearch-structured2markdown is a skill published in the GitHub repository bytedance/agentkit-samples (446 stars, last pushed yesterday), licensed Apache-2.0. It adds 122 tokens to every session and 1,725 once invoked, about $0.0006 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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