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
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/bytedance/agentkit-samplesnpx agentmods add skills/bytedance/agentkit-samples/byted-volcengine-capacity-auditorWrote 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-volcengine-capacity-auditor)<a href="https://agentmods.dev/skills/bytedance/agentkit-samples/byted-volcengine-capacity-auditor"><img src="https://agentmods.dev/badge/skills/bytedance/agentkit-samples/byted-volcengine-capacity-auditor/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-volcengine-capacity-auditor"><img src="https://agentmods.dev/badge/skills/bytedance/agentkit-samples/byted-volcengine-capacity-auditor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 3 findings, up to high
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 →
- high Privilege Escalation · line 71 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Privilege Escalation · line 83 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Privilege Escalation · line 96 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
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 | $0.00144 | $0.02432 |
| Opus 5 | $0.00072 | $0.01216 |
| Sonnet 5 | $0.00029 | $0.00486 |
| Haiku 4.5 | $0.00014 | $0.00243 |
Grade A, and why
byted-volcengine-capacity-auditor 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 6d 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 — 194 lines — stays where its author put it; the contents beside it link to each section on GitHub.
火山引擎资源巡检与容量判断 Skill
能力定位
这个 Skill 用来把“资源清单 + 近 30/90 天监控 + 规则化判断 + 巡检建议 + 趋势判断”收成一条稳定流程。
当前 V1 已验证的范围：
ECS：资源清单、CPU、内存、磁盘使用率、磁盘写 IOPSCLB：资源清单、监听器级QPS / 并发连接 / 新建连接 / 带宽摘要（默认先尝试拉取，失败时说明原因）ALB：资源清单、监听器级QPS / 并发连接 / 新建连接 / 带宽 / 丢连接 / HTTP 5xx摘要RDS MySQL：资源清单、当前 CPU/内存/磁盘快照、近 30 天QPS / ConnUsage
当前 V1 的已知边界：
CLB当前已默认尝试拉取监听器级带宽、连接数和QPS摘要，但仍缺少规格上限换算与更完整口径，因此先给事实摘要和待补建议，不给结论性扩缩容判断ALB当前已接入监听器级流量与异常摘要，但仍属于保守判断；如需更细粒度结论，还要继续补监听器/规则/服务器组维度口径RDS MySQL的历史CPU / Mem / Disk监控口径尚未完全映射，当前先使用实例快照 +QPS / ConnUsage做第一版评估
如果任务是在补监控口径或排查“为什么拿不到指标”，读取 references/metric_gaps.md。
路由原则
这个 Skill 内部统一处理两类请求，不再拆成两个独立 skill：
- 巡检模式：用户想先看当前资源、水位、低利用率和风险点
- 趋势模式：用户已经拿到巡检结果，或者明确要看未来
7/15/30天风险、预算方向、扩缩容时点
如果是趋势模式，读取 references/forecasting.md。
适用场景
- 想快速盘点当前账号在某个地域下的
ECS / CLB / ALB / RDS MySQL资源 - 想看近 30 天资源水位，找低利用率资源
- 想输出第一版扩缩容建议或预算优化建议
- 想基于近 30/90 天数据做容量趋势判断
- 想基于巡检结果继续判断未来
7/15/30天风险 - 想基于“明年客户数增长
10%/ 业务量增长20%/ 某条链路请求上涨”来判断整条链路应该怎么扩容 - 想做月度巡检、容量回顾、资源健康检查
不适用场景
- 用户要求执行高风险资源变更，例如直接停机、删实例、调规格
- 用户要求精确的
CLB容量预测，但当前还没补齐连接/带宽/QPS 监控口径 - 用户要求严格财务级预算预测，但没有价格口径、计费方式和历史账单
前置条件
- 当前工作空间有
.env .env至少包含：
VOLCENGINE_AK=...
VOLCENGINE_SK=...
VOLCENGINE_REGION=cn-beijing
- 本地 Python 环境已安装火山引擎 SDK
默认入口
优先使用内置脚本：
python3 <byted-volcengine-capacity-auditor-skill>/scripts/run_capacity_audit.py \
--env-path ./.env \
--project-name mysite \
--region cn-beijing \
--format markdown
如果用户没有给 project-name，默认输出整个地域下的结果。
如果是链路级趋势判断，使用：
python3 <byted-volcengine-capacity-auditor-skill>/scripts/run_capacity_audit.py \
--env-path ./.env \
--project-name mysite \
--region cn-beijing \
--mode forecast \
--growth-factor 0.1 \
--link-name payment-core \
--format markdown
如果已经有拓扑产物，希望按拓扑链路范围评估链路内相关资源容量，优先使用：
python3 <byted-volcengine-capacity-auditor-skill>/scripts/run_capacity_audit.py \
--env-path ./.env \
--region cn-beijing \
--mode forecast \
--growth-factor 0.1 \
--topology-file ./business_topologies/payment-core/topology.json \
--link-name payment-core \
--format markdown
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.
- 6d ago First seen · 194 lines · 144 tokens per session scan A 38c553bcca9d
byted-volcengine-capacity-auditor is a skill published in the GitHub repository bytedance/agentkit-samples (450 stars, last pushed 5d ago), licensed Apache-2.0. It adds 144 tokens to every session and 2,432 once invoked, about $0.0007 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 2026-09-03.
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