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-sol-stability-grafana-dashboard-reviewgit 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-sol-stability-grafana-dashboard-review)<a href="https://agentmods.dev/skills/bytedance/agentkit-samples/byted-sol-stability-grafana-dashboard-review"><img src="https://agentmods.dev/badge/skills/bytedance/agentkit-samples/byted-sol-stability-grafana-dashboard-review/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-sol-stability-grafana-dashboard-review"><img src="https://agentmods.dev/badge/skills/bytedance/agentkit-samples/byted-sol-stability-grafana-dashboard-review.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 | $0.00102 | $0.05815 |
| Opus 5 | $0.00051 | $0.02908 |
| Sonnet 5 | $0.00020 | $0.01163 |
| Haiku 4.5 | $0.00010 | $0.00581 |
Grade A, and why
byted-sol-stability-grafana-dashboard-review 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 9d 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 — 241 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Grafana 监控大盘设计评审 Skill
适用场景
当用户提供一个 Grafana 大盘的链接、截图、JSON 配置或设计稿，要求"评审 / 评价 / 打分 / 检查 / 提改进意见"时使用本技能。
输入可以是以下任一形式：
- Grafana 大盘 URL
- 大盘 JSON（导出文件）
- 截图（已上传或可访问）
- 文字描述的大盘结构
评审流程
按以下顺序执行：
- 采集大盘信息：优先调用
grafana-analyzer技能拉取面板列表与截图；若仅有 URL，至少获取 dashboard_uid、panel 列表、变量列表。 - 分类映射：把每个面板归类为
Health Metric或Diagnostic Metric，找不到归类则标记为Unclassified。 - 逐项检查：依据下文 §1 基础检查清单 + §2 进阶检查清单逐项判定 PASS / WARN / FAIL，并给出证据（panel 名 / 截图位置）。
- 模式比对：对照 §5 Good/Bad Case 模式库，识别常见反模式并标注。
- 量化打分：按 §3A 7 维度 24 细项给每项 0% / 50% / 100%，计算配置维度总分（满分 100）。如存在事故案例，再按 §3B 给事故视角分（满分 10）。
- 输出报告：按 §4 报告模板输出 Markdown，必须包含细项打分明细、问题清单、改进路线图。
§1 基础检查清单（必查项，对应文档 1.1）
| # | 检查项 | PASS 标准 | 常见 FAIL 模式 |
|---|---|---|---|
| B1 | Health vs Diagnostic 分区 | 顶部独立 Row 展示 Health，下方 Row 展示 Diagnostic，视觉上可区分（颜色/标题/Stat 面板） | 全部混排；只有 Diagnostic；分区不清 |
| B2 | Health 指标告警配置 | 每个 Health 面板挂载告警规则 + 处理人 + 时效 ≤ 1min（按 SLA） | 无告警；处理人空；告警延迟 > 1min |
| B3 | Health 覆盖 SLI/RTO 场景 | 覆盖 RTO 场景列表 + 控制面（页面 PV/UV、核心 API VALET）+ 数据面（实例/集群/Job VALET） | 仅有控制面或仅有数据面；RTO 场景未覆盖 |
| B4 | 量+率组合告警 | 业务指标告警同时含量与率（如错误数 > N 且错误率 > x%），或采用智能监控 | 仅设单一阈值，导致小流量误报 |
| B5 | 拨测覆盖小流量接口 | 流量不稳定的关键页面/API 引入拨测，且日常监控排除拨测流量 | 小流量接口无拨测；指标含拨测流量导致量级失真 |
| B6 | 分层诊断指标 | Diagnostic 至少包含：自身服务（吞吐/性能/容量）+ 强依赖 + 弱依赖 + 基础资源 + 事件（告警/变更） | 只有自身服务；无依赖；无资源；无事件 |
| B7 | 排查思路体现 | Diagnostic 排列顺序与一般排查思路一致（自顶向下：业务→服务→依赖→资源） | 随意堆叠，无层次 |
| B8 | 客户维度筛选 | 大盘变量支持 account_id / 客户 ID 筛选；SVIP 有专属面板 |
不支持 account_id；无 SVIP 面板 |
| B9 | 基础体验 | 已配置免登（权限可控）；URL 支持 region、account_id 等参数跳转；首屏包含关键 Health |
必须登录才能访问；URL 不支持参数；首屏全是 Diagnostic |
§2 进阶检查清单（加分项，对应文档 1.2）
| # | 检查项 | 说明 |
|---|---|---|
| A1 | 告警/变更叠加 | 关键 Health 趋势图叠加告警事件、变更事件 annotation |
| A2 | 风险预警 | 设置接近事故阈值（非达标）的预警告警 |
| A3 | 阈值贴合水位 | 阈值定期回归到日常水位附近，能感知缓慢恶化 |
| A4 | 无数据告警 | 关键趋势图配置 no data 告警，避免指标跌零误判为监控故障 |
| A5 | 例行变更指标治理 | 变更引起的"例行抖动"已被消除或屏蔽，避免麻木 |
| A6 | 分位数优先于均值 | 延迟类指标使用 P95/P99，而非 avg；只有数据对称无离群时才用 avg |
§3 大盘有效性评分模型
本节包含两套互补评分：§3A 配置维度量化评分（满分 100） 用于离线设计评审；§3B 事故视角评分（满分 10） 用于事后复盘。最终在报告中同时给出。
What ships with it
1 file 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.
- 9d ago First seen · 241 lines · 102 tokens per session scan A 361f6871fb46
byted-sol-stability-grafana-dashboard-review is a skill published in the GitHub repository bytedance/agentkit-samples (453 stars, last pushed 2d ago), licensed Apache-2.0. It adds 102 tokens to every session and 5,815 once invoked, about $0.0005 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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