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-recruitment-analyticsgit 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-recruitment-analytics)<a href="https://agentmods.dev/skills/bytedance/agentkit-samples/byted-recruitment-analytics"><img src="https://agentmods.dev/badge/skills/bytedance/agentkit-samples/byted-recruitment-analytics/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-recruitment-analytics"><img src="https://agentmods.dev/badge/skills/bytedance/agentkit-samples/byted-recruitment-analytics.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 | $0.00044 | $0.04022 |
| Opus 5 | $0.00022 | $0.02011 |
| Sonnet 5 | $0.00009 | $0.00804 |
| Haiku 4.5 | $0.00004 | $0.00402 |
Grade A, and why
byted-recruitment-analytics 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 — 503 lines — stays where its author put it; the contents beside it link to each section on GitHub.
招聘数据分析与优化建议技能
功能概述
本技能用于招聘数据的定期分析、进展汇报和优化建议，支持每日、每周、每月定时任务。
定时任务类型
任务一：每日招聘进展日报
触发时间：每个工作日上午 10:00 前
执行内容：
## 📋 招聘进展日报 - [日期]
### 一、各JD进展概览
| JD名称 | 状态 | 已查看候选人 | 高匹配度(4-5星) | 已打招呼 | 面试安排 | 下一步 |
|--------|------|-------------|-----------------|---------|---------|--------|
| [JD1] | 🟡 进行中 | X位 | X位 | X位 | X位 | [说明] |
| [JD2] | 🟢 待开始 | 0位 | 0位 | 0位 | 0位 | 等待确认 |
### 二、昨日数据统计
| 指标 | 数值 | 说明 |
|------|------|------|
| 新增候选人 | X位 | 昨日新增查看 |
| 高匹配度占比 | XX% | 4-5星候选人占比 |
| 打招呼发送 | X位 | 昨日发送数量 |
| 打招呼响应 | X位 | 收到回复数量 |
| 响应率 | XX% | 响应/发送 |
| 面试安排 | X位 | 昨日安排 |
### 三、各JD详细数据
#### [JD1名称]
- **当前状态**：[状态]
- **候选人分布**：
- 5星：X位
- 4星：X位
- 3星：X位
- 2星：X位
- 1星：X位
- **关键发现**：
- [发现1]
- [发现2]
- **下一步计划**：
- [计划1]
- [计划2]
### 四、发现的问题
1. **[问题1标题]**
- 描述：[问题描述]
- 影响：[影响范围]
- 建议：[初步建议]
2. **[问题2标题]**
- 描述：[问题描述]
- 影响：[影响范围]
- 建议：[初步建议]
### 五、优化建议
#### 1. JD优化建议
- **[JD1]**：[具体建议]
- **[JD2]**：[具体建议]
#### 2. 搜索策略优化
- [建议1]
- [建议2]
#### 3. 沟通话术优化
- [建议1]
- [建议2]
### 六、需要您确认
- [ ] 确认事项1：[内容]
- [ ] 确认事项2：[内容]
- [ ] 确认事项3：[内容]
---
**汇报时间**：[时间]
**下次汇报**：[明日/下周一]
任务二：每周招聘总结报告
触发时间：每周一上午 10:00
执行内容：
## 📊 招聘周报 - [开始日期] ~ [结束日期]
### 一、本周整体数据概览
| 指标 | 数值 | 较上周 |
|------|------|--------|
| 活跃JD数 | X个 | +X/-X |
| 查看候选人 | X位 | +X/-X |
| 高匹配度(4-5星) | X位 | +X/-X |
| 发送打招呼 | X位 | +X/-X |
| 收到响应 | X位 | +X/-X |
| 安排面试 | X位 | +X/-X |
| 完成录用 | X位 | +X/-X |
### 二、各JD详情
#### [JD1名称]
- **状态**：[状态]
- **本周数据**：
- 新增查看：X位
- 高匹配度：X位
- 打招呼：X位
- 面试：X位
- **候选人评分分布**：
- 5星：X位 (XX%)
- 4星：X位 (XX%)
- 3星：X位 (XX%)
- 2星：X位 (XX%)
- 1星：X位 (XX%)
- **亮点与问题**：
- 亮点：[描述]
- 问题：[描述]
### 三、渠道效果分析
| 招聘平台 | 查看人数 | 高匹配度 | 响应率 | 面试率 | 录用率 | ROI评分 |
|---------|---------|---------|--------|--------|--------|---------|
| BOSS直聘 | X位 | X位 | XX% | XX% | XX% | ⭐⭐⭐ |
| LinkedIn | X位 | X位 | XX% | XX% | XX% | ⭐⭐⭐⭐ |
| 猎聘 | X位 | X位 | XX% | XX% | XX% | ⭐⭐ |
**渠道优化建议**：
- [建议1]
- [建议2]
### 四、招聘漏斗分析
查看候选人 (X位) ↓ 高匹配度 (X位) - 转化率 XX% ↓ 发送打招呼 (X位) - 转化率 XX% ↓ 收到响应 (X位) - 转化率 XX% ↓ 安排面试 (X位) - 转化率 XX% ↓ 完成录用 (X位) - 转化率 XX%
**漏斗瓶颈分析**：
- 主要瓶颈：[环节]
- 优化建议：[建议]
### 五、市场洞察
#### 1. 薪资分布分析
- **[职位1]**：[薪资范围]
- **[职位2]**：[薪资范围]
- **市场趋势**：[趋势描述]
#### 2. 热门技能分析
- 本周热门技能1：[技能名称] - 出现频率 XX%
- 本周热门技能2：[技能名称] - 出现频率 XX%
- 本周热门技能3：[技能名称] - 出现频率 XX%
#### 3. 人才供需分析
- **供给情况**：[描述]
- **需求情况**：[描述]
- **建议**：[建议]
### 六、优化建议与策略调整
#### 1. JD优化建议
| JD名称 | 当前问题 | 优化建议 | 优先级 |
|--------|---------|---------|--------|
| [JD1] | [问题] | [建议] | 高 |
| [JD2] | [问题] | [建议] | 中 |
#### 2. 评分标准优化
- **当前权重**：经验30%、技能25%、学历15%、行业15%、薪资10%、其他5%
- **优化建议**：[具体调整建议]
- **理由**：[数据支撑理由]
#### 3. 搜索策略调整
- **当前策略**：[描述]
- **问题**：[描述]
- **调整建议**：[具体建议]
#### 4. 沟通话术优化
- **打招呼话术**：[当前话术] → [建议话术]
- **面试邀请话术**：[当前话术] → [建议话术]
### 七、下周工作计划
#### 1. 重点任务
- [ ] 任务1：[描述] - 负责人：[角色] - 截止时间：[时间]
- [ ] 任务2：[描述] - 负责人：[角色] - 截止时间：[时间]
- [ ] 任务3：[描述] - 负责人：[角色] - 截止时间：[时间]
#### 2. 重点JD
- **优先级1**：[JD名称] - [目标]
- **优先级2**：[JD名称] - [目标]
- **优先级3**：[JD名称] - [目标]
### 八、需要您决策
1. **[决策事项1]**
- 选项A：[描述]
- 选项B：[描述]
- 建议：[建议选项]
2. **[决策事项2]**
- 选项A：[描述]
- 选项B：[描述]
- 建议：[建议选项]
---
**报告生成时间**：[时间]
**上周周报**：[链接]
**下周预报**：下周一上午10点
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 · 503 lines · 44 tokens per session scan A 9ea2ab06722f
byted-recruitment-analytics is a skill published in the GitHub repository bytedance/agentkit-samples (450 stars, last pushed 4d ago), licensed Apache-2.0. It adds 44 tokens to every session and 4,022 once invoked, about $0.0002 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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