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/report-generator-skillnpx skills add bytedance/agentkit-samples --skill report-generator-skillgit 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/report-generator-skill)<a href="https://agentmods.dev/skills/bytedance/agentkit-samples/report-generator-skill"><img src="https://agentmods.dev/badge/skills/bytedance/agentkit-samples/report-generator-skill.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 | $0.00089 | $0.01097 |
| Opus 5 | $0.00044 | $0.00549 |
| Sonnet 5 | $0.00018 | $0.00219 |
| Haiku 4.5 | $0.00009 | $0.00110 |
Grade A, and why
report-generator 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- report-generator — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.
视频分析报告生成 (Report Generator)
概述
视频分析报告生成技能将分镜拆解数据和(可选的)钩子分析结果整合为一份专业的 Markdown 格式分析报告。报告包含视频基本信息、前三秒钩子评分、分镜概览表格、BGM 分析、场景分析和平台推荐等章节。
适用场景
- 视频分析交付:为客户或团队生成完整的视频分析文档
- 创意复盘:生成结构化的视频内容复盘报告
- 竞品报告:批量生成竞品视频分析报告
使用步骤
完整报告(分镜 + 钩子分析)
# 1. 准备分镜拆解数据和钩子分析数据(JSON 文件)
# 2. 生成报告
python scripts/generate_report.py breakdown.json hook_analysis.json
# 3. 保存到文件
python scripts/generate_report.py breakdown.json hook_analysis.json > report.md
仅分镜报告(无钩子分析)
python scripts/generate_report.py breakdown.json
报告结构
生成的报告包含以下章节:
# 视频分析报告
## 基本信息
- 视频时长、分镜数量、分辨率
## 前三秒钩子分析(核心)
- 综合评分
- 5维度评分表格
- 钩子类型
- 优势/不足/优化建议
- 留存预测
## 分镜概览
- 前10个分镜的概览表格
## BGM 分析
- 音乐风格、情绪基调、节拍
## 场景分析
- 主要场景、视频风格、目标受众
- 平台推荐
报告生成时间
输入格式
breakdown.json(必需)
{
"duration": 30.5,
"segment_count": 12,
"resolution": "1920x1080",
"segments": [...],
"bgm_analysis": {
"music_style": {"primary": "流行"},
"emotion": {"primary": "欢快"},
"tempo": {"bpm_estimate": 120, "pace": "中速"}
},
"scene_analysis": {
"primary_scene": "室内",
"video_style": {"overall": "生活方式", "target_audience": ["年轻人"]},
"platform_recommendations": [...]
}
}
hook_analysis.json(可选)
{
"overall_score": 7.5,
"visual_impact": 8.0,
"visual_comment": "评价...",
"language_hook": 7.0,
"language_comment": "评价...",
"emotion_trigger": 7.5,
"emotion_comment": "评价...",
"information_density": 7.0,
"info_comment": "评价...",
"rhythm_control": 8.0,
"rhythm_comment": "评价...",
"hook_type": "好奇型",
"strengths": ["优点1", "优点2"],
"weaknesses": ["不足1"],
"suggestions": ["建议1", "建议2"],
"retention_prediction": "中:50-70%,因为..."
}
输出格式
Markdown 格式的完整报告文本,直接输出到 stdout。
示例
# 完整流程
python ../video-breakdown-skill/scripts/process_video.py "https://example.com/video.mp4" > breakdown.json
cat breakdown.json | python ../hook-analyzer-skill/scripts/analyze_hook_segments.py - > hooks.json
# (hooks.json 需经 LLM 评分后得到 hook_analysis.json)
python scripts/generate_report.py breakdown.json hook_analysis.json > report.md
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.
- 4d ago First seen · 141 lines · 89 tokens per session scan A 8cf6a4ccf7b7
report-generator is a skill published in the GitHub repository bytedance/agentkit-samples (446 stars, last pushed today), licensed Apache-2.0. It adds 89 tokens to every session and 1,097 once invoked, about $0.0004 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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