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/hook-analyzer-skillWrote 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/hook-analyzer-skill)<a href="https://agentmods.dev/skills/bytedance/agentkit-samples/hook-analyzer-skill"><img src="https://agentmods.dev/badge/skills/bytedance/agentkit-samples/hook-analyzer-skill/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/hook-analyzer-skill"><img src="https://agentmods.dev/badge/skills/bytedance/agentkit-samples/hook-analyzer-skill.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 | $0.00093 | $0.01148 |
| Opus 5.5 | $0.00037 | $0.00459 |
| Sonnet 5.5 | $0.00019 | $0.00230 |
| Haiku 4.5 | $0.00009 | $0.00115 |
Grade A, and why
hook-analyzer 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 1mo 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:
- hook-analyzer — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
视频前三秒钩子分析 (Hook Analyzer)
概述
视频前三秒钩子分析技能从分镜拆解结果中提取前三秒的分镜数据，并构造结构化的分析上下文，包含视觉内容描述、关键帧图片 URL、运镜方式等信息，为后续的 LLM 多维度评分分析提供输入。
适用场景
- 短视频优化：评估视频开头的吸引力，提升 3 秒留存率
- 创意评审：从5个维度量化分析钩子质量
- 竞品对标：对比不同视频开头的钩子策略
分析维度
本技能提取的数据供以下 5 个维度评分（由 LLM 完成评分）：
| 维度 | 权重 | 评估要点 |
|---|---|---|
| 视觉冲击力 | 30% | 画面构图、色彩、光影、运镜动感 |
| 语言钩子 | 25% | 文案与画面配合度、悬念制造 |
| 情绪唤起 | 15% | 画面情绪、人物表情、氛围营造 |
| 信息密度 | 15% | 有效信息量、核心价值传达 |
| 节奏掌控 | 15% | 分镜切换节奏、平台特性适配 |
使用步骤
方式一：从文件读取分镜数据
# 1. 先使用 video-breakdown skill 处理视频并获取分镜结果
python ../video-breakdown-skill/scripts/process_video.py "https://example.com/video.mp4" > breakdown.json
# 2. 提取前三秒分镜数据
python scripts/analyze_hook_segments.py breakdown.json
方式二：通过 stdin 管道传入
cat breakdown.json | python scripts/analyze_hook_segments.py -
输出格式
{
"segment_count": 3,
"total_duration": 2.8,
"total_video_segments": 15,
"analysis_mode": "multimodal",
"segments": [
{
"index": 0,
"start_time": 0.0,
"end_time": 1.0,
"duration": 1.0,
"visual_content": "画面描述",
"speech_text": "语音文字",
"shot_type": "特写",
"camera_movement": "推镜头",
"function_tag": "开场",
"frame_images": [
{"type": "image_url", "image_url": {"url": "https://..."}}
],
"frame_count": 3
}
]
}
钩子类型分类
分析结果可用于识别以下钩子类型：
- 痛点型：直击用户痛点，引发共鸣
- 好奇型：设置悬念，引发好奇
- 冲突型：制造对比或冲突吸引注意
- 价值型：直接展示价值承诺
- 情感型：以情感共鸣打动用户
- 视觉冲击型：通过强烈视觉效果吸引
- 悬念型：留下悬念引导继续观看
评分标准
| 分数段 | 等级 | 描述 |
|---|---|---|
| 9-10 | 顶级 | 极强的吸引力和创意 |
| 7-8 | 优秀 | 具备良好的吸引力 |
| 5-6 | 一般 | 有改进空间 |
| 3-4 | 较弱 | 需要重大改进 |
| 1-2 | 很差 | 基本没有吸引力 |
注意事项
- 此脚本只做数据提取，不做 LLM 评分（评分由 Agent 的 LLM 完成）
- 提取的
frame_images字段中包含关键帧 URL，可供 Vision 模型直接分析 - 每个分镜最多取前 3 帧关键帧，避免 token 超限
- 输入必须是
process_video返回的完整 JSON 数据
故障排除
| 问题 | 解决方案 |
|---|---|
| 输出为空 segments | 确认输入 JSON 包含 segments 字段且不为空 |
| 前三秒无分镜 | 视频可能从静止画面开始，检查原始数据 |
| 关键帧 URL 失效 | TOS 签名 URL 可能已过期，重新拆解获取 |
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.
- 1mo ago First seen · 112 lines · 93 tokens per session scan A 1ec714cc0650
hook-analyzer is a skill published in the GitHub repository bytedance/agentkit-samples (474 stars, last pushed 2d ago), licensed Apache-2.0. It adds 93 tokens to every session and 1,148 once invoked, about $0.0004 per session on Opus 5.5. 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.
Other skills, from other repositories
eval-dataset-design
A guide to creating reliable test sets for AI agents. Each evaluation task includes the request, the simulated user's behaviour, a reset starting state, and a success check that can be independently verified.
aatmf-t10-confidentiality-breach
AATMF T10 — Integrity & Confidentiality Breach. System prompt extraction, training-data extraction, model-weight leakage, private-key recovery.
ensembl-database
Query Ensembl genome database REST API for 250+ species. Gene lookups, sequence retrieval, variant analysis, comparative genomics, orthologs, VEP predictions, for genomic research.
skill-news-intelligence
A daily industry-news research workflow that gathers in-depth articles and updates from Chinese technology, business, creator, and AI publications. It filters the results by a creator’s field and turns them into a brief with possible content topics.
sidewinder-rattlesnake
Adversary-emulation profile for SideWinder (G0121 / Rattlesnake / T-APT-04 / Razor Tiger), India's suspected state-sponsored cyber-espionage actor.
meeting-summarizer
Summarize a completed meeting from its transcript. Produces a structured summary with key decisions, action items, and discussion highlights. Triggered automatically when a meeting ends.