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/sugarforever/01coder-agent-skillsnpx agentmods add skills/sugarforever/01coder-agent-skills/video-plannerWrote 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/sugarforever/01coder-agent-skills/video-planner)<a href="https://agentmods.dev/skills/sugarforever/01coder-agent-skills/video-planner"><img src="https://agentmods.dev/badge/skills/sugarforever/01coder-agent-skills/video-planner/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/sugarforever/01coder-agent-skills/video-planner"><img src="https://agentmods.dev/badge/skills/sugarforever/01coder-agent-skills/video-planner.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
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 →
- medium Rogue Agent · line 3 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
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 | $0.00105 | $0.03343 |
| Opus 5 | $0.00053 | $0.01672 |
| Sonnet 5 | $0.00021 | $0.00669 |
| Haiku 4.5 | $0.00011 | $0.00334 |
Grade A, and why
video-planner 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 — 200 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Video Planner & Publishing Materials
Help YouTubers/UP主 prepare video content: write structured scripts (口播稿), blog posts, platform-specific publishing materials, and an X (Twitter) promo tweet.
Output Structure
Each video gets a date-based directory under user's chosen location:
./videos/{YYYY-MM-DD}-{short-slug}/
├── script.md # 视频口播稿
├── blog.md # 博客文章
├── youtube.md # YouTube 发布素材
├── bilibili.md # Bilibili 发布素材
├── x.md # X (Twitter) 推广文案
├── fact-check.md # 技术事实审核表(技术类视频必出,见 Step 8)
└── cover.png # 封面(可选,见 Step 10)
Interactive Workflow
Step 1: Gather Information
IMPORTANT: Before writing anything, collect sufficient context from the user. Ask the user:
请提供以下信息,帮我为你准备视频内容:
1. **视频主题**:这期视频讲什么?(必填)
2. **目标平台**:YouTube / Bilibili / 两者都有?(默认:两者)
3. **目标时长**:大约几分钟?(默认:10分钟)
4. **目标观众**:面向什么人群?(如:开发者、AI爱好者、初学者)
5. **关键要点**:你希望视频覆盖哪些要点?(可以是大纲、笔记、或链接)
6. **相关资料**:有没有参考文章、文档、代码仓库?(可选,我可以帮你研究)
已有部分信息的话,直接告诉我就好,缺的我会追问。
If the user provides partial info upfront, only ask for the missing pieces.
Step 2: Research (If Needed)
If the user provides reference URLs, docs, or repos:
- Use WebFetch to read reference articles/docs
- Use Read/Grep/Glob to explore code repos
- Use WebSearch to find supplementary information
- Summarize key points for script use
If the topic is about a specific technology/tool:
- Research its core features and selling points
- Find common pain points it solves
- Look for comparison angles with alternatives
Step 3: Create Directory
Create the date-based directory:
./videos/{YYYY-MM-DD}-{short-slug}/
Example: ./videos/2026-03-07-react-server-components/
The short-slug should be a brief, descriptive kebab-case label derived from the topic.
Step 4: Write Script (script.md)
Write a structured 口播稿 in the user's preferred language. The script is what the user reads aloud while recording, so two things matter as much as the content:
- 照着念友好(read-aloud first):每一句都要是「能顺口念出来」的话,不是写出来给人读的文章。太书面、太像技术文档的句子会拖慢录制 - 写完后过一遍,把长定语、术语堆砌、嵌套从句改成短句口语(详见
references/script-guidelines.md的「照着念友好」规则)。 - 屏上呈现推荐(screen-share cues):作为 AI,你要逐段智能判断这段口播配什么画面最好 - 某个网页的某一段、某个应用 / 终端界面、一段代码、还是一张对比 / 速查卡 - 并写成
(屏幕:……)备注。目的:用户照着口播稿念的同时,一眼知道该在屏幕上呈现什么。在脚本开头加一张「屏上呈现总则」总表(画面来源 + 逐段映射),正文每段再用内联(屏幕:……)落到具体内容。不是每段都要切画面 - 判断「需不需要」也是你的工作,纯口播段就标「纯口播」。
What ships with it
12 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.
- references/blog-guidelines.md 974 B
- references/examples-tutorial.md 9.0 KB
- references/examples.md 4.5 KB
- references/fact-audit.md 2.4 KB
- references/platform-differences.md 4.6 KB
- references/script-guidelines.md 5.4 KB
- templates/bilibili.md 910 B
- templates/blog.md 96 B
- templates/fact-check.md 1.3 KB
- templates/script.md 2.5 KB
- templates/x.md 777 B
- templates/youtube.md 1.5 KB
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 · 200 lines · 105 tokens per session scan A dec6fbb95022
video-planner is a skill published in the GitHub repository sugarforever/01coder-agent-skills (134 stars, last pushed 2mo ago), licensed MIT. It adds 105 tokens to every session and 3,343 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 2026-08-30.
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