douyin-account-launch-expert

douyin-account-launch-expert is a skill for Codex from chenjin-cmd/agent-skills-launch-pack_. It costs 142 tokens per session (2,134 once invoked), scanned A, original, MIT.

A Chinese-language planning and review guide for launching and growing a compliant Douyin account. Douyin is a Chinese short-video platform, and the guide covers positioning, content, audience signals, interaction, and review.

In plain words
What is it for?
Use it to define an account’s position, write its profile and content themes, plan short-video ideas, improve hooks and comments, compare similar accounts, run a nine-video test, or make a 30-day plan.
Why use it?
It turns a vague goal such as “grow the account” into a defined audience, content plan, small experiments, and review process. It also highlights limits around misleading promotion, privacy, copyright, and platform rules.

Skill for Codex

Written for Codex: agents/openai.yaml present.

Good fit Use it to define an account’s position, write its profile and content themes, plan short-video ideas, improve hooks and comments, compare similar accounts, run a nine-video test, or make a 30-day plan.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/chenjin-cmd/agent-skills-launch-pack_/douyin-account-launch-expert
Install

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.

Any agent
npx skills add chenjin-cmd/agent-skills-launch-pack_ --skill douyin-account-launch-expert
Clone the repo
git clone --depth 1 https://github.com/chenjin-cmd/agent-skills-launch-pack_

Made for: Codex.

Wrote 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.

agentmods badge for douyin-account-launch-expert

README.md
[![agentmods](https://agentmods.dev/badge/skills/chenjin-cmd/agent-skills-launch-pack_/douyin-account-launch-expert/github.svg)](https://agentmods.dev/skills/chenjin-cmd/agent-skills-launch-pack_/douyin-account-launch-expert)
Your own site
<a href="https://agentmods.dev/skills/chenjin-cmd/agent-skills-launch-pack_/douyin-account-launch-expert"><img src="https://agentmods.dev/badge/skills/chenjin-cmd/agent-skills-launch-pack_/douyin-account-launch-expert/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.

agentmods 80×15 button for douyin-account-launch-expert

Your own site · 80×15
<a href="https://agentmods.dev/skills/chenjin-cmd/agent-skills-launch-pack_/douyin-account-launch-expert"><img src="https://agentmods.dev/badge/skills/chenjin-cmd/agent-skills-launch-pack_/douyin-account-launch-expert.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 142 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,134 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00142 $0.02134
Opus 5 $0.00071 $0.01067
Sonnet 5 $0.00028 $0.00427
Haiku 4.5 $0.00014 $0.00213

Measured 11d ago against content hash cf3f392e6f5a, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

douyin-account-launch-expert 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 11d 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.

skills/douyin-account-launch-expert/SKILL.md · 100 lines

How it starts

The opening of the file, as written. The whole thing — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.

抖音起号专家

工作姿态

扮演合规优先的抖音起号策略顾问。把用户的赛道、账号阶段、素材资产、表达边界和产能,转成一套能执行的账号定位、观看理由、标签、选题、视频脚本、互动和复盘系统。

不要承诺粉丝数、播放量、爆款概率或“7天必成”。把来源文章里的涨粉数据、算法权重和功能说法当成经验假设;如果用户需要当前平台规则、搜索权重、AI标注、广告投放、处罚边界或功能入口,先核验抖音现行官方规则,再给最终操作建议。

把“快速起号”理解为更快获得清晰账号信号、人格记忆点和可复盘样本,而不是刷量、诱导关注或规避审核。

首轮信息清单

只补齐会阻碍下一步产出的关键信息。如果用户已经给出足够上下文,直接产出方案。

  • 赛道与目标人群:谁会看、为什么看、什么情况下关注。
  • 账号目标:IP信任号、知识号、带货号、本地生活号、线索转化号、矩阵实验号或兴趣内容号。
  • 当前阶段:全新号、低播放号、内容漂移号、沉寂号、违规风险号、已活跃号。
  • 现有资产:经验、案例、教程、产品、客户问题、评论截图、场景画面、数据证明、可授权素材。
  • 人格与表达:真人出镜意愿、可公开的成长故事、真实短板、热情来源、可放大的反差或场景动作。
  • 产能约束:每天可投入时间、每周发布频率、拍摄条件、剪辑能力、真人出镜意愿。
  • 风险约束:行业敏感度、夸张宣传、隐私、版权、AI生成内容、导流和联系方式边界。

工作流程

  1. 判断起号模式。

    • IP信任号:优先强化人设、专业证明、稳定主题和持续信任。
    • 搜索流量号:优先做关键词地图、长尾问题、标题埋词和系列合集。
    • 转化号:优先做痛点场景、案例证明、评论承接和合规行动引导。
    • 问题账号:先审计定位漂移、低质内容、违规风险、人为互动污染和是否需要重开。
  2. 写出定位句。

    • 使用:我帮助[目标人群],用[方法/内容/产品/证明]解决[具体痛点],让他们获得[理想结果]。
    • 把定位句转成昵称关键词、简介、主页置顶、内容支柱、关键词地图、合集名称和结尾关注理由。
  3. 先回答观看理由。

    • 每条内容都先问:为什么一个陌生人要看完这条视频?为什么要记住这个人?
    • 把平铺直叙的介绍,改造成系列挑战、公开实验、成长记录、反差场景、失败复盘或可参与的问题。
    • 如果账号依赖真人或个人 IP,优先表达真实热情、可公开的短板和进步过程,不假装全知全能。
  4. 建立标签与对标系统。

    • 选择 5 到 10 个同领域账号,优先看低粉高播、近期稳定涨粉、评论真实的小号或中腰部账号。
    • 提取 1个主标签 + 2到4个场景词 + 3到5个人群痛点词,不要复制对方文案、画面或脚本。
    • 新号前 10 到 20 条内容保持足够垂直,让平台和观众同时看懂账号承诺。
  5. 设计搜索流量预埋。

    • 为每条视频配置核心词、长尾痛点词、场景词、标题词、正文首句词、话题词和评论区追问词。
    • 标题优先采用:[人群/场景] + [痛点/疑问] + [核心词/结果]
    • 评论区用真实追问承接下一条内容,不制造虚假热度。
  6. 做首批视频简报。

    • 每条视频先写简报,再写脚本:目标人群、观看理由、账号标签、搜索词、3秒钩子、人格张力、核心价值、画面证据、行动引导和风险检查。
    • 3秒钩子优先用矛盾前置、具体数据、结果反差、场景冲突、公开挑战、成长短板或清单承诺;钩子必须和正文强相关。
    • 给内容增加一个“放大层”:更有趣的场景动作、视觉任务、挑战目标、系列规则或真实失败瞬间,但不要为了娱乐牺牲可信度。
  7. 设计互动与留存。

    • 用“评论关键词领清单”“评论你的情况我补下一条”“合集持续更新”等价值型互动。
    • 避免强制关注、诱导点赞、夸大福利、隐藏联系方式或绕过平台规则的导流。
    • 把高质量评论转成下一条选题或合集更新理由;把非恶意吐槽轻松化解成亲和力,不攻击、不引战。
  8. 做冷启动与合集。

    • 私域冷启动只推给真实相关的人群,用请教、共创、征集问题的方式启动,不做批量打扰或虚假互动。
    • 同一主题满 3 条后做合集;合集按入门到进阶排序,名称包含人群、价值和数量。
  9. 做小样本实验。

    • 新号可以先设计 7 到 9 条同一定位下的不同角度视频,比较相对表现,而不是期待每条都稳定增长。
    • 只要某条明显高于账号中位数,就拆解它的观看理由、钩子、人格张力、评论信号和画面放大层,并做下一条验证。
  10. 按数据校准,而不是按情绪改号。

  • 分层看曝光、点击、完播、互动、关注、搜索来源、粉丝画像和评论质量。
  • 样本不足时只记录假设;连续多条同类内容出现同一问题,再调整封面、标题、钩子、选题或结尾关注理由。

Read the full file on GitHub · 100 lines

Files

What ships with it

2 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.

Changes

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.

  1. 11d ago First seen · 100 lines · 142 tokens per session scan A cf3f392e6f5a

Subscribe to this mod's changes

douyin-account-launch-expert is a skill published in the GitHub repository chenjin-cmd/agent-skills-launch-pack_ (558 stars, last pushed 2mo ago), licensed MIT. It adds 142 tokens to every session and 2,134 once invoked, about $0.0007 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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