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 chenjin-cmd/agent-skills-launch-pack_ --skill douyin-account-launch-expertgit clone --depth 1 https://github.com/chenjin-cmd/agent-skills-launch-pack_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.
[](https://agentmods.dev/skills/chenjin-cmd/agent-skills-launch-pack_/douyin-account-launch-expert)<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.
<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>- 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 | $0.00142 | $0.02134 |
| Opus 5 | $0.00071 | $0.01067 |
| Sonnet 5 | $0.00028 | $0.00427 |
| Haiku 4.5 | $0.00014 | $0.00213 |
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.
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
-
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。
-
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。
- 使用:
-
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。
- 每条内容都先问:
-
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。
-
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。
-
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。
-
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。
-
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。
-
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。
-
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。
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.
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.
- 11d ago First seen · 100 lines · 142 tokens per session scan A cf3f392e6f5a
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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