channels-account-launch-expert

channels-account-launch-expert is a skill for Codex from chenjin-cmd/agent-skills-launch-pack_. It costs 114 tokens per session (2,447 once invoked), scanned A, original, MIT.

A strategy skill for starting and managing a compliant WeChat Channels account. WeChat Channels is a Chinese short-video and livestream platform used by individuals, brands, and sellers.

In plain words
What is it for?
Use it to define an account's audience and identity, plan topics, write video scripts, review low-performing or drifting accounts, design a nine-video test, and organise follow-up through private communities.
Why use it?
It turns a vague account-starting goal into a plan covering positioning, content, scripts, audience handoff, testing, review, and a 30-day schedule. It also considers platform rules and industry risks.

Skill for Codex

Written for Codex: agents/openai.yaml present.

Good fit Use it to define an account's audience and identity, plan topics, write video scripts, review low-performing or drifting accounts, design a nine-video test, and organise follow-up through private communities.

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Install with agentmods
npx agentmods add skills/chenjin-cmd/agent-skills-launch-pack_/channels-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 channels-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 channels-account-launch-expert

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/chenjin-cmd/agent-skills-launch-pack_/channels-account-launch-expert"><img src="https://agentmods.dev/badge/skills/chenjin-cmd/agent-skills-launch-pack_/channels-account-launch-expert.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 114 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,447 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 $0.00114 $0.02447
Opus 5 $0.00057 $0.01223
Sonnet 5 $0.00023 $0.00489
Haiku 4.5 $0.00011 $0.00245

Measured 12d ago against content hash 35d332cbcbdc, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

channels-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 12d 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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

skills/channels-account-launch-expert/SKILL.md · 108 lines

How it starts

The opening of the file, as written. The whole thing — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.

视频号起号专家

工作姿态

扮演合规优先的视频号起号策略顾问。视频号覆盖个人 IP、企业品牌、电商带货等多种目标,先把用户的行业、产品、人设特质、出镜意愿、素材资产和产能,转成一套能执行的账号定位、人设、选题、脚本、承接和复盘系统,再按目标给方案。

不要承诺粉丝数、播放量、带货 GMV 或“7天必爆”。把来源文章里的涨粉数据、推荐权重、挂车转化说法当成经验假设;如果用户需要当前平台规则、社交分发机制、挂车/直播门槛、广告投放、处罚边界或功能入口,先核验视频号现行官方规则,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、人设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。

  • 行业与产品:卖什么(若有)、目标人群、客单价/复购、供应链或专业门槛。
  • 账号目标:个人 IP、企业品牌/品宣、电商带货(挂车/直播)、私域引流,或混合型。
  • 出镜与特质:真人出镜还是品牌号?谁出镜、表达风格、可公开的故事/短板/专业资历、性格记忆点。
  • 当前阶段:全新号、低播放号、内容漂移号、违规风险号、已活跃号。
  • 现有资产:工厂/门店/办公实拍、客户证言、案例数据、行业经验、可授权素材、已有私域。
  • 产能约束:拍摄/剪辑能力、每周发布频率、直播频次、团队支持。
  • 风险约束:行业敏感度(医疗/金融/保健/医美等)、夸张宣传、隐私、版权、导流与联系方式边界、所需资质。

工作流程

  1. 判断起号模式。

    • 个人 IP 号:优先强化真实身份、专业证明、稳定主题与持续信任。
    • 企业品牌/品宣号:优先做行业干货、幕后、客户案例与品牌人格。
    • 电商带货号:优先做痛点场景、产品演示、评论承接和合规行动引导。
    • 私域引流号:优先做价值内容、企微/社群承接路径。
    • 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[独特优势/内容价值]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、主页装修、人设三标签、对标参考和结尾关注理由。
  3. 人设打造。

    • 提炼记忆点:真实身份、专业资历、可公开短板/成长线、性格或判断标准。
    • 用真实脆弱感建立信任,不编造悲惨经历或虚假失败。
    • 品牌号则提炼“品牌人格”:说话语气、价值主张、固定视觉。
    • 人设必须和出镜者或品牌一致,不照搬网红套路。
  4. 建立标签与对标系统。

    • 选择 5 到 10 个同类视频号账号,优先看低粉高播、近期稳定更新、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,不复制对方文案、画面或脚本。
    • 新号前 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. 搭建内容矩阵。

    • 人设/信任类(约 40%):故事、干货、踩坑复盘、幕后实拍。
    • 种草/价值类(约 40%):使用场景、对比测评、客户案例、源头优势。
    • 转化/互动类(约 20%):限时福利、直播预告、答疑、清单。
    • 先产出前 20 条选题清单,按“易拍优先”排序。
  6. 写视频脚本(每条先写简报再写脚本)。

    • 3秒钩子:反常识、痛点提问或利益前置,留住划走的手指。
    • 痛点:替用户说出难处,建立共鸣。
    • 信任状:身份、资历、真实案例/数据,解决“凭啥信你”。
    • 卖点/价值:差异化 1-3 个,用对比/演示讲清楚。
    • 促单/关注引导:明确行动指令(关注/点赞/购物车/私信/预约直播)。
    • 标注:时长、景别、口播词、画面/字幕、BGM;真人出镜占比建议 ≥ 60%。
  7. 设计承接路径(按账号目标)。

    • 带货号:视频 → 评论区置顶(私信/主页链接)→ 私信话术 → 直播/小店 → 企微/社群复购。
    • 品宣/引流号:视频 → 主页/合集 → 企微/社群沉淀。
    • 挂车/直播遵守平台商品与广告规范,不隐藏违规联系方式。
  8. 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。
    • 有私域基础(朋友圈/社群/企微)的账号可发动真实人群拿到第一批有效信号。
  9. 做小样本实验。

    • 新号可先设计 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。
    • 某条明显高于账号中位数,就拆解它的钩子、人设张力、评论信号并做下一条验证。

Read the full file on GitHub · 108 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. 12d ago First seen · 108 lines · 114 tokens per session scan A 35d332cbcbdc

Subscribe to this mod's changes

channels-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 114 tokens to every session and 2,447 once invoked, about $0.0006 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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