tiktok-lead-generation-operator

tiktok-lead-generation-operator is a skill for Codex from aronhy/tiktok-agent-skills. It costs 48 tokens per session (727 once invoked), scanned A, original, MIT.

An operating guide for turning TikTok activity into measurable leads, such as inquiries, appointments, or consultations. Leads are people who show interest and take a contact or booking action.

In plain words
What is it for?
Use it to plan TikTok lead generation, private-channel conversion, inquiry capture, appointment acquisition, or a reviewable response process.
Why use it?
It helps connect content, calls to action, qualification questions, and attribution so the resulting inquiries can be measured without treating public engagement as a private conversion.

Skill for Codex

Written for Codex: agents/openai.yaml present. Also seen: $skill-name invocation.

Good fit Use it to plan TikTok lead generation, private-channel conversion, inquiry capture, appointment acquisition, or a reviewable response process.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/aronhy/tiktok-agent-skills/tiktok-lead-generation-operator
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 aronhy/tiktok-agent-skills --skill tiktok-lead-generation-operator
Clone the repo
git clone --depth 1 https://github.com/aronhy/tiktok-agent-skills

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 tiktok-lead-generation-operator

README.md
[![agentmods](https://agentmods.dev/badge/skills/aronhy/tiktok-agent-skills/tiktok-lead-generation-operator/github.svg)](https://agentmods.dev/skills/aronhy/tiktok-agent-skills/tiktok-lead-generation-operator)
Your own site
<a href="https://agentmods.dev/skills/aronhy/tiktok-agent-skills/tiktok-lead-generation-operator"><img src="https://agentmods.dev/badge/skills/aronhy/tiktok-agent-skills/tiktok-lead-generation-operator/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 tiktok-lead-generation-operator

Your own site · 80×15
<a href="https://agentmods.dev/skills/aronhy/tiktok-agent-skills/tiktok-lead-generation-operator"><img src="https://agentmods.dev/badge/skills/aronhy/tiktok-agent-skills/tiktok-lead-generation-operator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 48 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 727 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 $0.00048 $0.00727
Opus 5 $0.00024 $0.00364
Sonnet 5 $0.00010 $0.00145
Haiku 4.5 $0.00005 $0.00073

Measured 12d ago against content hash 778560daee2f, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

tiktok-lead-generation-operator 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.

skills/tiktok-lead-generation-operator/SKILL.md · 28 lines

What it actually says

TikTok 内容获客运营

开始任何分析前,完整读取 references/workflow.mdreferences/report-template.md。详细定义、边界和报告格式只以两份参考为准。

控制流程

边界请求优先于定制方案输入门槛:仅判断指标可计算性时,直接应用同一统计总体或可追踪 cohort、同一归因窗口和兼容来源系统规则。兼容来源系统是指事件位于一个系统内,或存在明确、可信、可审计的跨系统关联/归因方法;漏斗与 KPI 必须记录关联键/方法。只有纯禁止操作请求(仅要求抓取联系人或执行外联,不含其他合规定制规划)可绕过四项输入门槛;此时必须拒绝,并同时提供不依赖业务假设的自愿加入内容方向、一个 CTA、资格问题与仅供审核的回复草稿,四项缺一不可。混合请求若同时包含禁止操作和合规定制规划,先只拒绝禁止操作,再对合规部分执行以下输入门槛;有缺项时只问第一个缺项,不提供定制策略:

  1. 依次确认目标国家或地区、具体产品或服务、目标客户、期望私域动作及渠道。一次只问第一个未收到的问题,然后停止;“收到”仅限用户明确表述或所给 brief 无歧义说明。
  2. 未提供账号 URL 时使用从零模式;所有定位和内容结论均标为假设。提供 URL 时,先复用 $tiktok-account-audit 的公开证据、来源账本与可信度规则,再做获客改造;绝不把公开互动推断成私域转化。
  3. 仅输出计划、可审阅的内容和回复草稿。不得发送私信、抓取或导入联系人、提交表单、发布内容,或改动任何外部账号。
  4. 以报告模板的固定十二节顺序交付,并为结论标注 A/B/C 可信度。
情况 处理
缺少必填输入 只问固定顺序中的第一个缺项并停止。
无账号 URL 从零模式;将账号、受众和结果写为假设。
有账号 URL 复用 tiktok-account-audit,仅采用已验证公开证据。
Shop 为主目标 转交 $tiktok-shop-operator,不以本 Skill 为主流程。
Shop 与线索并存 分开交易与线索路径;若影响方案,询问哪个业务结果优先。

缺项在用户明确说明无法提供某项但仍要求继续时,才给出带条件的有限框架,可信度为 C;“直接给方案”或“不要问问题”不等于无法提供,仍须只问第一个缺项并停止;不得伪装为定制正式报告。

Files

What ships with it

3 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 · 28 lines · 48 tokens per session scan A 778560daee2f

Subscribe to this mod's changes

tiktok-lead-generation-operator is a skill published in the GitHub repository aronhy/tiktok-agent-skills (152 stars, last pushed 1mo ago), licensed MIT. It adds 48 tokens to every session and 727 once invoked, about $0.0002 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.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

local-ai-agents

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…

microsoft/ai-agents-for-beginners · 200 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

insight-error-page

Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…

vercel/next.js · 83 tokens

next-cache-components-optimizer

Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…

vercel/next.js · 170 tokens

next-partial-prefetching-adoption

Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…

vercel/next.js · 103 tokens