tiktok-ad-research

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English
🇨🇳

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Chinese

TikTok Ad Research

TikTok广告研究

Follow shared release-shell rules in:
  • postplus-shared
    release-shell rules
Use this skill when the user wants paid TikTok ad intelligence, not organic creator or content discovery.
Typical requests:
  • 找 TikTok 上跑得好的广告素材
  • 看某个类目 / 国家 / objective 的 top ads
  • 研究竞品广告怎么做 hook、卖点、CTA
  • 从 Creative Center 数据里提炼 ad brief
Read first:
  • postplus-shared
    research preferences
  • ${CLAUDE_SKILL_DIR}/references/task-shapes.md
  • ${CLAUDE_SKILL_DIR}/references/input-notes.md
  • ${CLAUDE_SKILL_DIR}/references/normalized-schema.md
遵循
postplus-shared
中的发布shell规则:
  • postplus-shared
    release-shell规则
当用户需要TikTok付费广告情报,而非原生创作者或内容挖掘时,使用此技能。
典型请求:
  • 找 TikTok 上跑得好的广告素材
  • 看某个类目 / 国家 / objective 的 top ads
  • 研究竞品广告怎么做 hook、卖点、CTA
  • 从 Creative Center 数据里提炼 ad brief
请先阅读:
  • postplus-shared
    研究偏好
  • ${CLAUDE_SKILL_DIR}/references/task-shapes.md
  • ${CLAUDE_SKILL_DIR}/references/input-notes.md
  • ${CLAUDE_SKILL_DIR}/references/normalized-schema.md

Core Rule

核心规则

Do not treat ad data as if it were organic creator data.
This skill is for:
  • paid creative benchmarking
  • hook and offer analysis
  • objective / region / language comparisons
  • ad-creative sourcing for briefs
This skill is not for:
  • creator discovery
  • community comments research
  • organic content lane mapping
If the user wants organic content or creator research, route to:
  • ${CLAUDE_SKILL_DIR}/_postplus_shared/20-research/tiktok-research/SKILL.reference.md
请勿将广告数据当作原生创作者数据处理。
本技能适用于:
  • 付费创意基准测试
  • 钩子与优惠方案分析
  • 营销目标/区域/语言对比
  • 为广告简报挖掘创意素材
本技能不适用于:
  • 创作者挖掘
  • 社区评论研究
  • 原生内容赛道梳理
如果用户需要原生内容或创作者研究,请引导至:
  • ${CLAUDE_SKILL_DIR}/_postplus_shared/20-research/tiktok-research/SKILL.reference.md

Preferred Actor

首选执行器

Current default:
  • tiktok-creative-center-top-ads
Use this actor when the user wants top-performing Creative Center ads and optional analytics or keyframe metrics.
当前默认:
  • tiktok-creative-center-top-ads
当用户需要Creative Center中的顶级广告及可选分析或关键帧指标时,使用此执行器。

Minimal Toolchain

最小工具链

Use these pieces in combination:
  • scrape:
    • ${CLAUDE_SKILL_DIR}/scripts/collection_actor_run.mjs
  • normalize:
    • ${CLAUDE_SKILL_DIR}/scripts/normalize_tiktok_ads_dataset.mjs
  • analyze:
    • ${CLAUDE_SKILL_DIR}/scripts/analyze_tiktok_ads_dataset.mjs
组合使用以下组件:
  • 抓取:
    • ${CLAUDE_SKILL_DIR}/scripts/collection_actor_run.mjs
  • 标准化:
    • ${CLAUDE_SKILL_DIR}/scripts/normalize_tiktok_ads_dataset.mjs
  • 分析:
    • ${CLAUDE_SKILL_DIR}/scripts/analyze_tiktok_ads_dataset.mjs

Release-Shell Execution Contract

发布Shell执行约定

  • keep actor input JSON, raw datasets, normalized datasets, and analysis caches under
    <work-folder>/.postplus/tiktok-ads/
  • keep only final user-facing summaries or shortlisted exports outside
    .postplus/
  • start with a bounded first pass before broader ad pulls
  • if hosted capability is unavailable, unauthorized, or returns a stable network error, stop immediately instead of switching to ad hoc shell glue
  • 将执行器输入JSON、原始数据集、标准化数据集及分析缓存保存在
    <work-folder>/.postplus/tiktok-ads/
    路径下
  • 仅将面向用户的最终摘要或精选导出内容放在
    .postplus/
    目录外
  • 在进行大范围广告抓取前,先执行小范围的首轮抓取
  • 如果托管功能不可用、未授权或返回持续网络错误,请立即停止操作,不要切换到临时shell脚本

Recommended Workflow

推荐工作流程

  1. classify the request into a paid-ad task shape
  2. write a small actor input JSON
  3. run the actor with a narrow scope first
  4. normalize into the local ad schema
  5. analyze repeated hooks, brands, objectives, regions, and CTA language
  6. only then turn it into a brief or recommendation
  1. 将请求归类为付费广告任务类型
  2. 编写小型执行器输入JSON
  3. 先以窄范围运行执行器
  4. 将数据标准化为本地广告 schema
  5. 分析重复出现的钩子话术、品牌、营销目标、区域及CTA语言
  6. 之后再将结果整理为简报或建议

Example

示例

Run the actor:
bash
node ${CLAUDE_SKILL_DIR}/scripts/collection_actor_run.mjs \
  --collection-key tiktok-ads-top \
  --input ${CLAUDE_SKILL_DIR}/templates/top-ads-sample.json \
  --output <work-folder>/.postplus/tiktok-top-ads-raw.json
Normalize:
bash
node ${CLAUDE_SKILL_DIR}/scripts/normalize_tiktok_ads_dataset.mjs \
  --input <work-folder>/.postplus/tiktok-top-ads-raw.json \
  --actor tiktok-creative-center-top-ads \
  --output <work-folder>/.postplus/tiktok-top-ads-normalized.json
Analyze:
bash
node ${CLAUDE_SKILL_DIR}/scripts/analyze_tiktok_ads_dataset.mjs \
  --input <work-folder>/.postplus/tiktok-top-ads-normalized.json \
  --output <work-folder>/.postplus/tiktok-top-ads-analysis.json
运行执行器:
bash
node ${CLAUDE_SKILL_DIR}/scripts/collection_actor_run.mjs \
  --collection-key tiktok-ads-top \
  --input ${CLAUDE_SKILL_DIR}/templates/top-ads-sample.json \
  --output <work-folder>/.postplus/tiktok-top-ads-raw.json
标准化:
bash
node ${CLAUDE_SKILL_DIR}/scripts/normalize_tiktok_ads_dataset.mjs \
  --input <work-folder>/.postplus/tiktok-top-ads-raw.json \
  --actor tiktok-creative-center-top-ads \
  --output <work-folder>/.postplus/tiktok-top-ads-normalized.json
分析:
bash
node ${CLAUDE_SKILL_DIR}/scripts/analyze_tiktok_ads_dataset.mjs \
  --input <work-folder>/.postplus/tiktok-top-ads-normalized.json \
  --output <work-folder>/.postplus/tiktok-top-ads-analysis.json

Good Output

优质输出

Return:
  • top brands or advertisers in the sample
  • dominant objectives
  • repeated hook language
  • repeated offer language
  • repeated regions / geo scope
  • CTA patterns
  • duration distribution
  • top ads by likes or CTR when available
  • whether the sample is spotlight-curated or filter-driven
Separate:
  • observed ad facts
  • likely creative implications
  • missing evidence
返回内容应包含:
  • 样本中的顶级品牌或广告主
  • 主流营销目标
  • 重复出现的钩子话术
  • 重复出现的优惠话术
  • 重复出现的区域/地理范围
  • CTA模式
  • 广告时长分布
  • 若有数据,显示按点赞量或CTR排序的顶级广告
  • 样本是精选推荐还是筛选得到的
需区分:
  • 观察到的广告事实
  • 可能的创意启示
  • 缺失的证据

Handoff

转接

Escalate after this skill when needed:
  • ad video structure or spoken-line breakdown -> a dedicated visual analysis workflow
  • ad creative review after humans inspect outputs ->
    skills/40-creative/creative-qa
  • organic TikTok benchmark comparison ->
    skills/20-research/tiktok-research
在必要时,使用本技能后可转接至:
  • 广告视频结构或台词拆解 -> 专用视觉分析流程
  • 人工审核后的广告创意评审 ->
    skills/40-creative/creative-qa
  • TikTok原生内容基准对比 ->
    skills/20-research/tiktok-research