tiktok-ad-research
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ChineseTikTok Ad Research
TikTok广告研究
Follow shared release-shell rules in:
- release-shell rules
postplus-shared
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:
- research preferences
postplus-shared ${CLAUDE_SKILL_DIR}/references/task-shapes.md${CLAUDE_SKILL_DIR}/references/input-notes.md${CLAUDE_SKILL_DIR}/references/normalized-schema.md
遵循中的发布shell规则:
postplus-shared- release-shell规则
postplus-shared
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:
- 研究偏好
postplus-shared ${CLAUDE_SKILL_DIR}/references/task-shapes.md${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路径下
<work-folder>/.postplus/tiktok-ads/ - 仅将面向用户的最终摘要或精选导出内容放在目录外
.postplus/ - 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
- classify the request into a paid-ad task shape
- write a small actor input JSON
- run the actor with a narrow scope first
- normalize into the local ad schema
- analyze repeated hooks, brands, objectives, regions, and CTA language
- only then turn it into a brief or recommendation
- 将请求归类为付费广告任务类型
- 编写小型执行器输入JSON
- 先以窄范围运行执行器
- 将数据标准化为本地广告 schema
- 分析重复出现的钩子话术、品牌、营销目标、区域及CTA语言
- 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.jsonNormalize:
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.jsonAnalyze:
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.jsonGood 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