TikTok Ad Research

Follow shared public skill rules in:

• postplus-shared public skill rules

Use this skill when the user wants paid TikTok ad intelligence, not organic creator or content discovery.

Typical requests:

• Find TikTok ad creatives that are performing well
• Inspect top ads for a category, country, or objective
• Study how competitor ads handle hooks, selling points, and CTAs
• Extract ad briefs from Creative Center data

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

Core Rule

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

Preferred Actor

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.

Minimal Toolchain

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

Public Skill Execution Contract

• 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 PostPlus Cloud service is unavailable, unauthorized, or returns a stable network error, stop immediately instead of switching to ad hoc shell glue

Recommended Workflow

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

Example

Run the actor:

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:

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:

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

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

Handoff

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