Borrowing it
Nothing to install: this file belongs to Pauesome/Paid-Media-MCP. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Pauesome/Paid-Media-MCP/main/.claude/skills/tiktok-ads/SKILL.mdgit clone --depth 1 https://github.com/Pauesome/Paid-Media-MCPWrote 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.
[](https://agentmods.dev/skills/pauesome/paid-media-mcp/tiktok-ads)<a href="https://agentmods.dev/skills/pauesome/paid-media-mcp/tiktok-ads"><img src="https://agentmods.dev/badge/skills/pauesome/paid-media-mcp/tiktok-ads/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.
<a href="https://agentmods.dev/skills/pauesome/paid-media-mcp/tiktok-ads"><img src="https://agentmods.dev/badge/skills/pauesome/paid-media-mcp/tiktok-ads.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00099 | $0.00829 |
| Opus 5 | $0.00049 | $0.00415 |
| Sonnet 5 | $0.00020 | $0.00166 |
| Haiku 4.5 | $0.00010 | $0.00083 |
Grade A, and why
tiktok-ads 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.
How it starts
The opening of the file, as written. The whole thing — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
TikTok Ads Analysis — Spain
Entry point for any TikTok Ads request. Delegates the deep audit to the audit-tiktok agent; this skill resolves inputs, pulls a quick snapshot, and routes.
All thresholds come from .claude/references/benchmarks-spain.md.
Required Inputs
client_id— resolve vialist_clients. Best-guess match on label; flag if ambiguous.date_range_start/date_range_end(ISO) — default last 14 days.- Fail fast with a clear error block on missing input — do not prompt.
Read First
.claude/references/benchmarks-spain.md.claude/references/compliance-eu-spain.md— TikTok EU consent + restricted-category rules (alcohol, gambling, financial services, healthcare).claude/references/platform-specs.md— TikTok creative specs + hook standards
Data Collection (parallel MCP calls)
get_tiktok_campaign_performanceget_tiktok_ad_performance(video_watched_2s/video_watched_6s+ creative time series)get_tiktok_hourly_performance(dayparting + spend concentration)get_tiktok_auction_rankings(auction competitiveness)get_tiktok_anomaly_signal(spend / performance anomaly detection)
Routing
- Quick question: answer inline from pulled data. No report file.
- Full audit: launch the audit-tiktok agent with
client_id+ date range. It writes the scored report and returns the path.
Hook Detection (use actual time-series — never guess from CTR)
For each ad with spend > €100:
hold_rate_2s = video_watched_2s / impressions
hold_rate_6s = video_watched_6s / impressions
IF hold_rate_2s < 0.30 → WEAK_HOOK
IF hold_rate_2s ≥ 0.30 AND hold_rate_6s < 0.10 → STRONG_HOOK_WEAK_BODY
Ads with < €100 spend in the period: skip, insufficient data.
Critical Checks (severity ×5)
- TT-CR2 2s hold ≥ 30% on top-spend ads · TT-CR3 < 20% of spend on ads with
below_average_count >= 1· TT-AN1 nosevereanomaly flags
Hard Rules (mirror audit-tiktok)
- Use EU restricted-category rules — never US
- Verify Pixel + Events API active in TikTok Events Manager; missing Events API is Critical (configuration check — confirm in platform, not from data)
- Watermarks from Reels/Shorts = creative-flag finding (TikTok deprioritizes)
- Weight ROAS / CPA above CTR / CVR when metrics disagree
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.
- 12d ago First seen · 83 lines · 99 tokens per session scan A c8f80dfab1d4
tiktok-ads is a skill published in the GitHub repository Pauesome/Paid-Media-MCP (1 stars, last pushed 2mo ago), licensed MIT. It adds 99 tokens to every session and 829 once invoked, about $0.0005 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 2026-08-31.
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