A TikTok Ads analysis workflow for Spanish clients that reviews campaign, ad, viewing, hourly, auction, and anomaly data.

Use it to audit TikTok campaigns and ads for a client over a specified date range, including creative performance, hourly results, auction rankings, and unusual activity.

It brings several performance signals together so problems with creative, timing, spending, or auction competitiveness are easier to identify.

How Pauesome/Paid-Media-MCP configures Claude Code

Reuse

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.

Copy the file

curl -O https://raw.githubusercontent.com/Pauesome/Paid-Media-MCP/main/.claude/skills/tiktok-ads/SKILL.md

Clone the repo

git clone --depth 1 https://github.com/Pauesome/Paid-Media-MCP

Made for: Claude Code.

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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/pauesome/paid-media-mcp/tiktok-ads/github.svg)](https://agentmods.dev/skills/pauesome/paid-media-mcp/tiktok-ads)

Your own site

<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.

Your own site · 80×15

<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>

Per session 99 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 829 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.

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.

Model	Per session	Once invoked
Fable 5.1	$0.00099	$0.00829
Opus 5.5	$0.00040	$0.00332
Sonnet 5.5	$0.00020	$0.00166
Haiku 4.5	$0.00010	$0.00083

Measured 2mo ago against content hash c8f80dfab1d4, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 2mo 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.

.claude/skills/tiktok-ads/SKILL.md · 83 lines

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 via list_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_performance
get_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 no severe anomaly 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

Read the full file on GitHub · 83 lines

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.

2mo ago First seen · 83 lines · 99 tokens per session scan A c8f80dfab1d4

Subscribe to this mod's changes

tiktok-ads is a skill published in the GitHub repository Pauesome/Paid-Media-MCP (2 stars, last pushed 4mo ago), licensed MIT. It adds 99 tokens to every session and 829 once invoked, about $0.0004 per session on Opus 5.5. 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.

Analytics and metrics Marketing and SEO

tiktok-ads next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
tiktok-adsPauesome/Paid-Media-MCP	Skill	2	4mo ago	MIT	A	99	Claude Code
imagegenopenai/codex	Skill	133k	yesterday	Apache-2.0	A	113	Codex
playwright-climicrosoft/playwright	Skill	99k	yesterday	Apache-2.0	A	19	Claude Code
local-ai-agentsmicrosoft/ai-agents-for-beginners	Skill	79k	4d ago	MIT	A	200	Claude Code, Codex
systematic-debuggingobra/superpowers	Skill	313k	3d ago	MIT	A	21	Claude Code
next-cache-components-adoptionvercel/next.js	Skill	143k	yesterday	MIT	A	95	Claude Code
next-partial-prefetching-adoptionvercel/next.js	Skill	143k	yesterday	MIT	A	103	Claude Code

Paid-Media-MCP: Skill for Claude Code