Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Ad-Superpowers/ad-superpowers-plugin --skill tiktok-attribution-guidegit clone --depth 1 https://github.com/Ad-Superpowers/ad-superpowers-pluginWrote 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/ad-superpowers/ad-superpowers-plugin/tiktok-attribution-guide)<a href="https://agentmods.dev/skills/ad-superpowers/ad-superpowers-plugin/tiktok-attribution-guide"><img src="https://agentmods.dev/badge/skills/ad-superpowers/ad-superpowers-plugin/tiktok-attribution-guide/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/ad-superpowers/ad-superpowers-plugin/tiktok-attribution-guide"><img src="https://agentmods.dev/badge/skills/ad-superpowers/ad-superpowers-plugin/tiktok-attribution-guide.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 | $0.00114 | $0.04859 |
| Opus 5.5 | $0.00046 | $0.01944 |
| Sonnet 5.5 | $0.00023 | $0.00972 |
| Haiku 4.5 | $0.00011 | $0.00486 |
Grade A, and why
tiktok-attribution-guide 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 1mo 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 — 553 lines — stays where its author put it; the contents beside it link to each section on GitHub.
TikTok Attribution Guide
Purpose
Understand and optimize TikTok's unique attribution model. TikTok's entertainment-first nature means view-through attribution (VTA) captures significantly more value than traditional click-based platforms. This skill helps advertisers measure TikTok's true impact and prove ROI.
Why TikTok Attribution is Different
┌─────────────────────────────────────────────────────────────────────────────┐
│ TIKTOK vs TRADITIONAL PLATFORMS │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ TRADITIONAL (Google, Meta) TIKTOK │
│ ────────────────────────── ────── │
│ │
│ User Intent: HIGH User Intent: LOW (discovery) │
│ ├─ Search-driven ├─ Entertainment-driven │
│ ├─ Click-to-convert ├─ Watch → remember → convert │
│ └─ CTR: 2-5% └─ CTR: 0.5-1.5% │
│ │
│ Attribution: Attribution: │
│ ├─ 70-80% click-through (CTA) ├─ 40-60% click-through (CTA) │
│ ├─ 20-30% view-through (VTA) ├─ 40-60% view-through (VTA) │
│ └─ VTA often ignored └─ VTA is CRITICAL │
│ │
│ If you only measure CTA on TikTok: │
│ ══════════════════════════════════ │
│ You're missing 40-60% of TikTok's actual impact! │
│ │
└─────────────────────────────────────────────────────────────────────────────┘
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.
- 1mo ago First seen · 553 lines · 114 tokens per session scan A ab19f76e67e0
tiktok-attribution-guide is a skill published in the GitHub repository Ad-Superpowers/ad-superpowers-plugin (5 stars, last pushed 29d ago), licensed MIT. It adds 114 tokens to every session and 4,859 once invoked, about $0.0005 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 2026-09-11.
Other skills, from other repositories
analytics
Growth analytics authority — GA4 event tracking, conversion funnels, attribution models, cohort analysis, A/B testing, Supabase analytics queries, and revenue reporting patterns.
creative-generate
A workflow for creating advertising images and banners from a strategy brief. It separates the main visual artwork from the accurately typeset text and produces variants for display or social formats.
cro-optimizer
CRO specialist that pulls live analytics data via the Humblytics MCP, analyzes conversion funnels, identifies drop-off points, and generates prioritized A/B test hypotheses. Use when analyzing conversion rates, diagnosing funnel leaks, optimizing signup flows, or creating test roadmaps. Triggers: CRO, conversion rate…
learn
Save a marketing diagnosis insight to the pro-diagnosis knowledge base so it is applied in future operations across all platforms. Use when the user runs /learn, explicitly teaches the agent a marketing insight, corrects the agent's analysis, or asks to remember/record an operational learning for next time. Also use…
lead-form-create
Create a Meta Instant Form (Lead Ad form) through a one-question-at-a-time interview. Use when the user asks to create a lead form, Instant Form, CV form, or similar. The agent collects required parameters via a guided dialogue, confirms, then calls metaadsleadformscreate. Also use when the user asks in Japanese…
ai-hive-simulate-dn-samuel
A research and content-planning workflow that studies DN-Samuel's public material and turns its general methods into original work using your own facts and experience. It can also organise authorised documents into a source-linked reference library.