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-learning-phase-trackergit 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-learning-phase-tracker)<a href="https://agentmods.dev/skills/ad-superpowers/ad-superpowers-plugin/tiktok-learning-phase-tracker"><img src="https://agentmods.dev/badge/skills/ad-superpowers/ad-superpowers-plugin/tiktok-learning-phase-tracker/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-learning-phase-tracker"><img src="https://agentmods.dev/badge/skills/ad-superpowers/ad-superpowers-plugin/tiktok-learning-phase-tracker.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 | $0.00115 | $0.05280 |
| Opus 5.5 | $0.00046 | $0.02112 |
| Sonnet 5.5 | $0.00023 | $0.01056 |
| Haiku 4.5 | $0.00012 | $0.00528 |
Grade A, and why
tiktok-learning-phase-tracker 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 — 740 lines — stays where its author put it; the contents beside it link to each section on GitHub.
TikTok Learning Phase Tracker
Analyzer for TikTok Ads learning phase management. Predicts edit impact and advises on optimal timing for changes.
Learning Phase Basics
What Is TikTok's Learning Phase?
TIKTOK LEARNING PHASE EXPLAINED
================================
Period during which TikTok's algorithm collects data about:
+-- Which users are most relevant
+-- Optimal bid prices per auction
+-- Best times and placements
+-- Creative performance patterns
STATUS INDICATORS:
+-- "Learning": Algorithm is collecting data
+-- "Active": Exited, stable optimization
+-- "Learning Limited": Not enough conversions
+-- "Not Delivering": Budget/bid issues
Exit Criteria
TIKTOK EXIT REQUIREMENTS
========================
MINIMUM REQUIREMENTS:
+-- 50 conversion events per week per ad group
+-- 72-hour initial learning window
+-- Sufficient budget for target CPA
+-- Stable creative performance
FORMULA FOR MINIMUM BUDGET:
Daily Budget = (Target CPA x 50) / 7
EXAMPLE:
+-- Target CPA: EUR20
+-- Minimum weekly: EUR20 x 50 = EUR1,000
+-- Minimum daily: EUR1,000 / 7 = EUR143/day
+-- With buffer (1.5x): EUR215/day recommended
72-HOUR RULE:
+-- First 72 hours: Make NO changes
+-- Algorithm learns fundamental patterns
+-- Edits during this period = reset learning
+-- Avoid even small changes
Learning Phase Statuses
STATUS INTERPRETATION
=====================
STATUS: Learning (Normal)
+-- Duration: 0-7 days
+-- Meaning: Algorithm is actively learning
+-- Action: WAIT, do not change
+-- Expectation: CPA fluctuates 30-50%
+-- Exit: After 50+ conversions/week
STATUS: Learning Limited
+-- Duration: >7 days without exit
+-- Causes:
| +-- Budget too low
| +-- Audience too small (<100k)
| +-- Bid too low
| +-- Event too rare
+-- Action: Intervene (see Exit Strategies)
+-- Urgency: High
STATUS: Active
+-- Meaning: Learning complete
+-- CPA: Stabilized
+-- Action: Optimize, scale
+-- Edits: Careful, can re-trigger
STATUS: Not Delivering
+-- Causes:
| +-- Budget exhausted
| +-- Bid too low for auction
| +-- Creative rejected
| +-- Audience exhausted
+-- Action: Diagnose and fix
+-- Urgency: Critical
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 · 740 lines · 115 tokens per session scan A eddd5fa05a71
tiktok-learning-phase-tracker is a skill published in the GitHub repository Ad-Superpowers/ad-superpowers-plugin (5 stars, last pushed 29d ago), licensed MIT. It adds 115 tokens to every session and 5,280 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.
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