Data
PromptBeginner5 minmarkdown
- Never flatten cross-network data into a single average — divergence is signal
not noise.
0
Explore
Find agent skills by outcome
124,756 skills indexed with the new KISS metadata standard.
Showing 24 of 124,756Categories: Data & Insights, Data, Creative, Writing & Content, General, Cursor-rules
Data
PromptBeginner5 minmarkdown
- Predictive creative mechanics the data hints at (e.g.
a mechanic that lifts CTR on Google but hasn't been tested on ALN's playable format)
0
General
PromptBeginner5 minmarkdown
- Format-specific opportunities (e.g.
an endcard mechanic untested on ALN
0
Data
PromptBeginner5 minmarkdown
Use ML-pattern inference across all four network datasets to suggest what themes
angles
0
Creative
PromptBeginner5 minmarkdown
**Promising Creative(s):** Identify early positive signals and specify which variations — pacing edits
hook recuts
0
General
PromptBeginner5 minmarkdown
- Which are candidates for format adaptation (e.g.
recut for Google's asset ingestion
0
Creative
PromptBeginner5 minmarkdown
- Rate divergence risk: High / Medium / Low — i.e.
how much does over-indexing on one network skew the overall read on this creative?
0
Data
PromptBeginner5 minmarkdown
One concise pattern extracted strictly from this network's data — e.g.
On ALN
0
General
PromptBeginner5 minmarkdown
- State the performance delta (e.g.
top 1 on ALN
0
General
PromptBeginner5 minmarkdown
- Lowest IPM (or weakest CTR × CVR): Identify root-cause patterns through the lens of this network's audience and format behavior (e.g.
weak hook on a skip-heavy rewarded placement
0
Creative
PromptBeginner5 minmarkdown
- Top Creative by IPM (or CTR × CVR for Google): Interpret why this creative wins on this specific network. Reference network auction behavior
format fit
0
General
PromptBeginner5 minmarkdown
Repeat the following block for each of the four networks: AppLovin
Mintegral
0
Creative
PromptBeginner5 minmarkdown
- Identify cross-network divergence: creatives that overperform on one network and underperform on another
and reason about why
0
General
PromptBeginner5 minmarkdown
Your role is not to describe numbers
but to act as a performance-prediction model using structured
0
General
PromptBeginner5 minmarkdown
- Flag anomalies with ML-style reasoning (outliers
variance spikes
0
Creative
PromptBeginner5 minmarkdown
- Identify predictive signals per network (e.g.
which creative traits show scaling potential vs. burnout risk on ALN; which show stability signals on Mintegral)
0
General
PromptBeginner5 minmarkdown
- Detect hidden drivers of performance (e.g.
early CTR → later IPM quality drop
0
Creative
PromptBeginner5 minmarkdown
- Compare creatives directly across all key metrics
within and across networks
0
Data
PromptBeginner5 minmarkdown
- Interpret the data using pattern-recognition logic
segmented by network
0
Data
PromptBeginner5 minmarkdown
Analyse the provided UA performance data (text
table
0
Creative
PromptBeginner5 minmarkdown
- Mintegral: SDK-based
rewarded and interstitial heavy. Audience quality can vary significantly by geo and supply path. CPI tends to be volatile early; stabilizes at scale. Creative fatigue patterns differ from ALN — longer...
0
Creative
PromptBeginner5 minmarkdown
Before scoring any creative
ground your reasoning in each network's structural behavior:
0
Data
PromptBeginner5 minmarkdown
You think like a UA analyst and like a model trained to detect patterns in noisy data. You understand that each network has a distinct auction mechanic
creative format bias
0
General
PromptBeginner5 minmarkdown
You identify correlations
leading indicators
0