General
PromptBeginner5 minmarkdown
· Capture key constraints (budget
seating
0
Explore
Find agent skills by outcome
126,310 skills indexed with the new KISS metadata standard.
Showing 24 of 126,310Categories: Cursor-rules, Writing & Content, General, Research & Learning, Data, Coding & Debugging
Data
PromptBeginner5 minmarkdown
- Flag when data volume per network is insufficient to draw high-confidence conclusions
and adjust confidence language accordingly.,FALSE,TEXT,[email protected]
0
General
PromptBeginner5 minmarkdown
Car Buying Intake Interview
# ==========================================================
0
General
PromptBeginner5 minmarkdown
- When helpful
use ML language (correlation
0
General
PromptBeginner5 minmarkdown
- Isolate anomalies and outliers confidently
and attribute them to network mechanics where causally plausible.
0
General
PromptBeginner5 minmarkdown
- Keep the tone concise
analytical
0
General
PromptBeginner5 minmarkdown
- Highlight early signals the model would treat as predictors per network (CTR → IPM deterioration on ALN
CPI drift patterns on Mintegral
0
Data
PromptBeginner5 minmarkdown
- Never flatten cross-network data into a single average — divergence is signal
not noise.
0
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
General
PromptBeginner5 minmarkdown
- Which are candidates for format adaptation (e.g.
recut for Google's asset ingestion
0
Research & Learning
PromptBeginner5 minmarkdown
- Provide a hypothesis grounded in network mechanics (format fit mismatch
audience signal difference
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
General
PromptBeginner5 minmarkdown
Repeat the following block for each of the four networks: AppLovin
Mintegral
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
General
PromptBeginner5 minmarkdown
- Detect hidden drivers of performance (e.g.
early CTR → later IPM quality drop
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
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