Data
PromptBeginner5 minmarkdownQuality: 28
- The methodology mirrors approaches in recent literature
but potential differences in dataset preprocessing and parameter tuning may exist.
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Find agent skills by outcome
13,785 skills indexed with the new KISS metadata standard.
Showing 24 of 13,785Categories: Data, Creative
Creative
PromptBeginner5 minmarkdownQuality: 24
framing: the subject appears inside the laptop display
with the laptop bezel partially visible
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Creative
PromptBeginner5 minmarkdownQuality: 28
description: A young woman lying on a bed
holding a smartphone and looking at the screen with a calm
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Creative
PromptBeginner5 minmarkdownQuality: 24
avoid: over-processed skin
artificial lighting
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Creative
PromptBeginner5 minmarkdownQuality: 24
avoid: Artificial faces in background
over-processed skin
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Creative
PromptBeginner5 minmarkdownQuality: 24
compression: Minimal compression artifacts
close to original capture
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Creative
PromptBeginner5 minmarkdownQuality: 24
camera_behavior: Natural smartphone processing
slight edge sharpening
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Creative
PromptBeginner5 minmarkdownQuality: 24
- Keep design minimal
not decorative
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Creative
PromptBeginner5 minmarkdownQuality: 28
Vibe Coding with Commands and Skills
Act as a Vibe Coding Expert with built-in /commands and skills. You are proficient in leveraging AI models for coding and UX/UI design tasks, using a variety of tools and frameworks to streamline the...
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Data
PromptBeginner5 minmarkdownQuality: 28
- Flag when data volume per network is insufficient to draw high-confidence conclusions
and adjust confidence language accordingly.,FALSE,TEXT,[email protected]
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Creative
PromptBeginner5 minmarkdownQuality: 28
- Provide specific
technically grounded creative recommendations that account for format constraints per network.
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Data
PromptBeginner5 minmarkdownQuality: 24
- Never flatten cross-network data into a single average — divergence is signal
not noise.
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Creative
PromptBeginner5 minmarkdownQuality: 28
- Always analyze creatives at two levels: within each network
and across all four networks simultaneously.
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Data
PromptBeginner5 minmarkdownQuality: 28
- 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)
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Data
PromptBeginner5 minmarkdownQuality: 24
Use ML-pattern inference across all four network datasets to suggest what themes
angles
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Creative
PromptBeginner5 minmarkdownQuality: 28
**Promising Creative(s):** Identify early positive signals and specify which variations — pacing edits
hook recuts
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Creative
PromptBeginner5 minmarkdownQuality: 28
- Rate divergence risk: High / Medium / Low — i.e.
how much does over-indexing on one network skew the overall read on this creative?
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Data
PromptBeginner5 minmarkdownQuality: 24
One concise pattern extracted strictly from this network's data — e.g.
On ALN
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Creative
PromptBeginner5 minmarkdownQuality: 28
- Top Creative by IPM (or CTR × CVR for Google): Interpret why this creative wins on this specific network. Reference network auction behavior
format fit
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Creative
PromptBeginner5 minmarkdownQuality: 28
- Identify cross-network divergence: creatives that overperform on one network and underperform on another
and reason about why
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Creative
PromptBeginner5 minmarkdownQuality: 28
- Identify predictive signals per network (e.g.
which creative traits show scaling potential vs. burnout risk on ALN; which show stability signals on Mintegral)
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Creative
PromptBeginner5 minmarkdownQuality: 24
- Compare creatives directly across all key metrics
within and across networks
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Data
PromptBeginner5 minmarkdownQuality: 24
- Interpret the data using pattern-recognition logic
segmented by network
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Data
PromptBeginner5 minmarkdownQuality: 24
Analyse the provided UA performance data (text
table
1