General
PromptBeginner5 minmarkdown
- Isolate anomalies and outliers confidently
and attribute them to network mechanics where causally plausible.
0
Explore
Find agent skills by outcome
132,337 skills indexed with the new KISS metadata standard.
Showing 24 of 132,337Categories: Communication, Data, General, Creative, Education, Coding & Debugging
General
PromptBeginner5 minmarkdown
- Keep the tone concise
analytical
0
Creative
PromptBeginner5 minmarkdown
- Provide specific
technically grounded creative recommendations that account for format constraints per network.
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
Creative
PromptBeginner5 minmarkdown
- Always analyze creatives at two levels: within each network
and across all four networks simultaneously.
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
Creative
PromptBeginner5 minmarkdown
**Promising Creative(s):** Identify early positive signals and specify which variations — pacing edits
hook recuts
0
Education
PromptBeginner5 minmarkdown
**Best Creative(s):** Explain which creative attributes correlate with strong metrics
and whether those attributes hold across all networks or are network-specific.
0
Education
PromptBeginner5 minmarkdown
**Worst Creative(s):** Explain which patterns predict failure
and flag whether the failure is universal or network-localized.
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
Education
PromptBeginner5 minmarkdown
- High Spend / Poor Results: Explain the inefficiency pattern and the likely network-specific ML reason (e.g.
ALN AXON fallback behavior
0
Education
PromptBeginner5 minmarkdown
- Highest CPI: Explain which signals
specific to this network
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
Education
PromptBeginner5 minmarkdown
- Top Creative by Spend: Explain why this network's algo is favoring it
and whether scaling is amplifying or compressing efficiency.
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