Data
PromptBeginner5 minmarkdownQuality: 28
You are a forensic AI-text analyst specialized in spotting lazy or default LLM outputs from 2023–2026 models (ChatGPT
Claude
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Explore
Find agent skills by outcome
8,080 skills indexed with the new KISS metadata standard.
Showing 24 of 8,080Categories: Education, Data
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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Education
PromptBeginner5 minmarkdownQuality: 28
- For each suggestion or brainstormed idea
briefly explain your reasoning or rationale behind it.
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Education
PromptBeginner5 minmarkdownQuality: 28
**[Translated Instruction in ${language}]:** [Provide a sentence in ${language} explaining that the user should say Next or its equivalent in ${language} (e.g.
devam for Turkish
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Education
PromptBeginner5 minmarkdownQuality: 28
Oxford 3000: Step-by-Step Vocabulary Coach
I want you to act as an English Language Tutor. Your task is to teach me the Oxford 3000 word list step-by-step in alphabetical order.
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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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Data
PromptBeginner5 minmarkdownQuality: 24
- Never flatten cross-network data into a single average — divergence is signal
not noise.
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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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Education
PromptBeginner5 minmarkdownQuality: 28
**Worst Creative(s):** Explain which patterns predict failure
and flag whether the failure is universal or network-localized.
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Education
PromptBeginner5 minmarkdownQuality: 28
**Best Creative(s):** Explain which creative attributes correlate with strong metrics
and whether those attributes hold across all networks or are network-specific.
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Education
PromptBeginner5 minmarkdownQuality: 24
- Highest CPI: Explain which signals
specific to this network
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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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Education
PromptBeginner5 minmarkdownQuality: 28
- High Spend / Poor Results: Explain the inefficiency pattern and the likely network-specific ML reason (e.g.
ALN AXON fallback behavior
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Education
PromptBeginner5 minmarkdownQuality: 28
- Top Creative by Spend: Explain why this network's algo is favoring it
and whether scaling is amplifying or compressing efficiency.
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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
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Education
PromptBeginner5 minmarkdownQuality: 24
- Google UAC (ACi): Machine-learning-first
multi-format ingestion (YouTube
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Education
PromptBeginner5 minmarkdownQuality: 28
- AppLovin (ALN): Operates on a closed DSP with a proprietary ML bidding stack (AXON). Heavy on playable and interactive end-cards. IPM is the primary optimization signal; CTR is secondary. Algo learns fast but punishes creative fatigue aggressively. Look for: steep IPM decay curves
install clustering by creative batch
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Data
PromptBeginner5 minmarkdownQuality: 28
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
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Data
PromptBeginner5 minmarkdownQuality: 24
User Acquisition Data Analysis
Persona
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Data
PromptBeginner5 minmarkdownQuality: 24
- Failure scenarios (invalid input
missing data
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Data
PromptBeginner5 minmarkdownQuality: 26
- 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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Data
PromptBeginner5 minmarkdownQuality: 22
- Never flatten cross-network data into a single average — divergence is signal
not noise.
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