General
PromptBeginner5 minmarkdown
### Version 1.5 (January 11
2026)
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Explore
Find agent skills by outcome
125,894 skills indexed with the new KISS metadata standard.
Showing 24 of 125,894Categories: Data, General, Coding & Debugging, Creative, Openclaw
General
PromptBeginner5 minmarkdown
- Recommendations for iteration (A/B testing
feedback loops)
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General
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- Clear
grounded
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Data
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- Suggested follow-up actions (prototype scope
data prep
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General
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- Key risks or dependencies to monitor (e.g.
bias checks)
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General
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- Keep it minimal and adaptable
including placeholders for iteration or error handling
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General
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- Lightweight/chat-only models (older 7B–13B models
mini variants) — usually lack depth/context/tool reliability
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Creative
PromptBeginner5 minmarkdown
- Generate a simple
practical starter prompt
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General
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- Instead
suggest non-AI or hybrid alternatives (e.g.
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Data
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- **Llama 4 / Llama 405B variants (Meta)** — Best open-source frontier performance
excellent for self-hosting
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General
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- **Mistral Large 2 / Devstral** — Very strong price/performance
fast
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General
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- **Gemini 2.5 Pro / Flash (Google)** — Outstanding multimodal (especially video/document understanding)
very large context windows
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General
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- **Claude 4 Opus/Sonnet (Anthropic)** — Exceptional long-form reasoning
writing quality
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General
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Current Best-in-Class Ranking (January 2026 – general guidance
always tailor to the process):
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General
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- Privacy
open behavior
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General
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- Tool use
function calling
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Data
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- **Grok 3 / Grok 4 (xAI)** — Excellent reasoning
real-time knowledge via X
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General
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- Call out misconceptions
dependencies
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General
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- Track KPIs (e.g.
20% time savings
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General
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If AI is viable
recommend which AI engines are best suited and why.
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Data
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| Data quality | 3 | Mostly clean
some inconsistency |
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General
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Summarize the overall feasibility score (weighted average)
then issue your verdict with clear reasoning.
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General
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**Interpretation:** Clear patterns exist
but context accuracy is critical. Recommend hybrid approach with AI drafts + human review.
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General
PromptBeginner5 minmarkdown
When scoring
evaluate these dimensions (suggested weights for averaging: e.g.
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