Today's Prompt Engineering: Fastest-Growing Projects — October 18, 2026
This week, the Prompt Engineering space continues to evolve with a growing focus on leveraging large language models (LLMs) for decision-making, intent routing, and structured workflows in various domains such as bug bounty hunting and video production. One standout project is AnyJev from Nokia Applied Research, which transforms LLMs into Jev-style decision models that provide typed decisions and real probabilities without the need for training.
nokia-applied-research/AnyJev: This repository turns any LLM into a structured decision-making model, offering typed decisions and real probabilities. With its impressive growth score of 40.80 and over 1,387 stars, AnyJev is gaining significant traction among developers looking to enhance their decision-making processes with minimal training overhead.
angel291592/Intent-Router: Intent-Router helps convert vague user requests into structured intent contracts that machines can understand and act upon. The project's high growth score of 31.52 and substantial engagement (763 stars) highlight its importance in improving the clarity and effectiveness of human-machine interactions.
mdpsec/bug-bounty-hunting-prompts: This repository offers reusable prompts for a bug bounty hunting workflow that emphasizes structured evidence collection. With 28.22 growth points and 620 stars, it is evident that security professionals find these prompts valuable in their efforts to identify and report vulnerabilities efficiently.
wousp112/justin-sun-alan-shao-ai-notes: This project provides a comprehensive set of AI planning prompts based on a conversation between Justin Sun and Alan Shao. Despite fewer stars (152) compared to others, its 14.78 growth score indicates growing interest in leveraging such conversations for structured AI task planning.
Abdu213/yaml-wit-studio: Aimed at speeding up chatbot dataset creation through YAML-based training, this toolkit garners attention with a notable 12.09 growth score and 53 stars. Developers appreciate its potential to streamline the process of building conversational AI systems.
Rylaispirit/cinematic-video-prompt-skill: This resource offers an extensive cheat sheet for creating cinematic video prompts, covering topics like camera angles, lighting, and color grading across multiple platforms. With a steady growth score of 7.36 and 151 stars, it is clear that creators are finding value in these detailed instructions to elevate their visual content.
e2sy/jailbreak-archives: Designed for AI safety research and defensive hardening against adversarial prompt engineering techniques, this structured archive has a modest but steady growth score of 5.33 and 42 stars. Its importance lies in its role as a resource for understanding and mitigating risks associated with sophisticated attacks on LLMs.
menonpg/ste-prompt: This project integrates Simplified Technical English (STE100) into Claude Code, Codex, and other coding agents to ensure clearer and more unambiguous outputs. With 4.80 growth points and 39 stars, it reflects a niche but growing interest in technical communication standards for AI-generated content.
tngklp/ComfyUI-Prompt-Studio: Offering a multimodal prompt studio for refining image, video, and audio prompts using both local LLMs and API-based solutions, this tool has garnered 4.19 growth points and 40 stars. Its utility in enhancing creative workflows makes it an interesting option for artists and developers alike.
gilsonnogueira/notebooklm-para-concursos: This ecosystem integrates NotebookLM with Gemini Estudantes to provide a methodical approach (N.A.G.) for studying with AI, including calibrated prompts and statistical analysis of exam boards. With 3.43 growth points and 41 stars, it demonstrates growing interest in leveraging AI for structured learning and preparation.
These projects showcase the diverse applications and evolving capabilities of prompt engineering across various domains, from decision-making to creative content generation, highlighting its importance in enhancing human-AI interactions and efficiency.
nokia-applied-research/AnyJev: This repository turns any LLM into a structured decision-making model, offering typed decisions and real probabilities. With its impressive growth score of 40.80 and over 1,387 stars, AnyJev is gaining significant traction among developers looking to enhance their decision-making processes with minimal training overhead.
angel291592/Intent-Router: Intent-Router helps convert vague user requests into structured intent contracts that machines can understand and act upon. The project's high growth score of 31.52 and substantial engagement (763 stars) highlight its importance in improving the clarity and effectiveness of human-machine interactions.
mdpsec/bug-bounty-hunting-prompts: This repository offers reusable prompts for a bug bounty hunting workflow that emphasizes structured evidence collection. With 28.22 growth points and 620 stars, it is evident that security professionals find these prompts valuable in their efforts to identify and report vulnerabilities efficiently.
wousp112/justin-sun-alan-shao-ai-notes: This project provides a comprehensive set of AI planning prompts based on a conversation between Justin Sun and Alan Shao. Despite fewer stars (152) compared to others, its 14.78 growth score indicates growing interest in leveraging such conversations for structured AI task planning.
Abdu213/yaml-wit-studio: Aimed at speeding up chatbot dataset creation through YAML-based training, this toolkit garners attention with a notable 12.09 growth score and 53 stars. Developers appreciate its potential to streamline the process of building conversational AI systems.
Rylaispirit/cinematic-video-prompt-skill: This resource offers an extensive cheat sheet for creating cinematic video prompts, covering topics like camera angles, lighting, and color grading across multiple platforms. With a steady growth score of 7.36 and 151 stars, it is clear that creators are finding value in these detailed instructions to elevate their visual content.
e2sy/jailbreak-archives: Designed for AI safety research and defensive hardening against adversarial prompt engineering techniques, this structured archive has a modest but steady growth score of 5.33 and 42 stars. Its importance lies in its role as a resource for understanding and mitigating risks associated with sophisticated attacks on LLMs.
menonpg/ste-prompt: This project integrates Simplified Technical English (STE100) into Claude Code, Codex, and other coding agents to ensure clearer and more unambiguous outputs. With 4.80 growth points and 39 stars, it reflects a niche but growing interest in technical communication standards for AI-generated content.
tngklp/ComfyUI-Prompt-Studio: Offering a multimodal prompt studio for refining image, video, and audio prompts using both local LLMs and API-based solutions, this tool has garnered 4.19 growth points and 40 stars. Its utility in enhancing creative workflows makes it an interesting option for artists and developers alike.
gilsonnogueira/notebooklm-para-concursos: This ecosystem integrates NotebookLM with Gemini Estudantes to provide a methodical approach (N.A.G.) for studying with AI, including calibrated prompts and statistical analysis of exam boards. With 3.43 growth points and 41 stars, it demonstrates growing interest in leveraging AI for structured learning and preparation.
These projects showcase the diverse applications and evolving capabilities of prompt engineering across various domains, from decision-making to creative content generation, highlighting its importance in enhancing human-AI interactions and efficiency.