Today's Prompt Engineering: Fastest-Growing Projects — May 07, 2026
Today's Prompt Engineering, we're seeing a surge in tools that aim to refine and optimize large language model (LLM) outputs. The top-growing repositories are focused on providing concrete rules, templates, and workflows for creating sharper and more genre-aware text. From industrial-grade prompt engines to drop-in compression solutions, these tools are gaining traction among developers looking to improve their LLM apps.
Anbeeld's WRITING.md repository is growing rapidly with a growth score of 14.04 and 230 stars, as it provides a set of rules for creating sharper and more genre-aware text with built-in self-auditing workflows. By offering concrete anchors for LLM outputs, this tool is attracting developers who need to fine-tune their models for specific use cases.
Freestylefly's awesome-gpt-image-2 repository has an impressive 4,153 stars and a growth score of 11.32, thanks to its comprehensive prompt engine and template library for GPT-Image2. With over 370 case studies and 20+ industrial-grade templates, this tool is becoming the go-to resource for developers working with multimodal models.
Jia-gao's leanctx repository boasts a growth score of 10.47 and 32 stars, as it offers a drop-in prompt compression solution that can cut token bills by 40-60% without requiring code changes. By providing a Python SDK and support for LLMLingua-2, this tool is gaining popularity among developers looking to optimize their LLM apps.
Yaojingang's yao-open-prompts repository has a growth score of 9.77 and 294 stars, as it provides a comprehensive library of Chinese AI prompts covering various scenarios such as work, learning, content creation, marketing, and everyday life. By offering a curated collection of prompts, this tool is becoming a valuable resource for developers working on Chinese language models.
Shiphrahx's AI-for-engineering-leaders repository has a growth score of 8.69 and 46 stars, as it provides practical AI playbooks for engineering leaders to improve delivery, decision-making, and team productivity. By offering real workflows, prompts, and systems, this tool is attracting developers who need to integrate AI into their engineering workflows.
Chengzhongwei's Prompt-sensei repository boasts a growth score of 6.29 and 64 stars, as it offers a local-first prompt coach for Claude Code and Codex that improves prompts and analyzes local history with explicit consent. By providing a personalized coaching experience, this tool is gaining popularity among developers looking to refine their LLM outputs.
Bsquang's claude-comstyle repository has a growth score of 5.62 and 26 stars, as it provides 13 prompts to control how Claude communicates, allowing users to choose their preferred style and cut noise. By offering a simple yet effective solution for customizing model outputs, this tool is attracting developers who need more flexibility in their LLM apps.
ZeroLu's awesome-happy-horse repository has a growth score of 3.82 and 73 stars, as it provides a community-curated hub for Happy Horse model resources, including prompts, news, benchmarks, and sample outputs. By offering a one-stop shop for developers working with the Happy Horse model, this tool is gaining traction among multimodal AI enthusiasts.
Josh-blythe's bordair-multimodal repository has a growth score of 3.13 and 46 stars, as it provides an open-source cross-modal and multimodal prompt injection test suite with over 50,500 attack payloads across various modalities. By offering a comprehensive testing framework, this tool is attracting developers who need to ensure the security and robustness of their LLM apps.
Anbeeld's WRITING.md repository is growing rapidly with a growth score of 14.04 and 230 stars, as it provides a set of rules for creating sharper and more genre-aware text with built-in self-auditing workflows. By offering concrete anchors for LLM outputs, this tool is attracting developers who need to fine-tune their models for specific use cases.
Freestylefly's awesome-gpt-image-2 repository has an impressive 4,153 stars and a growth score of 11.32, thanks to its comprehensive prompt engine and template library for GPT-Image2. With over 370 case studies and 20+ industrial-grade templates, this tool is becoming the go-to resource for developers working with multimodal models.
Jia-gao's leanctx repository boasts a growth score of 10.47 and 32 stars, as it offers a drop-in prompt compression solution that can cut token bills by 40-60% without requiring code changes. By providing a Python SDK and support for LLMLingua-2, this tool is gaining popularity among developers looking to optimize their LLM apps.
Yaojingang's yao-open-prompts repository has a growth score of 9.77 and 294 stars, as it provides a comprehensive library of Chinese AI prompts covering various scenarios such as work, learning, content creation, marketing, and everyday life. By offering a curated collection of prompts, this tool is becoming a valuable resource for developers working on Chinese language models.
Shiphrahx's AI-for-engineering-leaders repository has a growth score of 8.69 and 46 stars, as it provides practical AI playbooks for engineering leaders to improve delivery, decision-making, and team productivity. By offering real workflows, prompts, and systems, this tool is attracting developers who need to integrate AI into their engineering workflows.
Chengzhongwei's Prompt-sensei repository boasts a growth score of 6.29 and 64 stars, as it offers a local-first prompt coach for Claude Code and Codex that improves prompts and analyzes local history with explicit consent. By providing a personalized coaching experience, this tool is gaining popularity among developers looking to refine their LLM outputs.
Bsquang's claude-comstyle repository has a growth score of 5.62 and 26 stars, as it provides 13 prompts to control how Claude communicates, allowing users to choose their preferred style and cut noise. By offering a simple yet effective solution for customizing model outputs, this tool is attracting developers who need more flexibility in their LLM apps.
ZeroLu's awesome-happy-horse repository has a growth score of 3.82 and 73 stars, as it provides a community-curated hub for Happy Horse model resources, including prompts, news, benchmarks, and sample outputs. By offering a one-stop shop for developers working with the Happy Horse model, this tool is gaining traction among multimodal AI enthusiasts.
Josh-blythe's bordair-multimodal repository has a growth score of 3.13 and 46 stars, as it provides an open-source cross-modal and multimodal prompt injection test suite with over 50,500 attack payloads across various modalities. By offering a comprehensive testing framework, this tool is attracting developers who need to ensure the security and robustness of their LLM apps.