Improve prompt quality by applying optimal engineering techniques like chain-of-thought reasoning based on intent analysis.
MIT
112,329 tools. Last updated 2026-04-20 03:14
- Track prompt performance feedback to improve reasoning framework selection over time. Submit task IDs and execution results for optimization.MIT
- Improve existing prompts by applying targeted feedback to add missing context, clarify instructions, or adapt for different AI models while preserving original structure.
- Transform raw prompts into clearer, more detailed versions with better structure to improve LLM results. Enhances AI interactions by adding context and requirements.MIT
- Provides practical welding tips to improve bead quality and consistency for MIG, TIG, and Stick processes. Offers guidance on travel speed, angle, voltage settings, and proper technique for beginners and intermediate welders.
- Submit security bypass incidents to improve detection patterns for prompt injection, SQL injection, PII exposure, and file write vulnerabilities.Apache 2.0
Matching MCP Servers
- -securityFlicense-qualityTransforms prompts into Chain of Draft (CoD) or Chain of Thought (CoT) format to enhance LLM reasoning quality while reducing token usage by up to 92.4%, supporting multiple LLM providers including Claude, GPT, Ollama, and local models.Last updated1018
- -securityFlicense-qualityProvides AI-powered selection and generation of specialized system prompts from a database of over 66 templates for Claude Code. It uses semantic search to find the best matching template and can adapt it to fit specific user tasks and contexts.Last updated
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