Open-source LLM eval and red teaming: test prompts, compare models, catch jailbreaks and data leaks locally and in CI.
Results for “prompt-engineering”
12 tools found
Collaboration platform for AI teams to manage, test and monitor the prompts that power their LLM applications.
A dashboard that tells you whether your AI feature is actually getting worse or better over time, built on top of a popular open-source testing library instead of asking you to guess from user complaints.
A self-learning plugin for Claude Code that captures your corrections and repeated workflow patterns, then syncs them into CLAUDE.md and config files so the agent remembers them across sessions.
A memory system for AI coding agents that remembers past decisions, fixes, and dead ends, and can sync that memory across different tools and devices.
A pack of 13 ready-made AI skills that plug into Claude Code, Cursor or Codex so solo founders can also get design, marketing and product work done, not just code.
A dashboard for teams building products on top of ChatGPT-like AI models, so they can test whether a change to their prompt actually made answers better or worse, and watch what the AI is doing once it's live.
A free, local-first memory layer that shares your context and preferences across multiple AI coding assistants like Cursor, Claude Code, and Codex, so you stop repeating yourself.
A proxy that sits between your app and the LLM API, blocking prompt-injection attacks and stripping leaked secrets before they reach the model — with an audit trail for every request.
A reference library of research-backed UX rules you can paste straight into Claude, Cursor, or V0 as a prompt — so the interface an AI generates for you follows real cognitive-science principles instead of just looking plausible.
A free open-source tool that turns any MCP server, OpenAPI spec, or GraphQL endpoint into a plain command-line tool, so an AI coding agent can call it using far fewer tokens than the usual way.
Open-source projects tagged prompt-engineering on GitHub