# Tools

> Every listed resource, editors’ picks first. The filters below can be combined.

- Search: append `?q=<keywords>` to the URL. Separate several keywords with spaces; all of them must match. Matches name, repository, tags, professions, description and README.
- Filter by type: append `?type=<cli|skill|plugin|mcp>` to the URL.
- Filter by profession: append `?profession=<profession id>` to the URL. Separate several ids with commas; a resource matches when it suits any of them. Ids are listed at https://hebbmind.com/api/v1/professions
- Filter by tag: append `?tag=<tag>` to the URL. Separate several tags with commas; a resource must carry all of them. Tags are listed at https://hebbmind.com/api/v1/tags

Resources for DevOps / SRE or ML / AI or Data analysis, Tagged `prompts`: 8 items.

> - DevOps / SRE: Deployment, monitoring, cloud resources and reliability.
> - ML / AI: Model training, evaluation and LLM application development.
> - Data analysis: Querying, statistical analysis, reporting and visualization.

- [prompts.chat](https://hebbmind.com/plugin/prompts-chat.md): [Plugin] Official Claude Code plugin for the prompts.chat library: search prompts and Agent Skills from your coding assistant, fill in variables and improve prompts with AI. Includes commands, subagents and a remote MCP; saving your own prompts needs an API key.
- [LLM Application Dev](https://hebbmind.com/plugin/llm-application-dev.md): [Plugin] Plugin for building LLM apps: LangGraph agents with memory and tool use, hybrid-search RAG, vector index tuning, prompt optimization and LLM evaluation. Includes 3 subagents and 8 skills.
- [promptfoo](https://hebbmind.com/cli/promptfoo.md): [CLI] CLI and library for evaluating and red-teaming LLM apps: compare prompts and model outputs in bulk, scan for security vulnerabilities and plug into CI/CD, with evals running locally. Now part of OpenAI and still open source under MIT.
- [Agent Skills for Context Engineering](https://hebbmind.com/plugin/agent-skills-for-context-engineering.md): [Plugin] Context and harness engineering skills covering context compression and optimization, multi-agent architectures, memory systems, tool design and LLM-as-judge evaluation, activated automatically by task. Aimed at building production-grade agent systems.
- [Prompt Master](https://hebbmind.com/skill/prompt-master.md): [Skill] Writes precise prompts for a specific AI tool: identifies the target tool, extracts your intent, asks at most three questions if details are missing, then outputs a lean, paste-ready prompt. Covers dozens of tools including ChatGPT, Claude Code, Cursor, Midjourney and Sora.
- [LLM](https://hebbmind.com/cli/llm.md): [CLI] Simon Willison's LLM CLI and Python library: call OpenAI, Claude, Gemini and local models from the terminal, with conversations logged to SQLite automatically, plus embeddings, structured extraction, tool calling and a plugin system.
- [Arize Phoenix MCP Server](https://hebbmind.com/mcp/phoenix-mcp.md): [MCP] Connects to the Arize Phoenix observability platform so agents can query LLM traces and sessions, manage prompts, browse datasets and read experiment evaluation results. Maintained by Arize; needs a self-hosted or cloud Phoenix instance.
- [Opik MCP Server](https://hebbmind.com/mcp/opik-mcp.md): [MCP] Comet's official Opik MCP: query traces and spans of LLM apps in chat, log scores and comments, and manage datasets, experiments and prompt versions. Works with Opik Cloud (API key or browser sign-in) and self-hosted open-source Opik.