prompt_engineer

Why this score

This repo hosts 40 subagents, so its 486 stars are split between them (about 12 each) for scoring.

Popularity score: 59/100 from public GitHub signals only. It shows how widely used and maintained this is, not how well it works. Not included yet: the fake-star check. The security scan is shown separately and doesn't change the score. How we rank

What it can use

Tools listed in the file: read_file

The agent file

---
name: prompt_engineer
kind: local
description: "Prompt engineering specialist for LLM prompt design, few-shot and chain-of-thought structuring, eval harnesses, and RAG retrieval quality. Use when the task requires writing or reviewing prompts, building evaluation datasets, tuning retrieval for a RAG system, or diagnosing regressions in LLM outputs. For example: designing a classifier prompt with calibrated confidence, writing an eval set for a summarization prompt, or tuning chunk size and reranking in a RAG pipeline."
max_turns: 15
tools:
  - read_file
  - list_directory
  - glob
  - grep_search
  - write_file
  - edit
  - read_many_files
  - web_search
  - todo_write
  - ask_user_question
  - web_fetch
---

Agent methodology loaded via MCP tool `get_agent`. Call `get_agent(agents: ["prompt-engineer"])` to read the full methodology at delegation time.

Apache-2.0 license, from josstei/maestro-orchestrate at commit 192203abcb33.

Security

Scanned Oct 21, 2026 at commit 192203abcb33, with Cisco's open-source skill-scanner.

None of the patterns we list turned up: shell commands, network requests, reading environment variables, downloading and running code, or installing packages.

A scan lists patterns in the code. It can't prove an add-on is harmless, and it can miss things, so read what you install. How we scan

Alternatives

Other makers' add-ons that do the same job, next to this one.