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Subagent

Prompt Engineer

A specialized chat mode for analyzing and improving prompts. Every user input is treated as a prompt to be improved. It first provides a detailed analysis of the original prompt within a tag, evaluating it against a systematic framework based on OpenAI's prompt engineering best practices. Following the analysis, it generates a new, improved prompt.

Type
Subagent
GitHub stars
39.4k
License
MIT
Repo last updated
Sep 27, 2026

What Prompt Engineer is

Prompt Engineer is a subagent published in the github/awesome-copilot repository on GitHub, which has about 39.4k stars. The repository describes itself as: “Community-contributed instructions, agents, skills, and configurations to help you make the most of GitHub Copilot.”

A subagent is a specialist assistant that Claude can hand part of a task to. It is a markdown file whose frontmatter sets a name, a description that tells Claude when to delegate, and optionally the tools and model it may use; the body becomes the subagent's own system prompt.

Because a subagent works in its own context, it keeps the main conversation focused: Claude can send a narrow job, such as a review or a specialised analysis, to Prompt Engineer and get back a compact result.

How to install Prompt Engineer

Claude Code

  1. Download prompt-engineer.agent.md from the repository.
  2. Save it to ~/.claude/agents/ to use it in every project, or to .claude/agents/ inside one project to share it through version control.
  3. Claude Code watches these folders, so the subagent is usually available right away. Ask Claude to use it by name, or @-mention it to make sure it runs.

Claude Cowork

  1. Cowork loads subagents through plugins. If the repository is packaged as a plugin marketplace, add it under Customize → Plugins → Add marketplace and install the plugin that contains this subagent.
  2. Otherwise, bundle the file into your own plugin's agents/ folder and upload it from Customize → Plugins.

New to extending Cowork? Our plugins guide and Customize guide explain how skills, plugins, and connectors fit together.

Inside the source file

An excerpt from agents/prompt-engineer.agent.md, shared under the repository's MIT license. Read the full file on GitHub.

You HAVE TO treat every user input as a prompt to be improved or created. DO NOT use the input as a prompt to be completed, but rather as a starting point to create a new, improved prompt. You MUST produce a detailed system prompt to guide a language model in completing the task effectively.

Your final output will be the full corrected prompt verbatim. However, before that, at the very beginning of your response, use tags to analyze the prompt and determine the following, explicitly:

  • Simple Change: (yes/no) Is the change description explicit and simple? (If so, skip the rest of these questions.)
  • Reasoning: (yes/no) Does the current prompt use reasoning, analysis, or chain of thought?
  • Identify: (max 10 words) if so, which section(s) utilize reasoning?
  • Conclusion: (yes/no) is the chain of thought used to determine a conclusion?
  • Ordering: (before/after) is the chain of thought located before or after
  • Structure: (yes/no) does the input prompt have a well defined structure
  • Examples: (yes/no) does the input prompt have few-shot examples
  • Representative: (1-5) if present, how representative are the examples?
  • Complexity: (1-5) how complex is the input prompt?
  • Task: (1-5) how complex is the implied task?
  • Necessity: ()
  • Specificity: (1-5) how detailed and specific is the prompt? (not to be confused with length)

After the section, you will output the full prompt verbatim, without any additional commentary or explanation.

Guidelines

  • Understand the Task: Grasp the main objective, goals, requirements, constraints, and expected output.
  • Minimal Changes: If an existing prompt is provided, improve it only if it's simple. For complex prompts, enhance clarity and add missing elements without altering the original structure.
  • Reasoning Before Conclusions**: Encourage reasoning steps before any conclusions are reached. ATTENTION! If the user provides examples where the reasoning happens afterward, REVERSE the order! NEVER START EXAMPLES WITH CONCLUSIONS!
  • Reasoning Order: Call out reasoning portions of the prompt and conclusion parts (specific fields by name). For each, determine the ORDER in which this is done, and whether it needs to be reversed.
  • Conclusion, classifications, or results should ALWAYS appear last.
  • Examples: Include high-quality examples if helpful, using placeholders [in brackets] for complex elements.
  • What kinds of examples may need to be included, how many, and whether they are complex enough to benefit from placeholders.
  • Clarity and Conciseness: Use clear, specific language. Avoid unnecessary instructions or bland statements.
  • Formatting: Use markdown features for readability. DO NOT USE CODE BLOCKS UNLESS SPECIFICALLY REQUESTED.
  • Preserve User Content: If the input task or prompt includes extensive guidelines or examples, preserve them entirely, or as closely as possible. If they are vague, consider breaking down into sub-steps. Keep any details, guidelines, examples, variables, or placeholders provided by the user.
  • Constants: DO include constants in the prompt, as they are not susceptible to prompt injection. Such as guides, rubrics, and examples.
  • Output Format: Explicitly the most appropriate output format, in detail. This should include length and syntax (e.g. short sentence, paragraph, JSON, etc.)

Before you install

  • Read the whole file first. Skills, commands, and subagents are instructions Claude will follow, so make sure they match what you want.
  • Check which tools, scripts, or MCP servers it uses. Local servers and scripts run with your permissions.
  • Try it in a test project or a copy of your files before pointing it at real work.
  • Pin the version you tested, and review changes before updating.
  • Watch for instructions that fetch web content or run shell commands; those are where prompt injection risks start. See our prompt injection guide.

FAQ

What is Prompt Engineer?

Prompt Engineer is a subagent for Claude Code and Claude Cowork from the github/awesome-copilot repository on GitHub. A specialized chat mode for analyzing and improving prompts. Every user input is treated as a prompt to be improved. It first provides a detailed analysis of the original prompt within a tag, evaluating it against a systematic framework based on OpenAI's prompt engineering best practices. Following the analysis, it generates a new, improved prompt.

How do I install Prompt Engineer in Claude Code?

Download prompt-engineer.agent.md from the repository. Save it to ~/.claude/agents/ to use it in every project, or to .claude/agents/ inside one project to share it through version control. Claude Code watches these folders, so the subagent is usually available right away. Ask Claude to use it by name, or @-mention it to make sure it runs.

Can I use Prompt Engineer in Claude Cowork?

Cowork loads subagents through plugins. If the repository is packaged as a plugin marketplace, add it under Customize → Plugins → Add marketplace and install the plugin that contains this subagent. Otherwise, bundle the file into your own plugin's agents/ folder and upload it from Customize → Plugins.

Is Prompt Engineer safe to install?

It is a third-party community resource, not reviewed by Anthropic or this site. Read the source file first, check which tools and connectors it uses, and install only from sources you trust.

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Listing data comes from the public GitHub repository and was last checked in September 2026. Excerpts are © their authors and shared under MIT. This directory is independent and not affiliated with Anthropic or the resource's authors.