Prompt Engineer

Prompt Engineer is an agent for coding agents from github/awesome-copilot. It costs 71 tokens per session (1,188 once invoked), scanned A, original, MIT.

A chat mode that treats every user message as a prompt to improve rather than as a task to complete. It analyzes the original instructions and returns a revised system prompt for another AI model.

In plain words
What is it for?
Use it to review prompts systematically and produce a more structured version with clearer guidance for the target AI.
Why use it?
It helps expose ambiguity, missing context, conflicting instructions, and unclear goals before a prompt is used.

Agent

About the project

Awesome GitHub Copilot is a community collection of custom agents, instructions, skills, hooks, workflows, plugins, and configuration for GitHub Copilot. It helps Copilot users customize coding and development tasks. Catalogue entries are individual Copilot add-ons from this collection.

github/awesome-copilot · 38,651 stars · on GitHub

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add agents/github/awesome-copilot/prompt-engineer
Clone the repo
git clone --depth 1 https://github.com/github/awesome-copilot

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for Prompt Engineer

README.md
[![agentmods](https://agentmods.dev/badge/agents/github/awesome-copilot/prompt-engineer.svg)](https://agentmods.dev/agents/github/awesome-copilot/prompt-engineer)
Your own site
<a href="https://agentmods.dev/agents/github/awesome-copilot/prompt-engineer"><img src="https://agentmods.dev/badge/agents/github/awesome-copilot/prompt-engineer.svg" alt="Measured on agentmods" height="20"></a>
Per session 71 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,188 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00071 $0.01188
Opus 5 $0.00036 $0.00594
Sonnet 5 $0.00014 $0.00238
Haiku 4.5 $0.00007 $0.00119

Measured yesterday against content hash 8894d62bf78e, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

Prompt Engineer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

Copies of this mod

3 near-identical copies found in the catalogue:

agents/prompt-engineer.agent.md · 74 lines

How it starts

The opening of the file, as written. The whole thing — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Prompt Engineer

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)
  • Prioritization: (list) what 1-3 categories are the MOST important to address.
  • Conclusion: (max 30 words) given the previous assessment, give a very concise, imperative description of what should be changed and how. this does not have to adhere strictly to only the categories listed

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.)
    • For tasks outputting well-defined or structured data (classification, JSON, etc.) bias toward outputting a JSON.
    • JSON should never be wrapped in code blocks (```) unless explicitly requested.

Read the full file on GitHub · 74 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. yesterday First seen · 74 lines · 71 tokens per session scan A 8894d62bf78e

Subscribe to this mod's changes

Prompt Engineer is an agent published in the GitHub repository github/awesome-copilot (38,651 stars, last pushed today), licensed MIT. It adds 71 tokens to every session and 1,188 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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