custom_github_copilot_agent_builder: Agent for Claude Code

.github/agents/prompt-engineer.agent.md

prompt-engineer is an agent for Claude Code from dhar174/custom_github_copilot_agent_builder. It costs 68 tokens per session (1,209 once invoked), scanned A, a copy of Prompt Engineer, MIT.

A chat mode that treats every user message as a prompt to analyze and improve rather than as a task to complete directly.

In plain words
What is it for?
Use it to review prompt structure, assess clarity and examples, identify reasoning requirements, and produce a revised system prompt.
Why use it?
It helps identify weaknesses in a prompt and rewrite it into clearer instructions for another AI system.

Agent for Claude Code

Written for Claude Code: a Claude Code subagent (agents/*.md).

This is dhar174/custom_github_copilot_agent_builder's own configuration. It tells Claude Code how to work on custom_github_copilot_agent_builder itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything custom_github_copilot_agent_builder configures →

Reuse

Borrowing it

Nothing to install: this file belongs to dhar174/custom_github_copilot_agent_builder. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/dhar174/custom_github_copilot_agent_builder/main/.github/agents/prompt-engineer.agent.md
Clone the repo
git clone --depth 1 https://github.com/dhar174/custom_github_copilot_agent_builder

Made for: Claude Code.

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/dhar174/custom_github_copilot_agent_builder/prompt-engineer/github.svg)](https://agentmods.dev/agents/dhar174/custom_github_copilot_agent_builder/prompt-engineer)
Your own site
<a href="https://agentmods.dev/agents/dhar174/custom_github_copilot_agent_builder/prompt-engineer"><img src="https://agentmods.dev/badge/agents/dhar174/custom_github_copilot_agent_builder/prompt-engineer/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for prompt-engineer

Your own site · 80×15
<a href="https://agentmods.dev/agents/dhar174/custom_github_copilot_agent_builder/prompt-engineer"><img src="https://agentmods.dev/badge/agents/dhar174/custom_github_copilot_agent_builder/prompt-engineer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 68 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,209 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 94% copy Near-identical to another mod 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 5.1 $0.00068 $0.01209
Opus 5.5 $0.00027 $0.00484
Sonnet 5.5 $0.00014 $0.00242
Haiku 4.5 $0.00007 $0.00121

Measured 1mo ago against content hash 0449570a9d3d, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, 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 1mo ago.

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

This is a copy

94% identical to Prompt Engineer — 14 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

.github/agents/prompt-engineer.agent.md · 84 lines

How it starts

The opening of the file, as written. The whole thing — 84 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 · 84 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. 1mo ago First seen · 84 lines · 68 tokens per session scan A 0449570a9d3d

Subscribe to this mod's changes

prompt-engineer is an agent published in the GitHub repository dhar174/custom_github_copilot_agent_builder (7 stars, last pushed 8mo ago), licensed MIT. It adds 68 tokens to every session and 1,209 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 94% identical to Prompt Engineer, differing in 14 lines, and is treated as a copy.

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