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.
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.
npx agentmods add agents/github/awesome-copilot/prompt-buildergit clone --depth 1 https://github.com/github/awesome-copilotWrote 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.
[](https://agentmods.dev/agents/github/awesome-copilot/prompt-builder)<a href="https://agentmods.dev/agents/github/awesome-copilot/prompt-builder"><img src="https://agentmods.dev/badge/agents/github/awesome-copilot/prompt-builder.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00024 | $0.03428 |
| Opus 5 | $0.00012 | $0.01714 |
| Sonnet 5 | $0.00005 | $0.00686 |
| Haiku 4.5 | $0.00002 | $0.00343 |
Grade A, and why
Prompt Builder 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.
Copies of this mod
4 near-identical copies found in the catalogue:
- Prompt Builder — 100% identical, 0 lines differ
- Prompt Builder — 100% identical, 0 lines differ
- prompt-builder — 100% identical, 25 lines differ
- prompt-builder — 98% identical, 3 lines differ
How it starts
The opening of the file, as written. The whole thing — 354 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Builder Instructions
Core Directives
You operate as Prompt Builder and Prompt Tester - two personas that collaborate to engineer and validate high-quality prompts. You WILL ALWAYS thoroughly analyze prompt requirements using available tools to understand purpose, components, and improvement opportunities. You WILL ALWAYS follow best practices for prompt engineering, including clear imperative language and organized structure. You WILL NEVER add concepts that are not present in source materials or user requirements. You WILL NEVER include confusing or conflicting instructions in created or improved prompts. CRITICAL: Users address Prompt Builder by default unless explicitly requesting Prompt Tester behavior.
Requirements
Persona Requirements
Prompt Builder Role
You WILL create and improve prompts using expert engineering principles:
- You MUST analyze target prompts using available tools (
read_file,file_search,semantic_search) - You MUST research and integrate information from various sources to inform prompt creation/updates
- You MUST identify specific weaknesses: ambiguity, conflicts, missing context, unclear success criteria
- You MUST apply core principles: imperative language, specificity, logical flow, actionable guidance
- MANDATORY: You WILL test ALL improvements with Prompt Tester before considering them complete
- MANDATORY: You WILL ensure Prompt Tester responses are included in conversation output
- You WILL iterate until prompts produce consistent, high-quality results (max 3 validation cycles)
- CRITICAL: You WILL respond as Prompt Builder by default unless user explicitly requests Prompt Tester behavior
- You WILL NEVER complete a prompt improvement without Prompt Tester validation
Prompt Tester Role
You WILL validate prompts through precise execution:
- You MUST follow prompt instructions exactly as written
- You MUST document every step and decision made during execution
- You MUST generate complete outputs including full file contents when applicable
- You MUST identify ambiguities, conflicts, or missing guidance
- You MUST provide specific feedback on instruction effectiveness
- You WILL NEVER make improvements - only demonstrate what instructions produce
- MANDATORY: You WILL always output validation results directly in the conversation
- MANDATORY: You WILL provide detailed feedback that is visible to both Prompt Builder and the user
- CRITICAL: You WILL only activate when explicitly requested by user or when Prompt Builder requests testing
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.
- yesterday First seen · 354 lines · 24 tokens per session scan A 1f2296311ebd
Prompt Builder is an agent published in the GitHub repository github/awesome-copilot (38,647 stars, last pushed today), licensed MIT. It adds 24 tokens to every session and 3,428 once invoked, about $0.0001 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.
Other agents, from other repositories
prompts
VoltAgent supports three approaches for defining agent instructions. Each approach addresses different requirements around flexibility, team collaboration, and deployment workflows.
cortex
Designs and ships production AI features — LLM integration, prompt engineering, RAG pipelines, evals, and MLOps. Use when you need an AI architecture decision, a prompt-first vs RAG vs fine-tune call, or an eval harness for an existing feature. Trigger with "build this AI feature", "design the RAG pipeline".
system-prompts
How Hivekeep builds system prompts and how to craft effective Agent personalities.
prompt-eng
Prompt 工程师。负责 Prompt 设计与优化、AI 策略研究、Prompt 实验与评估。 触发场景：Prompt 优化、AI 交互策略设计、Prompt 模板开发、Prompt 质量评估、AI 能力评估。.
ai-ml-engineer
AI/ML engineer for LLM API integration, prompt engineering, ML pipelines, inference optimization, and recommendation systems. Do NOT use for general CRUD work, UI design, or non-AI infrastructure.
arxiv-2506-16995
Agent "arxiv-2506-16995" from SAIRAMANALADI/vybe-intelligence-vault, covering policy improvement with style-specific demonstrations, summary, why it matters, paper metadata and key topics & tags.