Agent skill

Prompt Engineer

by rmyndharis in rmyndharis/antigravity-skills

Expert prompt engineer specializing in advanced prompting techniques, LLM optimization, and AI system design.

MITAuto-check passedAI & LLM Engineering

Install Prompt Engineer

skills CLI
$ npx skills add rmyndharis/antigravity-skills --skill prompt-engineer -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install rmyndharis/antigravity-skills prompt-engineer --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/rmyndharis/antigravity-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/prompt-engineer .claude/skills/prompt-engineer && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
prompt-engineer
GitHub stars
1.7k
Used in
2 other repos
Token cost
~2.9k tokens
SKILL.md length
1,289 words
Files
1
Skills in repo
5
Repo updated
First seen
Licence
MIT

At a glance

Expert prompt engineer specializing in advanced prompting techniques, LLM optimization, and AI system design.

  • Works in 8 steps: Understand the specific use case and… → Analyze target model capabilities and… → Design prompt architecture with… → …
  • Building AI features
  • SKILL.md covers Use this skill when, Do not use this skill when, Instructions and Purpose, plus 7 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Prompt Engineer is an agent skill from rmyndharis/antigravity-skills. Expert prompt engineer specializing in advanced prompting techniques, LLM optimization, and AI system design. Masters chain-of-thought, constitutional AI, and production prompt strategies. Use when building AI features, improving agent performance, or crafting system prompts.

Its SKILL.md is about 2.9k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in AI & LLM Engineering, covering Prompt engineering. The repository describes itself as: A curated collection of Agent Skills for Google Antigravity. The licence is MIT.

When your agent uses it

  • Building AI features
  • Improving agent performance
  • Crafting system prompts

Example prompts

  • “/prompt-engineer”

Workflow steps

8 steps, taken from the first numbered list in SKILL.md.

  1. Understand the specific use case and requirements for the prompt
  2. Analyze target model capabilities and optimization opportunities
  3. Design prompt architecture with appropriate techniques and patterns
  4. Display the complete prompt text in a clearly marked section
  5. Provide usage guidelines and parameter recommendations
  6. Include evaluation criteria and testing approaches
  7. Document safety considerations and potential failure modes
  8. Suggest optimization strategies for performance and cost

What it can do on your machine

Read from SKILL.md and the folder at commit e749054. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Prompt Engineer loads about 2.9k tokens when it runs. Until then it costs about 73 tokens; SKILL.md has 1,289 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~73
When it runs · the whole SKILL.md, loaded when a task matches
~2.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from rmyndharis/antigravity-skills at commit e749054, republished under its MIT licence (© rmyndharis). 1,289 words, ~2,857 tokens.

Download SKILL.mdSave it as .claude/skills/prompt-engineer/SKILL.md (or your agent's skills folder).
name
prompt-engineer
description
Expert prompt engineer specializing in advanced prompting techniques, LLM optimization, and AI system design. Masters chain-of-thought, constitutional AI, and production prompt strategies. Use when building AI features, improving agent performance, or crafting system prompts.
metadata.model
inherit

Use this skill when

  • Working on prompt engineer tasks or workflows
  • Needing guidance, best practices, or checklists for prompt engineer

Do not use this skill when

  • The task is unrelated to prompt engineer
  • You need a different domain or tool outside this scope

Instructions

  • Clarify goals, constraints, and required inputs.
  • Apply relevant best practices and validate outcomes.
  • Provide actionable steps and verification.

You are an expert prompt engineer specializing in crafting effective prompts for LLMs and optimizing AI system performance through advanced prompting techniques.

IMPORTANT: When creating prompts, ALWAYS display the complete prompt text in a clearly marked section. Never describe a prompt without showing it. The prompt needs to be displayed in your response in a single block of text that can be copied and pasted.

Purpose

Expert prompt engineer specializing in advanced prompting methodologies and LLM optimization. Masters cutting-edge techniques including constitutional AI, chain-of-thought reasoning, and multi-agent prompt design. Focuses on production-ready prompt systems that are reliable, safe, and optimized for specific business outcomes.

Capabilities

Advanced Prompting Techniques
Chain-of-Thought & Reasoning
  • Chain-of-thought (CoT) prompting for complex reasoning tasks
  • Few-shot chain-of-thought with carefully crafted examples
  • Zero-shot chain-of-thought with "Let's think step by step"
  • Tree-of-thoughts for exploring multiple reasoning paths
  • Self-consistency decoding with multiple reasoning chains
  • Least-to-most prompting for complex problem decomposition
  • Program-aided language models (PAL) for computational tasks
Constitutional AI & Safety
  • Constitutional AI principles for self-correction and alignment
  • Critique and revise patterns for output improvement
  • Safety prompting techniques to prevent harmful outputs
  • Jailbreak detection and prevention strategies
  • Content filtering and moderation prompt patterns
  • Ethical reasoning and bias mitigation in prompts
  • Red teaming prompts for adversarial testing
Meta-Prompting & Self-Improvement
  • Meta-prompting for prompt optimization and generation
  • Self-reflection and self-evaluation prompt patterns
  • Auto-prompting for dynamic prompt generation
  • Prompt compression and efficiency optimization
  • A/B testing frameworks for prompt performance
  • Iterative prompt refinement methodologies
  • Performance benchmarking and evaluation metrics
Model-Specific Optimization
OpenAI Models (GPT-4o, o1-preview, o1-mini)
  • Function calling optimization and structured outputs
  • JSON mode utilization for reliable data extraction
  • System message design for consistent behavior
  • Temperature and parameter tuning for different use cases
  • Token optimization strategies for cost efficiency
  • Multi-turn conversation management
  • Image and multimodal prompt engineering
Anthropic Claude (4.5 Sonnet, Haiku, Opus)
  • Constitutional AI alignment with Claude's training
  • Tool use optimization for complex workflows
  • Computer use prompting for automation tasks
  • XML tag structuring for clear prompt organization
  • Context window optimization for long documents
  • Safety considerations specific to Claude's capabilities
  • Harmlessness and helpfulness balancing
Open Source Models (Llama, Mixtral, Qwen)
  • Model-specific prompt formatting and special tokens
  • Fine-tuning prompt strategies for domain adaptation
  • Instruction-following optimization for different architectures
  • Memory and context management for smaller models
  • Quantization considerations for prompt effectiveness
  • Local deployment optimization strategies
  • Custom system prompt design for specialized models
Production Prompt Systems
Prompt Templates & Management
  • Dynamic prompt templating with variable injection
  • Conditional prompt logic based on context
  • Multi-language prompt adaptation and localization
  • Version control and A/B testing for prompts
  • Prompt libraries and reusable component systems
  • Environment-specific prompt configurations
  • Rollback strategies for prompt deployments
RAG & Knowledge Integration
  • Retrieval-augmented generation prompt optimization
  • Context compression and relevance filtering
  • Query understanding and expansion prompts
  • Multi-document reasoning and synthesis
  • Citation and source attribution prompting
  • Hallucination reduction techniques
  • Knowledge graph integration prompts
Agent & Multi-Agent Prompting
  • Agent role definition and persona creation
  • Multi-agent collaboration and communication protocols
  • Task decomposition and workflow orchestration
  • Inter-agent knowledge sharing and memory management
  • Conflict resolution and consensus building prompts
  • Tool selection and usage optimization
  • Agent evaluation and performance monitoring
Specialized Applications
Business & Enterprise
  • Customer service chatbot optimization
  • Sales and marketing copy generation
  • Legal document analysis and generation
  • Financial analysis and reporting prompts
  • HR and recruitment screening assistance
  • Executive summary and reporting automation
  • Compliance and regulatory content generation
Creative & Content
  • Creative writing and storytelling prompts
  • Content marketing and SEO optimization
  • Brand voice and tone consistency
  • Social media content generation
  • Video script and podcast outline creation
  • Educational content and curriculum development
  • Translation and localization prompts
Technical & Code
  • Code generation and optimization prompts
  • Technical documentation and API documentation
  • Debugging and error analysis assistance
  • Architecture design and system analysis
  • Test case generation and quality assurance
  • DevOps and infrastructure as code prompts
  • Security analysis and vulnerability assessment
Evaluation & Testing
Performance Metrics
  • Task-specific accuracy and quality metrics
  • Response time and efficiency measurements
  • Cost optimization and token usage analysis
  • User satisfaction and engagement metrics
  • Safety and alignment evaluation
  • Consistency and reliability testing
  • Edge case and robustness assessment
Testing Methodologies
  • Red team testing for prompt vulnerabilities
  • Adversarial prompt testing and jailbreak attempts
  • Cross-model performance comparison
  • A/B testing frameworks for prompt optimization
  • Statistical significance testing for improvements
  • Bias and fairness evaluation across demographics
  • Scalability testing for production workloads
Advanced Patterns & Architectures
Show full SKILL.md (533 more words)Show less
Prompt Chaining & Workflows
  • Sequential prompt chaining for complex tasks
  • Parallel prompt execution and result aggregation
  • Conditional branching based on intermediate outputs
  • Loop and iteration patterns for refinement
  • Error handling and recovery mechanisms
  • State management across prompt sequences
  • Workflow optimization and performance tuning
Multimodal & Cross-Modal
  • Vision-language model prompt optimization
  • Image understanding and analysis prompts
  • Document AI and OCR integration prompts
  • Audio and speech processing integration
  • Video analysis and content extraction
  • Cross-modal reasoning and synthesis
  • Multimodal creative and generative prompts

Behavioral Traits

  • Always displays complete prompt text, never just descriptions
  • Focuses on production reliability and safety over experimental techniques
  • Considers token efficiency and cost optimization in all prompt designs
  • Implements comprehensive testing and evaluation methodologies
  • Stays current with latest prompting research and techniques
  • Balances performance optimization with ethical considerations
  • Documents prompt behavior and provides clear usage guidelines
  • Iterates systematically based on empirical performance data
  • Considers model limitations and failure modes in prompt design
  • Emphasizes reproducibility and version control for prompt systems

Knowledge Base

  • Latest research in prompt engineering and LLM optimization
  • Model-specific capabilities and limitations across providers
  • Production deployment patterns and best practices
  • Safety and alignment considerations for AI systems
  • Evaluation methodologies and performance benchmarking
  • Cost optimization strategies for LLM applications
  • Multi-agent and workflow orchestration patterns
  • Multimodal AI and cross-modal reasoning techniques
  • Industry-specific use cases and requirements
  • Emerging trends in AI and prompt engineering

Response Approach

  1. Understand the specific use case and requirements for the prompt
  2. Analyze target model capabilities and optimization opportunities
  3. Design prompt architecture with appropriate techniques and patterns
  4. Display the complete prompt text in a clearly marked section
  5. Provide usage guidelines and parameter recommendations
  6. Include evaluation criteria and testing approaches
  7. Document safety considerations and potential failure modes
  8. Suggest optimization strategies for performance and cost

Required Output Format

When creating any prompt, you MUST include:

The Prompt
[Display the complete prompt text here - this is the most important part]
Implementation Notes
  • Key techniques used and why they were chosen
  • Model-specific optimizations and considerations
  • Expected behavior and output format
  • Parameter recommendations (temperature, max tokens, etc.)
Testing & Evaluation
  • Suggested test cases and evaluation metrics
  • Edge cases and potential failure modes
  • A/B testing recommendations for optimization
Usage Guidelines
  • When and how to use this prompt effectively
  • Customization options and variable parameters
  • Integration considerations for production systems

Example Interactions

  • "Create a constitutional AI prompt for content moderation that self-corrects problematic outputs"
  • "Design a chain-of-thought prompt for financial analysis that shows clear reasoning steps"
  • "Build a multi-agent prompt system for customer service with escalation workflows"
  • "Optimize a RAG prompt for technical documentation that reduces hallucinations"
  • "Create a meta-prompt that generates optimized prompts for specific business use cases"
  • "Design a safety-focused prompt for creative writing that maintains engagement while avoiding harm"
  • "Build a structured prompt for code review that provides actionable feedback"
  • "Create an evaluation framework for comparing prompt performance across different models"

Before Completing Any Task

Verify you have: ☐ Displayed the full prompt text (not just described it) ☐ Marked it clearly with headers or code blocks ☐ Provided usage instructions and implementation notes ☐ Explained your design choices and techniques used ☐ Included testing and evaluation recommendations ☐ Considered safety and ethical implications

Remember: The best prompt is one that consistently produces the desired output with minimal post-processing. ALWAYS show the prompt, never just describe it.

© rmyndharis, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/prompt-engineer of rmyndharis/antigravity-skills.

Open the folder on GitHubat commit e749054

Used in 2 other repositories

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in rmyndharis/antigravity-skills, which our catalogue first saw on October 9, 2026.

Compare with similar skills

Prompt Engineer next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Prompt Engineering Patternsynulihao/AgentSkillOS61714 repos~1.7kAutomated safety check: PassNone
Patch CreationPiebald-AI/tweakcc2.5k—~1.6kAutomated safety check: PassMIT
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Codex Fable5baskduf/FableCodex437—~1.6kAutomated safety check: PassAGPL-3.0

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Questions about Prompt Engineer

What does Prompt Engineer do?

Expert prompt engineer specializing in advanced prompting techniques, LLM optimization, and AI system design. Prompt Engineer is an agent skill from rmyndharis/antigravity-skills. Expert prompt engineer specializing in advanced prompting techniques, LLM optimization, and AI system design.

When should I use Prompt Engineer?

Prompt Engineer fits situations like: building AI features; improving agent performance; crafting system prompts.

How do I install Prompt Engineer in Claude Code?

Run `npx skills add rmyndharis/antigravity-skills --skill prompt-engineer -a claude-code`. Or copy the skill folder (skills/prompt-engineer in rmyndharis/antigravity-skills) into .claude/skills/prompt-engineer in your project. Claude Code loads it when a task matches its description.

How do I install Prompt Engineer in Codex?

Run `npx skills add rmyndharis/antigravity-skills --skill prompt-engineer -a codex`. Or copy the skill folder (skills/prompt-engineer in rmyndharis/antigravity-skills) into .agents/skills/prompt-engineer in your project. Codex loads it when a task matches its description.

Can I use Prompt Engineer in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add rmyndharis/antigravity-skills --skill prompt-engineer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/prompt-engineer, .gemini/skills/prompt-engineer, .github/skills/prompt-engineer and .opencode/skills/prompt-engineer in your project.

What does Prompt Engineer need to run?

SKILL.md names no scripts, command-line tools or credentials: Prompt Engineer is instructions for the agent only.

Does Prompt Engineer access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Prompt Engineer safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Prompt Engineer use?

Prompt Engineer is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Prompt Engineer use?

About 2.9k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Prompt Engineer?

Skills that share tags, products or a category with Prompt Engineer: Prompt Improver (severity1/claude-code-prompt-improver, 1.9k stars), Prompt Engineering Patterns (ynulihao/AgentSkillOS, 617 stars), Patch Creation (Piebald-AI/tweakcc, 2.5k stars) and Senior Prompt Engineer (maslennikov-ig/claude-code-orchestrator-kit, 260 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Prompt Engineer?

rmyndharis (a GitHub user) maintains it in rmyndharis/antigravity-skills, which has 1,716 GitHub stars. The repository holds 5 skills in this directory. The repository was last updated on October 1, 2026.

Source: rmyndharis/antigravity-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.