Skip to content
TrustedSkillsbeta
Back to all skills

Senior Prompt Engineer

Checked
ovachiever·vlatest·ai-ml

Crafts highly effective prompts for complex AI tasks, boosting model performance and unlocking advanced capabilities – a vital skill for optimal results.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add ovachiever/droid-tings --skill senior-prompt-engineer -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

ovachiever

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned ovachiever/droid-tings at 7acd12a754.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
7acd12a754
Manifest
skills/senior-prompt-engineer/SKILL.md
Scanned
7 files, 769 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

The Senior Prompt Engineer skill equips AI agents with world-class capabilities in crafting and optimizing prompts for complex AI/ML/Data systems within a production environment. It provides tools and expertise to improve model performance, implement advanced architectures, and ensure scalable, secure, and cost-effective solutions. This skill focuses on applying MLOps and DataOps best practices to prompt engineering workflows.

When to use it

  • When needing to optimize the performance of AI models in a production setting.
  • For designing and implementing scalable data processing pipelines for large datasets.
  • To deploy and monitor machine learning models with high availability and low latency.
  • When building agentic systems requiring complex orchestration and evaluation.

Key capabilities

  • Advanced prompt engineering patterns and best practices
  • Scalable system design and implementation
  • Performance optimization at scale for AI/ML workloads
  • MLOps and DataOps best practices
  • Real-time processing and inference
  • Model deployment and monitoring using tools like MLflow and Weights & Biases.
  • Utilizes tools including prompt_optimizer.py, rag_evaluator.py, and agent_orchestrator.py.

Example prompts

  • "Optimize the prompt for this text classification task to improve accuracy."
  • "Evaluate the performance of this Retrieval-Augmented Generation (RAG) system."
  • "Deploy this agentic workflow using the provided configuration file."

Tips & gotchas

  • This skill requires familiarity with Python and related ML/Data tools.
  • The prompt_optimizer.py, rag_evaluator.py, and agent_orchestrator.py scripts are core components, and understanding their usage is essential for effective implementation.
  • Refer to the reference documentation (prompt_engineering_patterns.md, llm_evaluation_frameworks.md, agentic_system_design.md) for detailed guidance on patterns, architectures, and best practices.