skills.rest
akiselev
akiselevCOMMUNITY
Explore all skills by this author →
GitHub

prompt-engineer

Automate prompt optimization by invoking the Python optimize workflow across multiple scopes.

1|Updated Jan 12, 2026
Search Tags:#automation#workflow#python#llm#orchestration#triage#prompt-engineering
One-click install
npx skills add https://github.com/akiselev/qt-llm-template --skill prompt-engineer-akiselev
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-engineer
Source: https://github.com/akiselev/qt-llm-template/tree/main/.claude/skills/prompt-engineer
Command: npx skills add https://github.com/akiselev/qt-llm-template --skill prompt-engineer-akiselev

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates prompt optimization by immediately invoking the underlying Python workflow, eliminating manual prompt-tuning delays.

Core Features & Use Cases

  • Immediate execution: triggers the Python script located at .claude/skills/scripts to perform step-based prompt optimization.
  • Scope-aware triage: runs step 1 triage to determine scope and then executes steps 2-6 based on scope.
  • Ecosystem-aware prompts: supports single-prompt, ecosystem, greenfield, and problem-scoped optimization tasks.
  • Self-contained orchestration: reads frontmatter metadata to guide LLM interactions during activation.

Quick Start

Run the prompt-engineer optimize script to triage scope and kick off prompt optimization.

Frequently Asked Questions about prompt-engineer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate prompt optimization for an LLM workflow?▼

Automated prompt optimization triggers a Python script that applies triage-driven prompt engineering across single-prompt, ecosystem, greenfield, and problem-specific scopes to eliminate manual tuning.

What is triage-driven prompt engineering and when is it needed?▼

Triage-driven prompt engineering determines the optimization scope in step 1 and then executes steps 2-6 based on that scope, needed when managing single-prompt, ecosystem, greenfield, or problem-specific LLM interactions.

Do I need Python to run the prompt optimization workflow?▼

Yes, you need Python 3 and access to the script located at .claude/skills/scripts to run the optimize workflow and execute the automated triage and step-based prompt engineering process.

How do I optimize a greenfield prompt from scratch?▼

Optimizing a greenfield prompt requires running the optimize script, which performs scope-aware triage to identify the greenfield context and then executes targeted optimization steps for new prompts.

Can I use this to optimize prompts across an entire LLM ecosystem?▼

Yes, ecosystem-aware optimization supports managing prompts across an entire LLM ecosystem by reading frontmatter metadata to guide interactions and applying scope-specific triage steps.

What is the best way to handle problem-specific prompt tuning?▼

Problem-specific prompt tuning is handled by invoking the Python workflow, which automatically triages the problem scope and executes the corresponding optimization steps without manual intervention.

Related Skills

Explore More Software Engineering Skills→
canvascodingcanvascoding
10

skill-creator

Draft, evaluate, and iterate Claude Skills with Python-based validation.

Community
Advanced
apassuelloapassuello

llm-evaluation

Evaluate LLM outputs with automated metrics, human judgments, and benchmarking.

Community
Advanced
mcauduro0mcauduro0
1

senior-prompt-engineer

Analyze and optimize LLM prompts with Python scripts.

Community
Advanced
zzw4257zzw4257

ObliInjection

Test LLM agents for order-oblivious prompt injection vulnerabilities by analyzing prompt processing sequences.

Community
Intermediate