| name | prompt-engineering |
| description | Advanced prompt engineering — chain-of-thought, few-shot, tree-of-thought, self-consistency, meta-prompting, system design, debugging, and optimization for production AI systems. Use when working with prompt engineering. |
| domain | core |
| author | oyi77 |
| license | Apache-2.0 |
| subdomain | core-platform |
| tags | ["engineering","infrastructure","memory","prompt","self-improvement"] |
| version | 1.0.0 |
Prompt Engineering
When to Use
Trigger phrases:
-
"prompt engineering"
-
"Advanced prompt engineering — chain-of-thought, few-shot, tree-of-thought, self-"
-
LLM outputs are inconsistent or low quality
-
Complex reasoning tasks that need step-by-step thinking
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Building reusable prompt templates for production systems
-
Optimizing prompts for cost (fewer tokens) or accuracy
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Creating system prompts and custom instructions for AI agents
-
Debugging prompt performance issues
-
Designing multi-turn conversation flows
When NOT to Use
- When the task can be solved with existing standard libraries
- When the infrastructure is already in place and working
- When the added complexity does not provide measurable benefit
Overview
Prompt Engineering is a foundational core infrastructure skill that provides system foundation capabilities for the agent ecosystem.
Architecture
- Input layer — Receives and validates incoming requests
- Processing layer — Core logic for system foundation
- Output layer — Formats and delivers results
- State management — Maintains context across invocations
Configuration
- Set up required environment variables and paths
- Configure logging level and output format
- Define resource limits (memory, time, API calls)
- Enable/disable features via configuration flags
Integration
- Exposes standard interfaces for other skills to consume
- Supports event-driven and request-response patterns
- Compatible with the 1ai-skills hook system
- Logs metrics for the skill performance monitor
Anti-Rationalization Table
| Rationalization | Reality |
|---|
| "I will add monitoring later" | Without monitoring, you cannot detect failures. Add it from day one. |
| "One model is enough" | Different tasks need different models. Route intelligently. |
| "Premature optimization" | Infrastructure decisions are hard to change later. Design for scale early. |
ROUTES = {
"code": ["claude-sonnet-4-20250514", "gpt-4o"],
"vision": ["gemini-2.5-pro", "gpt-4o"],
"fast": ["gemini-2.5-flash", "gpt-4o-mini"],
}
def route_request(task: str, prompt: str):
models = ROUTES.get(task, ROUTES["fast"])
for model in models:
try:
return call_model(model, prompt)
except Exception:
continue
raise RuntimeError("All models failed")
Process
- Prepare — Gather requirements, verify prerequisites, set up environment
- Execute — Run prompt engineering workflow with configured parameters
- Verify — Validate output meets requirements, document results
Verification
