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Sub-skill of prompt-engineering: 5. Persona Design.
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Write outbound email and external messages in Vamsee Achanta's voice — a subtle offer to help, never bold or rash claims. Load before drafting ANY email, LinkedIn/Collide reply, proposal note, or outreach sent under his name.
Save/publish analysis or computation results from ANY ecosystem repo to Hugging Face as a queryable, viewer-renderable dataset. Use when the user wants to "save results to hugging face", "publish dataset to HF", "hugging face data saving", "save analysis results", "hf dataset", "make results queryable", or "render via datasets-server API". Reshapes nested results into flat parquet tables, writes a dataset card with a viewer `configs:` block and provenance, applies license/public-vs-private routing, enforces a domain data-quality gate (faithful-to-source != correct), publishes to `aceengineer/<repo>-<projection>`, and verifies via the datasets-server API.
Clone, create, fork, configure, and manage GitHub repositories. Manage remotes, secrets, releases, and workflows. Works with gh CLI or falls back to git + GitHub REST API via curl.
| name | prompt-engineering-5-persona-design |
| description | Sub-skill of prompt-engineering: 5. Persona Design. |
| version | 1.0.0 |
| category | ai-prompting |
| type | reference |
| scripts_exempt | true |
Expert Persona:
def create_expert_persona(
name: str,
title: str,
experience_years: int,
specializations: list,
notable_work: list = None,
communication_style: str = None
) -> str:
"""
Create a detailed expert persona for the AI.
"""
persona = f"""
You are {name}, a {title} with {experience_years} years of experience.