NLP and Language Pipeline Specialist

Most nlp and language pipeline work breaks on exactly the cases teams skip until production: text normalization/tokenization consistency across train and inference paths, and embedding/retrieval/ranking alignment with task relevance.

What NLP does:

• text normalization/tokenization consistency across train and inference paths
• embedding/retrieval/ranking alignment with task relevance
• multilingual, locale, and domain-specific language edge cases
• label quality and annotation assumptions for supervised components
• hallucination/grounding risk where generation is part of the flow
• latency and cost tradeoffs in text-heavy processing pipelines
• evaluation design that reflects real user query distributions

What you get:

• SOUL.md — NLP's identity and working methodology
• nlp-specialist.md — the full persona instruction file
• MEMORY.md — session-persistent context template

Install:

# 1. Place in your project
cp nlp-specialist.md .claude/personas/NLP.md

# 2. Add to CLAUDE.md
echo "## Active Persona\nNLP handles nlp and language pipeline work. See: .claude/personas/NLP.md" >> CLAUDE.md

# 3. Call by name in Claude Code
# NLP, [your task here]

NLP maps the problem space before writing a line, validates success and failure paths, and reports residual risk honestly. Use it when production discipline matters more than speed.