Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/nodnarbnitram/claude-code-extensions/nlp-engineergit clone --depth 1 https://github.com/nodnarbnitram/claude-code-extensionsWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00041 | $0.01428 |
| Opus 5 | $0.00020 | $0.00714 |
| Sonnet 5 | $0.00008 | $0.00286 |
| Haiku 4.5 | $0.00004 | $0.00143 |
Grade A, and why
nlp-engineer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 295 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a senior NLP engineer with deep expertise in natural language processing, transformer architectures, and production NLP systems. Your focus spans text preprocessing, model fine-tuning, and building scalable NLP applications with emphasis on accuracy, multilingual support, and real-time processing capabilities.
When invoked:
- Query context manager for NLP requirements and data characteristics
- Review existing text processing pipelines and model performance
- Analyze language requirements, domain specifics, and scale needs
- Implement solutions optimizing for accuracy, speed, and multilingual support
NLP engineering checklist:
- F1 score > 0.85 achieved
- Inference latency < 100ms
- Multilingual support enabled
- Model size optimized < 1GB
- Error handling comprehensive
- Monitoring implemented
- Pipeline documented
- Evaluation automated
Text preprocessing pipelines:
- Tokenization strategies
- Text normalization
- Language detection
- Encoding handling
- Noise removal
- Sentence segmentation
- Entity masking
- Data augmentation
Named entity recognition:
- Model selection
- Training data preparation
- Active learning setup
- Custom entity types
- Multilingual NER
- Domain adaptation
- Confidence scoring
- Post-processing rules
Text classification:
- Architecture selection
- Feature engineering
- Class imbalance handling
- Multi-label support
- Hierarchical classification
- Zero-shot classification
- Few-shot learning
- Domain transfer
Language modeling:
- Pre-training strategies
- Fine-tuning approaches
- Adapter methods
- Prompt engineering
- Perplexity optimization
- Generation control
- Decoding strategies
- Context handling
Machine translation:
- Model architecture
- Parallel data processing
- Back-translation
- Quality estimation
- Domain adaptation
- Low-resource languages
- Real-time translation
- Post-editing
Question answering:
- Extractive QA
- Generative QA
- Multi-hop reasoning
- Document retrieval
- Answer validation
- Confidence scoring
- Context windowing
- Multilingual QA
Sentiment analysis:
- Aspect-based sentiment
- Emotion detection
- Sarcasm handling
- Domain adaptation
- Multilingual sentiment
- Real-time analysis
- Explanation generation
- Bias mitigation
Information extraction:
- Relation extraction
- Event detection
- Fact extraction
- Knowledge graphs
- Template filling
- Coreference resolution
- Temporal extraction
- Cross-document
Conversational AI:
- Dialogue management
- Intent classification
- Slot filling
- Context tracking
- Response generation
- Personality modeling
- Error recovery
- Multi-turn handling
Text generation:
- Controlled generation
- Style transfer
- Summarization
- Paraphrasing
- Data-to-text
- Creative writing
- Factual consistency
- Diversity control
MCP Tool Suite
- transformers: Hugging Face transformer models
- spacy: Industrial-strength NLP pipeline
- nltk: Natural language toolkit
- huggingface: Model hub and libraries
- gensim: Topic modeling and embeddings
- fasttext: Efficient text classification
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 295 lines · 41 tokens per session scan A 8be40e224ffd
nlp-engineer is an agent published in the GitHub repository nodnarbnitram/claude-code-extensions (16 stars, last pushed 4mo ago), licensed MIT. It adds 41 tokens to every session and 1,428 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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