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.
git clone --depth 1 https://github.com/alexmmatos/arthur-mcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/alexmmatos/arthur-mcp/nlp-engineer)<a href="https://agentmods.dev/agents/alexmmatos/arthur-mcp/nlp-engineer"><img src="https://agentmods.dev/badge/agents/alexmmatos/arthur-mcp/nlp-engineer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/alexmmatos/arthur-mcp/nlp-engineer"><img src="https://agentmods.dev/badge/agents/alexmmatos/arthur-mcp/nlp-engineer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00039 | $0.01372 |
| Opus 5 | $0.00019 | $0.00686 |
| Sonnet 5 | $0.00008 | $0.00274 |
| Haiku 4.5 | $0.00004 | $0.00137 |
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 8d 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.
This is a copy
91% identical to nlp-engineer — 14 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 287 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
Communication Protocol
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.
- 8d ago First seen · 287 lines · 39 tokens per session scan A 2eb4f75904b4
nlp-engineer is an agent published in the GitHub repository alexmmatos/arthur-mcp (2 stars, last pushed 1mo ago), licensed MIT. It adds 39 tokens to every session and 1,372 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to nlp-engineer, differing in 14 lines, and is treated as a copy.
Other agents, from other repositories
tool-developer
Builds new UEFN Toolbelt tools autonomously. Audits the registry for duplicates, writes the tool, bumps counts, runs drift check, and gives the user exact test instructions.
verse-deployer
Verse codegen and error-fix loop for UEFN Toolbelt. Handles Phases 5–7 of the pipeline — write Verse, deploy, read build errors, fix, repeat until SUCCESS.
timps_federated_learning
Design Flower/FedAvg/PySyft FL pipelines with differential privacy and aggregation strategies. Use the timpsfederatedlearning MCP tool to perform this task. Do not answer directly — delegate to this sub-agent.
Judge Ethics & Bias
Evaluates code for model bias indicators, fairness metrics, explainability, data representativeness, consent handling, and human-in-the-loop safeguards.
FAI DSPy Expert
DSPy framework specialist — declarative LM programs, signature-based modules, optimizers (BootstrapFewShot, MIPRO), assertions, metric-driven prompt optimization, and compiled prompt pipelines.
timps_rag_designer
Design chunking/embedding/retrieval/reranking RAG pipelines for LlamaIndex or LangChain. Use the timpsragdesigner MCP tool to perform this task. Do not answer directly — delegate to this sub-agent.