Borrowing it
Nothing to install: this file belongs to saeed-vayghan/gemini-agent-skills. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/saeed-vayghan/gemini-agent-skills/master/.gemini/skills/nlp-engineer/SKILL.mdgit clone --depth 1 https://github.com/saeed-vayghan/gemini-agent-skillsWrote 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/skills/saeed-vayghan/gemini-agent-skills/nlp-engineer)<a href="https://agentmods.dev/skills/saeed-vayghan/gemini-agent-skills/nlp-engineer"><img src="https://agentmods.dev/badge/skills/saeed-vayghan/gemini-agent-skills/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/skills/saeed-vayghan/gemini-agent-skills/nlp-engineer"><img src="https://agentmods.dev/badge/skills/saeed-vayghan/gemini-agent-skills/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 | $0.00041 | $0.01255 |
| Opus 5 | $0.00020 | $0.00628 |
| Sonnet 5 | $0.00008 | $0.00251 |
| Haiku 4.5 | $0.00004 | $0.00126 |
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.
How it starts
The opening of the file, as written. The whole thing — 269 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 ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 269 lines · 41 tokens per session scan A 6a7322a180b0
nlp-engineer is a skill published in the GitHub repository saeed-vayghan/gemini-agent-skills (34 stars, last pushed 7mo ago), licensed MIT. It adds 41 tokens to every session and 1,255 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 2026-09-03.
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