nlp-skills

NLP Skills Marketplace

Claude Code plugin for NLP tasks - LLM fine-tuning with coaching-style guidance workflow.

v0.3 Highlights

• Coaching-style Guidance: Explore pain points, clarify goals, recommend optimal solutions
• Multi-task Management: Version tracking and comparison for multiple training tasks
• Data Source Tracking: Reproducible data pipelines (DB, API, web scraping, LLM generation)
• Intelligent Agents: Auto-diagnose issues, analyze results, recommend improvements

Installation

Method 1: Via Marketplace (Recommended)

# Add nlp-skills marketplace
/plugin marketplace add p988744/nlp-skills

# Install
/plugin install nlp-skills

Method 2: Local Directory

claude --plugin-dir /path/to/nlp-skills

Components

Skills (7 Specialized Domains)

Skill	Triggers	Description
llm-coach	"train model", "fine-tune", "optimize"	Coaching guidance entry point
llm-knowledge	"what is LoRA", "compare models"	Knowledge base
task-manager	"list tasks", "compare versions"	Multi-task management
data-pipeline	"data source", "where does data come from"	Data pipeline configuration
writing-plans	"write a plan", "create training plan"	Plan-based task tracking
executing-plans	"execute plan", "run the plan"	Batch execution with checkpoints
finetune-llm	"fine-tune LLM", "training workflow"	Overview skill

Commands (9 Quick Actions)

Command	Description
/nlp-skills:coach	Start coaching dialogue
/nlp-skills:tasks	List all task status
/nlp-skills:new-task	Create new task
/nlp-skills:data-source	Configure data sources
/nlp-skills:generate	Generate project structure
/nlp-skills:write-plan	Write detailed execution plan
/nlp-skills:execute-plan	Execute plan with checkpoint reviews
/nlp-skills:evaluate	Run evaluation analysis
/nlp-skills:deploy	Deploy model

Agents (4 Intelligent Assistants)

Agent	Trigger	Function
goal-clarifier	Vague requirements	Proactively clarify goals
data-source-advisor	Data source questions	Help configure data pipelines
problem-diagnoser	Performance issues	Auto-diagnose and recommend fixes
result-analyzer	Post-training/evaluation	Analyze results, decision support

Usage

Quick Start

# Coaching guidance
"I want to train a model"

# Direct creation
/nlp-skills:new-task entity-sentiment

# List tasks
/nlp-skills:tasks

Complete Workflow

1. Start coaching        → Clarify goals, pain points, resources
2. Configure data source → Set up DB, API, scraping, LLM generation
3. Generate project      → Create scripts, configs, docs
4. Prepare data          → Run data generation scripts
5. Train model           → Execute training scripts
6. Evaluate performance  → Analyze results, compare versions
7. Deploy                → HuggingFace, Ollama

Task Project Structure

Each task is a fully independent, self-contained project:

{task-name}/
├── task.yaml               # Task definition
├── data_source.yaml        # Data source config (reproducible)
├── plans/                  # Execution plans (plan-based tracking)
│   └── YYYY-MM-DD-goal.md
├── versions/               # Version tracking (full lineage)
│   ├── v1/
│   │   ├── config.yaml
│   │   ├── data_snapshot.json
│   │   ├── results.json
│   │   └── lineage.yaml
│   └── v2/
├── data/
├── scripts/
├── configs/
├── models/
└── benchmarks/

Data Source Configuration

Core feature of v0.3 - reproducible data pipelines:

# data_source.yaml
sources:
  - type: database
    connection: postgresql://...
    query: "SELECT text, label FROM annotations"

  - type: api
    endpoint: https://api.example.com/data

  - type: web_scrape
    urls: ["https://..."]
    keywords: ["finance", "stock"]

  - type: llm_generated
    model: gpt-4o
    count: 500

Built-in Knowledge Base

Reduce web searches with built-in 2025-2026 knowledge:

Category	Content
Architecture	Dense vs MoE, MLA
Base Models	Qwen3, DeepSeek-V3/R1, Llama 3.3
Training Methods	SFT, LoRA, QLoRA, ORPO, DPO
Task Types	Sentiment Analysis, NER, Relation Extraction
Troubleshooting	Overfitting, Class Imbalance, Low Accuracy

Requirements

Remote Server (Training)

• GPU: NVIDIA GPU (24GB+ VRAM recommended)
• Python: 3.10+

Local Development

• Claude Code: Latest version

Development

Local Testing

claude --plugin-dir .
claude --debug --plugin-dir .

Validation

./scripts/validate-plugin.sh

CI/CD

• GitHub Actions: Auto-validates on push/PR
• GitLab CI: Auto-validates on push/MR

Version History