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Agent skill

Npcpy Research Guide

by wentorai in wentorai/research-plugins

All-in-one Python library for NLP, agents, and knowledge graphs

MITAuto-check passedKnowledge Management

Install Npcpy Research Guide

skills CLI
$ npx skills add wentorai/research-plugins --skill npcpy-research-guide -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install wentorai/research-plugins npcpy-research-guide --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/wentorai/research-plugins.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/domains/ai-ml/npcpy-research-guide .claude/skills/npcpy-research-guide && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
npcpy-research-guide
GitHub stars
298
Used in
1 other repo
Token cost
~818 tokens
SKILL.md length
113 words
Files
1
Skills in repo
405
Repo updated
First seen
Licence
MIT

At a glance

All-in-one Python library for NLP, agents, and knowledge graphs

  • Works in 5 steps: Quick NLP: Text processing without heavy… → Agent prototyping: Rapid agent creation… → Knowledge extraction: Build KGs from… → …
  • Tasks that involve Natural language processing
  • SKILL.md covers Overview, Installation, Core Modules and Research Workflows, plus 2 more sections
  • Calls pip

What it does

Npcpy Research Guide is an agent skill from wentorai/research-plugins. All-in-one Python library for NLP, agents, and knowledge graphs

Its SKILL.md is about 820 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Knowledge Management, covering Natural language processing and Knowledge graphs. It works with Python. The repository describes itself as: 350+ academic research skills, MCP configs, and plugins for Research-Claw and AI agents. The licence is MIT.

When your agent uses it

  • Tasks that involve Natural language processing
  • Tasks that involve Knowledge graphs

Example prompts

  • “/npcpy-research-guide”

Requirements

  • Python 3

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Quick NLP: Text processing without heavy setup
  2. Agent prototyping: Rapid agent creation and testing
  3. Knowledge extraction: Build KGs from research text
  4. Research automation: Literature search and synthesis
  5. Teaching: Demonstrate NLP/agent concepts

What it can do on your machine

Read from SKILL.md and the folder at commit bf44b3c. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • pip

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • github.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Npcpy Research Guide loads about 818 tokens when it runs. Until then it costs about 21 tokens; SKILL.md has 113 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~21
When it runs · the whole SKILL.md, loaded when a task matches
~818

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from wentorai/research-plugins at commit bf44b3c, republished under its MIT licence (© wentorai). 113 words, ~818 tokens.

Download SKILL.mdSave it as .claude/skills/npcpy-research-guide/SKILL.md (or your agent's skills folder).
name
npcpy-research-guide
description
All-in-one Python library for NLP, agents, and knowledge graphs

npcpy Research Guide

Overview

npcpy is an all-in-one Python library that combines NLP, agent orchestration, and knowledge graph capabilities in a single package. It provides tools for text processing, entity extraction, agent creation, graph-based reasoning, and research automation. Designed as a Swiss Army knife for AI researchers who need quick access to diverse NLP and agent capabilities without juggling many dependencies.

Installation

bash
pip install npcpy

Core Modules

NLP Processing
python
from npcpy import NLP

nlp = NLP()

# Text processing pipeline
doc = nlp.process(
    "Transformers have revolutionized NLP since Vaswani et al. "
    "introduced the attention mechanism in 2017."
)

# Named entities
for entity in doc.entities:
    print(f"[{entity.type}] {entity.text}")
# [METHOD] Transformers
# [PERSON] Vaswani
# [CONCEPT] attention mechanism
# [DATE] 2017

# Key phrases
print(doc.key_phrases)
# ["attention mechanism", "Transformers", "NLP"]

# Sentiment / stance
print(doc.sentiment)  # positive
Agent Creation
python
from npcpy import Agent, Tool

# Create a research agent
agent = Agent(
    name="research_assistant",
    llm_provider="anthropic",
    tools=[
        Tool("web_search", description="Search the web"),
        Tool("paper_search", description="Search academic papers"),
        Tool("calculator", description="Math calculations"),
    ],
)

# Run a task
result = agent.run(
    "Find the top 5 most cited papers on few-shot learning "
    "from 2023 and summarize their approaches."
)
print(result.output)
Knowledge Graphs
python
from npcpy import KnowledgeGraph

kg = KnowledgeGraph()

# Extract knowledge from text
kg.extract_from_text(
    "BERT uses masked language modeling for pre-training. "
    "GPT uses autoregressive language modeling. "
    "Both are based on the Transformer architecture."
)

# Query the graph
results = kg.query("What models use Transformer architecture?")
# ["BERT", "GPT"]

# Visualize
kg.visualize("knowledge_graph.html")

# Export
kg.export("kg.json")

Research Workflows

python
from npcpy import ResearchWorkflow

workflow = ResearchWorkflow(llm_provider="anthropic")

# Literature search + synthesis
report = workflow.literature_review(
    topic="prompt engineering techniques",
    num_papers=20,
    synthesis_style="academic",
)
report.save("review.md")

# Paper analysis
analysis = workflow.analyze_paper("paper.pdf")
print(analysis.summary)
print(analysis.methodology)
print(analysis.key_findings)

Use Cases

  1. Quick NLP: Text processing without heavy setup
  2. Agent prototyping: Rapid agent creation and testing
  3. Knowledge extraction: Build KGs from research text
  4. Research automation: Literature search and synthesis
  5. Teaching: Demonstrate NLP/agent concepts

References

© wentorai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/domains/ai-ml/npcpy-research-guide of wentorai/research-plugins.

Open the folder on GitHubat commit bf44b3c

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in wentorai/research-plugins, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Npcpy Research Guide next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Npcpy Research Guide compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Npcpy Research Guide this skillwentorai/research-plugins2981 repos~818Automated safety check: PassMIT
LLM Wiki Knowledge GraphEgonex-AI/Understand-Anything86k—~1.5kAutomated safety check: PassMIT
Agoraflancian/agora136—~295Automated safety check: PassApache-2.0
Geo Knowledgeyaojingang/GEOHub165—~349Automated safety check: PassAGPL-3.0
Mini Context Graphgithub/awesome-copilot40k1 repos~2kAutomated safety check: PassMIT
Neo4j Genai Plugin Skillneo4j-contrib/neo4j-skills114—~3kAutomated safety check: NotesMIT

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Works with

Categories

Questions about Npcpy Research Guide

What does Npcpy Research Guide do?

All-in-one Python library for NLP, agents, and knowledge graphs. Npcpy Research Guide is an agent skill from wentorai/research-plugins.

When should I use Npcpy Research Guide?

Npcpy Research Guide fits situations like: tasks that involve Natural language processing; tasks that involve Knowledge graphs.

How do I install Npcpy Research Guide in Claude Code?

Run `npx skills add wentorai/research-plugins --skill npcpy-research-guide -a claude-code`. Or copy the skill folder (skills/domains/ai-ml/npcpy-research-guide in wentorai/research-plugins) into .claude/skills/npcpy-research-guide in your project. Claude Code loads it when a task matches its description.

How do I install Npcpy Research Guide in Codex?

Run `npx skills add wentorai/research-plugins --skill npcpy-research-guide -a codex`. Or copy the skill folder (skills/domains/ai-ml/npcpy-research-guide in wentorai/research-plugins) into .agents/skills/npcpy-research-guide in your project. Codex loads it when a task matches its description.

Can I use Npcpy Research Guide in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add wentorai/research-plugins --skill npcpy-research-guide -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/npcpy-research-guide, .gemini/skills/npcpy-research-guide, .github/skills/npcpy-research-guide and .opencode/skills/npcpy-research-guide in your project.

What does Npcpy Research Guide need to run?

Going by SKILL.md and its folder, Npcpy Research Guide needs the command-line tools its instructions call (pip). Our summary lists: Python 3.

Does Npcpy Research Guide access the network?

SKILL.md names 1 domain. As links in the text: github.com. This is read from the text; nothing was executed.

Is Npcpy Research Guide safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Npcpy Research Guide use?

Npcpy Research Guide is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Npcpy Research Guide use?

About 818 tokens (SKILL.md is roughly 3.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Npcpy Research Guide?

Skills that share tags, products or a category with Npcpy Research Guide: LLM Wiki Knowledge Graph (Egonex-AI/Understand-Anything, 86k stars), Agora (flancian/agora, 136 stars), Geo Knowledge (yaojingang/GEOHub, 165 stars) and Mini Context Graph (github/awesome-copilot, 40k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Npcpy Research Guide?

wentorai (a GitHub user) maintains it in wentorai/research-plugins, which has 298 GitHub stars. The repository holds 405 skills in this directory. The repository was last updated on June 19, 2026.

Source: wentorai/research-plugins on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.