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All-in-one Python library for NLP, agents, and knowledge graphs
Passed 20 of 22 test cases with the skill, and 10 of 22 without it.
and 15 more in the full report
10 fixed10 passed either way2 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 10 test cases the skill fixed. Same model, same prompt, with and without the skill.
What is the exact import statement for npcpy's knowledge graph module? Avoid subpackages like npcpy.graph or npcpy.kg.
| Checked | Without skill | With skill |
|---|---|---|
| Imports KnowledgeGraph directly from top-level npcpy. | Missed | Passed |
The instructions your model reads when the skill loads.
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.
pip install npcpy
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
test cases passed, vs 10 / 22 without
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