make descriptions shorter

Author: vinta

README.md

@@ -134,10 +134,10 @@ _Libraries for building AI applications, LLM integrations, and autonomous agents
 
 - Agent Skills
   - [django-ai-plugins](https://github.com/vintasoftware/django-ai-plugins) - Django backend agent skills for Django, DRF, Celery, and Django-specific code review.
-  - [graphify](https://github.com/safishamsi/graphify) - Turn any folder of code, SQL schemas, R scripts, shell scripts, docs, papers, images, or videos into a queryable knowledge graph.
+  - [graphify](https://github.com/safishamsi/graphify) - Turn any folder of code, SQL schemas, docs, papers, images, or videos into a queryable knowledge graph.
   - [nuwa-skill](https://github.com/alchaincyf/nuwa-skill/blob/main/README_EN.md) - Nuwa distills the thinking of anyone — let Musk, Naval, Munger, and Feynman work for you.
   - [sentry-skills](https://github.com/getsentry/skills) - Python-focused engineering skills for code review, debugging, and backend workflows.
-  - [trailofbits-skills](https://github.com/trailofbits/skills) - Python-friendly security skills for auditing, testing, and safer backend development. Also [skills-curated](https://github.com/trailofbits/skills-curated).
+  - [trailofbits-skills](https://github.com/trailofbits/skills) - Python-friendly security skills for auditing, testing, and safer backend development.
 - Orchestration
   - [ag2](https://github.com/ag2ai/ag2) - An open-source AgentOS for multi-agent orchestration and building agentic AI systems.
   - [autogen](https://github.com/microsoft/autogen) - A programming framework for building agentic AI applications.
@@ -192,7 +192,7 @@ _Libraries for Machine Learning. Also see [awesome-machine-learning](https://git
 - [mindsdb](https://github.com/mindsdb/minds-platform) - MindsDB is an open source AI layer for existing databases that allows you to effortlessly develop, train and deploy state-of-the-art machine learning models using standard queries.
 - [pgmpy](https://github.com/pgmpy/pgmpy) - A Python library for probabilistic graphical models and Bayesian networks.
 - [scikit-learn](https://github.com/scikit-learn/scikit-learn) - The most popular Python library for Machine Learning with extensive documentation and community support.
-- * [scikit-lego](https://github.com/koaning/scikit-lego) - A collection of lego bricks for scikit-learn pipelines.
+- - [scikit-lego](https://github.com/koaning/scikit-lego) - A collection of lego bricks for scikit-learn pipelines.
 - [spark.ml](https://github.com/apache/spark) - [Apache Spark](https://spark.apache.org/)'s scalable [Machine Learning library](https://spark.apache.org/docs/latest/ml-guide.html) for distributed computing.
 - [TabGAN](https://github.com/Diyago/Tabular-data-generation) - Synthetic tabular data generation using GANs, Diffusion Models, and LLMs.
 - [timesfm](https://github.com/google-research/timesfm) - A pretrained foundation model from Google Research for time-series forecasting.