scholar-compute

scholar-compute is a Statistical modelling skill for AI agents, published by joshzyj in the open-scholar-skill repository on GitHub. Skillspace rates scholar-compute D (60/100) and it passed the safety scan; the rating is based on static analysis only.

Design and execute computational social science analyses across 11 modules: text-as-data/NLP (STM, BERTopic, Wordfish, BERT, conText embedding regression, LLM annotation + DSL bias correction, XLM-R/mBERT); ML (supervised, Double ML, Causal Forests, Bayesian brms/Stan, conformal prediction); network analysis (ERGMs, SAOMs, relational event models, GNN via PyTorch Geometric); agent-based modeling (Mesa 3.x, NetLogo, LLM agents, ODD, SALib); computer vision (DINOv2, CLIP, ViT, multimodal LLMs, VideoMAE); LLM workflows (structured extraction, CoT coding, computational grounded theory, RAG); LLM synthetic data (personas, silicon sampling, vignette sim); geospatial (sf, tidycensus, spdep, Moran's I, spatial SEM); audio-as-data (Essentia, Whisper, pyannote, PANNs/wav2vec2); life-event sequence modeling (life2vec per Savcisens et al. 2024 NCS). Runs per-module verification subagents. Saves internal log + publication-ready Methods + Results. Targets NCS, Science Advances, and computational…

Grade

D

Quality

60 / 100

Safety

Passed

GitHub stars

102

Unproven: rated by static analysis only, not yet compared head-to-head.

Our two independent classifiers differ on this skill's category, so treat the placement as provisional.

This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.

Skillspace indexes and rates this skill; it does not host or endorse it. Run third-party skills at your own risk.