Packs

8 packs

Build GraphQL API

Design a GraphQL schema, implement resolvers with DataLoader, and integrate with Apollo.

4 skills · pack

Builds multi-language source code graphs for security analysis: call graphs, attack surface mapping, blast radius, taint propagation, complexity hotspots, and entry point enumeration. Generates Mermaid diagrams (call graphs, class hierarchies, dependency maps, heatmaps). Compares code graph snapshots for structural diff and evolution analysis. Runs graph-informed mutation testing triage (genotoxic

10 skills · pack

Threat Intelligence Analysis

For threat analysts correlating IOCs, building campaign graphs, and producing intelligence reports.

9 skills · pack

Logo Design and OG Image

Install this pack to design a logo and create matching Open Graph images for social sharing.

4 skills · pack

Build Agent with LangGraph

Build production-grade stateful AI agents using LangGraph, covering graph construction, state management, persistence, and human-in-the-loop patterns.

9 skills · pack

Simulator.Company platform assistant. Exposes the Simulator REST API as an MCP server and provides skills for managing actors, forms, graph structures, and financial accounts.

19 skills · pack

@pwdev-solucoes

Spec-driven development framework v2.3 — 8 real subagents (incl. advisor), per-task model routing, curated memory graph, opt-in parallel waves, external CLI delegation (Codex/OpenCode/Kimi/Gemini/Kiro), simplification pass, strict verify, audit hooks, 22 commands

2 skills · pack

@brycewang-stanford

Twelve-skill bundle covering the Cell manuscript lifecycle: workflow router, scope/significance fit, single-narrative framing, the Highlights + eTOC + Graphical Abstract trio, the ≤150-word Summary, main-text writing, display items, STAR Methods + Key Resources Table, data/code availability, Cell Press author–date references, submission preflight + cover letter, and reviewer rebuttal.

9 skills · pack

Results for “graph”

12 skills

nemo-mbridge-perf-cuda-graphs

Validate and use CUDA graph capture in Megatron Bridge, including local full-iteration graphs and Transformer Engine scoped graphs for attention, MLP, and MoE modules.

Build and train graph neural networks for drug discovery, protein modeling, and molecular science using PyTorch-native tools.

30.2k · bundle

Visualize training metrics, debug models with histograms, compare experiments, visualize model graphs, and profile performance with TensorBoard.

Predict molecular properties, train graph neural networks, and run drug discovery workflows using DeepChem's featurizers, models, and MoleculeNet benchmarks.

30.2k · bundle

Loads and processes whole-slide pathology images, builds spatial graphs, trains deep learning models, and analyzes multiplexed immunofluorescence data across 160+ slide formats.

More results

Evaluates image captions by converting them into scene graphs and computing an F-score over semantic propositions, measuring how well a generated caption captures the meaning of an image compared to human references.

Analyze whole-slide pathology images with Python: load 160+ slide formats, preprocess H&E stains, segment nuclei, construct spatial graphs, train ML models, and process multiplex immunofluorescence data (CODEX, Vectra).

30.2k · bundle

orchestra-research

Visualize training metrics, debug models with histograms, compare experiments, visualize model graphs, and profile performance using TensorBoard.

10.4k · bundle

Optimizes Dify workflows and plugins by restructuring graphs, reducing LLM token usage, tuning worker pools, and improving parallel processing.

optimize-for-gpu

GPU-accelerate Python code using CuPy, Numba CUDA, Warp, cuDF, cuML, cuGraph, and other RAPIDS libraries for dramatic speedups on numerical, data, ML, graph, and simulation workloads.

30.2k · bundle

Provides domain knowledge on the Causal Dynamics Architecture (CDA), an alternative AI computing architecture based on causal graphs and Hamiltonian dynamics, with references for deep dives.

Evaluates automated metrics and vision-language models on identifying granular errors in detailed image descriptions and ranking paired descriptions against human judgments, using macro F1, pairwise accuracy, Spearman rank ρ, and Kendall's τ.