Results for “primekg”

Query the Precision Medicine Knowledge Graph (PrimeKG) for multiscale biological data including genes, drugs, diseases, phenotypes, and more.

Queries the Precision Medicine Knowledge Graph (PrimeKG) for multiscale biomedical relationships across genes, drugs, diseases, phenotypes, pathways, and biological processes. Use when exploring drug-disease or gene-disease links, building disease-centric knowledge subgraphs, or sourcing relations for drug repurposing and precision-medicine analyses. Part of the AlterLab Academic Skills suite.

Prüft KG- und Personengesellschaften-Fälle in Großkanzlei Corporate/M&A-Mandaten: Frist, Form, Zuständigkeit, Rechtsweg und Sofortmaßnahmen; liefert eine Fristen- und Risikoampel mit Sofortschritten.

Primary Python tool for 40+ bioinformatics services. Preferred for multi-database workflows: UniProt, KEGG, ChEMBL, PubChem, Reactome, QuickGO. Unified API for queries, ID mapping, pathway analysis. For direct REST control, use individual database skills (uniprot-database, kegg-database).

Entrena modelos de machine learning con Scikit-learn, LightGBM y XGBoost, desde un baseline hasta un modelo productivo con validación robusta y explicabilidad.

Build self-serve acquisition and expansion motions, including deciding between PLG and sales-led, optimizing activation, driving freemium conversion, and building growth equations.

Data pipeline specialist for ML experiments. Use when the user asks to talk to Gekko, requests the data engineer, or needs DataLoader optimization.

Submit and manage protocols on Ginkgo Bioworks Cloud Lab for autonomous lab execution, including protein expression, purification, quantification, RNA synthesis, and custom workflows via EstiMate.

本地 pub2gg 全链路：文章→DeepSeek排版→GitHub存稿→WordPress发布→Telegram推送。不依赖阿里云中继，纯 Hermes 本地执行。

Quantize large language models to 4-bit with minimal accuracy loss using GPTQ, enabling deployment of 70B+ models on consumer GPUs with 4× memory reduction and 3-4× faster inference.

Calculates first- and second-order option Greeks (Delta, Gamma, Theta, Vega, Rho, Charm, Vanna, Volga) for single contracts or multi-leg positions, with scenario heatmaps and position-level aggregation.

Documents PostHog's design system: a warm cream canvas, hand-drawn hedgehog mascots, IBM Plex Sans typography, and a single yellow-orange CTA pill, with detailed color, typography, and component specifications.

Premium brand-kit image generation skill for creating high-end brand-guidelines boards, logo systems, identity decks, and visual-world presentations. Trained for minimalist, cinematic, editorial, dark-tech, luxury, cultural, security, gaming, developer-tool, and consumer-app brand systems. Optimized for intentional logo concepting, refined composition, sparse typography, strong symbolic meaning, premium mockups, art-directed imagery, and flexible grid layouts.

Comprehensive guide to ddpg. Master the concepts, implementation, best practices, and real-world applications of ddpg in professional environments.

Manage Kafka consumer groups on Redpanda with rpk group: list, describe, seek, delete groups, and delete offsets.

KINTSUGI NOTEBOOK processing principles: Never sacrifice quality for speed, always use GPU when available. Trigger: performance optimization, CPU/GPU choice, fast mode, quality vs speed in NOTEBOOKS (not SLURM).

Regex/parser/DSL design specialist for grammar authoring and ReDoS-safe regex. Not for REST APIs (Gateway) or DB schemas (Schema).

Language-independent tokenizer treating text as raw Unicode. Supports BPE and Unigram algorithms. Fast (50k sentences/sec), lightweight (6MB memory), deterministic vocabulary. Used by T5, ALBERT, XLNet, mBART. Train on raw text without pre-tokenization. Use when you need multilingual support, CJK languages, or reproducible tokenization.

Generates optimized prompts for any AI tool. Use when writing, fixing, improving, or adapting a prompt for LLM, Cursor, Midjourney, image AI, video AI, coding agents, or any other AI tool.

Premium brand-kit image generation skill for creating high-end brand-guidelines boards, logo systems, identity decks, and visual-world presentations. Trained for minimalist, cinematic, editorial, dark-tech, luxury, cultural, security, gaming, developer-tool, and consumer-app brand systems. Optimized for intentional logo concepting, refined composition, sparse typography, strong symbolic meaning, premium mockups, art-directed imagery, and flexible grid layouts.

Activation-aware weight quantization for 4-bit LLM compression with 3x speedup and minimal accuracy loss. Use when deploying large models (7B-70B) on limited GPU memory, when you need faster inference than GPTQ with better accuracy preservation, or for instruction-tuned and multimodal models. MLSys 2024 Best Paper Award winner.

pg-boss expert for PostgreSQL-backed job queues with exactly-once delivery, perfect for applications already using Postgres (Supabase, Neon, etc.). Use when "pg-boss, postgres queue, postgresql job, supabase background job, neon job queue, postgres scheduling, database job queue, pg-boss, postgresql, job-queue, background-jobs, supabase, neon, exactly-once, scheduling" mentioned.

Optimize for AI recommendations so ChatGPT, Claude, and Perplexity suggest your brand first.

Use when the user wants India-specific MSME marketing automation using Gemini CLI — government scheme content, local business growth, Hindi/Hinglish copy, startup funding content, or B2G/B2B marketing for Indian markets. Trigger phrases: MSME marketing Gemini CLI, India startup content CLI, PMEGP MUDRA content Gemini, Hinglish marketing automation, UP Bihar MSME digital marketing. For general content see gemini-cli-copywriting.

Quantize large language models to 4-bit precision using activation-aware weight quantization, reducing memory footprint and speeding up inference with minimal accuracy loss.

Designing and evaluating AI/ML systems: prompt engineering, RAG design, LLM application patterns, AI safety, evaluation frameworks, MLOps, cost optimization. Use for AI pipelines or eval harnesses.

Reference guide for building Preact 10 applications with HTM, covering components, signals, web components, and performance patterns.

Post-execution skill crystallization and learning engine updates for WinDAGs. Runs after successful execution to update Thompson sampling parameters, track method quality, detect monster-barring, log near-miss events, and signal Kuhnian crises. Activate on "curator", "learning update", "skill crystallization", "Thompson sampling", "monster-barring", "near-miss", "Kuhnian crisis", "post-execution learning". NOT for pre-execution risk scanning (use windags-premortem), retrospective analysis (use windags-looking-back), or DAG construction (use windags-architect).

Optimizes content for AI search systems like AI Overviews, ChatGPT, and Perplexity by analyzing citability, structure, authority, and technical accessibility, and producing a GEO analysis report.

Post-training 4-bit quantization for LLMs with minimal accuracy loss. Use for deploying large models (70B, 405B) on consumer GPUs, when you need 4× memory reduction with <2% perplexity degradation, or for faster inference (3-4× speedup) vs FP16. Integrates with transformers and PEFT for QLoRA fine-tuning.

Generates premium brand-kit images including logo systems, identity decks, and visual-world presentations. Creates intentional, minimal, and strategic brand boards with grid-based layouts, symbolic logo concepts, and application mockups.

Predicts the highest-impact next action for your project by running a 5-agent meta-DAG pipeline. Gathers project signals automatically (git, recent files, port-daddy, CLAUDE.md), then runs sensemaker → decomposer → skill-selector + premortem → synthesizer. When execution is approved, convert each predicted node into a skillful node prompt using skillful-node-prompt + skillful-subagent-creator, prefer live WinDAGs visualization backed by POST /api/execute and /ws/execution/:id, and fall back to ASCII only when live visualization is unavailable. Activate on: "what should I do", "what's next", "next move", "/next-move", "where should I focus", "what's the highest impact thing right now". NOT for: creating skills, debugging one specific bug, or promising topology-specific runtime behavior the current server cannot execute.

Automatically rig 3D models with UniRig (VAST-AI-Research, SIGGRAPH'25) — predict a skeleton, predict skinning weights, and merge the rig back onto the original mesh. Use when the user wants auto-rigging for .obj/.fbx/.glb/.gltf/.dae/.vrm assets, a skeleton or skin weights for a character or creature, a UniRig environment prepared on a CUDA machine, batch rigging of a model directory, or an honest comparison between UniRig, SkinTokens, Tripo, Mixamo, AccuRig, and Blender Rigify. Triggers on: unirig, auto rig, auto-rigging, 3D rigging, skeleton prediction, skinning weights, rig a character, armature generation, rigged glb, rigged fbx, bone weights.

Performs differential expression analysis on label-free quantitative (LFQ) proteomics data from MaxQuant and DIA-NN outputs, including preprocessing, imputation, statistical testing, and visualization.

Primeiro agente do Time de Migração. Detecta o paradigma do sistema legado a partir das specs, infere o paradigma natural da stack alvo, alerta sobre gaps e força uma decisão consciente do usuário. Produz paradigm_decision.md, leitura obrigatória de todos os agentes posteriores. Ativação: /reversa-paradigm-advisor (geralmente invocado por /reversa-migrate).