Onboard and manage Paperclip AI for research-paper knowledge and agent orchestration
原文の言語: 英語
メニュー
SkillsMP は lamm-mit/scienceclaw から 292 件の skill を収集しています。skill を開くとソースと詳細を確認できます。
収集済み skill 292 件中 40 件を表示しています。
Onboard and manage Paperclip AI for research-paper knowledge and agent orchestration
原文の言語: 英語
Generate a structured scientific post and publish it to Infinite. Runs a focused single-agent investigation (PubMed search → LLM analysis → hypothesis/method/findings/conclusion) and posts the result. Faster than scienceclaw-investigate — best for targeted,…
原文の言語: 英語
Infinite platform integration for AI agent collaboration
原文の言語: 英語
Read a CSV or XLSX file and return columns, shape, dtypes, and first N rows as JSON.
原文の言語: 英語
Execute arbitrary Python code and return stdout. NumPy, pandas, scipy, matplotlib, and other scientific libraries are available.
原文の言語: 英語
Generate a structured scientific PDF report from a JSON description. Accepts a JSON file specifying title, authors, abstract, sections (headings, text, tables, figures), and inline data panels (heatmap, bar, scatter, line). Produces a publication-style A4 PDF…
原文の言語: 英語
Agentic computation — iteratively write code, run commands, read results, and reason about next steps
原文の言語: 英語
Run structure relaxation and phonon calculations using Meta's UMA (Universal Materials Accelerator) via fairchem
原文の言語: 英語
Submit, monitor, and retrieve DFT calculations on Artemis/SLURM via DREAMS framework
原文の言語: 英語
SLURM HPC job management on Artemis — write submission scripts, submit jobs, monitor status, retrieve results
原文の言語: 英語
Read and parse results from completed SLURM jobs — check status, retrieve output, filter candidates
原文の言語: 英語
Compute phonon properties and assess dynamic stability using ML potentials via phonopy
原文の言語: 英語
Generate candidate crystal structures by element substitution in prototype structures
原文の言語: 英語
Use when running AlphaFold2 predictions on custom protein sequences, validating designed sequences via self-consistency, predicting binder-target complexes, or interpreting AF2 confidence metrics (pLDDT, pTM, ipTM).
原文の言語: 英語
Use when predicting biomolecular structures (proteins, RNA, DNA, ligands) with the open-source Boltz diffusion model as an alternative to AlphaFold3.
原文の言語: 英語
Use when predicting molecular structures (proteins, nucleic acids, small molecules, and complexes) with the Chai-1 foundation model via local inference or the Chai Discovery API.
原文の言語: 英語
Generate comprehensive disease research reports using 100+ ToolUniverse tools. The agent creates a detailed markdown report file and progressively updates it with findings from 10 research dimensions, with full source citations. Use when users ask about…
原文の言語: 英語
Generates comprehensive drug research reports with compound disambiguation, evidence grading, and mandatory completeness sections. Covers identity, chemistry, pharmacology, targets, clinical trials, safety, pharmacogenomics, and ADMET properties. Use when…
原文の言語: 英語
Autonomous AI agent that modifies and iteratively improves a GPT language model training setup, running experiments within a 5-minute time budget to optimize validation bits-per-byte.
原文の言語: 英語
Multimodal reasoning LLM for protein function prediction integrating protein embeddings with biological context to generate structured reasoning traces and functional annotations.
原文の言語: 英語
A method to instantly internalize document contexts into language models using LoRA without fine-tuning.
原文の言語: 英語
Agentic materials discovery and DFT simulation framework using ASE, Quantum ESPRESSO, and Claude LLMs via LangGraph.
原文の言語: 英語
Soft differentiable drop-in replacements for non-differentiable JAX functions (abs, relu, sort, argmax, comparison, logical operators, etc.) with adjustable softening strength.
原文の言語: 英語
Approximate deep learning model components with symbolic equations using PySR
原文の言語: 英語
Generate task-specific LoRA adapters from natural language descriptions using a trained T2L model for instant transformer adaptation.
原文の言語: 英語
Flexible, high-performance framework for building, running, and evaluating autonomous agents with automated generation, experience learning, and RL training capabilities.
原文の言語: 英語
Run a multi-agent autonomous scientific investigation on any topic. Spawns specialized AI agents that use 300+ scientific tools (PubMed, BLAST, UniProt, PubChem, TDC, RDKit, etc.) to investigate and post findings to Infinite.
原文の言語: 英語
Investigate local files (PDFs, FASTA, CSV, TSV, JSON, TXT) using ScienceClaw's multi-agent science engine. Accepts files shared in chat or paths on disk, extracts content, and runs a full scientific investigation.
原文の言語: 英語
Run a scientific investigation on any topic and return findings directly to chat — without posting to Infinite. Use this for quick research, previews, or when the user says "don't post" or "just show me".
原文の言語: 英語
Check the status of a ScienceClaw agent — journal stats, recent investigations, knowledge graph size, and activity summary.
原文の言語: 英語
Run a live multi-agent scientific collaboration session and return a full summary when complete. Multiple specialised agents work in parallel, challenge each other's findings, and generate figures. Results and figures are saved to disk and a summary is…
原文の言語: 英語
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence…
原文の言語: 英語
ToolUniverse workflow — Adverse Event Detection
原文の言語: 英語
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed…
原文の言語: 英語
Access AlphaFold 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology.
原文の言語: 英語
Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for…
原文の言語: 英語
ToolUniverse workflow — Antibody Engineering
原文の言語: 英語
Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and…
原文の言語: 英語
Search ArXiv for scientific preprints in biology, chemistry, and related fields
原文の言語: 英語
Atomic Simulation Environment (ASE) for computational materials science. Perform DFT calculations, geometry optimization, band structure analysis, molecular property prediction, and periodic structure simulations. Supports VASP, MOPAC, Quantum ESPRESSO…
原文の言語: 英語