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SkillsMP는 companion-inc/feynman에서 48개의 skill을 수집했습니다. skill을 열어 소스와 세부 정보를 확인하세요.
수집된 skill 48개 중 40개를 표시합니다.
Predict or audit protein structures with AlphaFold2-style workflows. Use when a research task needs monomer/multimer structure prediction, MSA/template handling, confidence metrics, or comparison against PDB/AlphaFold references.
원문 언어: 영어
Run or plan Boltz biomolecular structure predictions for proteins, complexes, ligands, or nucleic-acid assemblies. Use when a task asks for Boltz setup, inputs, outputs, confidence interpretation, or reproduction.
원문 언어: 영어
Use Borzoi-style regulatory genomics models for sequence-to-expression or variant-effect analysis. Use when the task asks for noncoding variant impact, regulatory sequence design, or expression prediction.
원문 언어: 영어
Run or prepare Chai-1 structure predictions for biomolecular complexes. Use when a task asks for Chai-1 inputs, multimers, ligand/nucleic acid structure prediction, or confidence review.
원문 언어: 영어
Set up a reproducible Feynman compute environment for research jobs. Use when a task needs Python/R packages, GPU libraries, containers, Modal, SSH, caches, or managed model runtime setup.
원문 언어: 영어
Configure Feynman specialists, skills, connectors, permissions, memory categories, compute providers, and project setup. Use when the task asks to customize the research workbench or create a reusable Feynman research capability.
원문 언어: 영어
Run or plan DiffDock molecular docking workflows. Use when a task asks for protein-ligand pose prediction, docking setup, ligand/protein preparation, pose ranking, or docking-result verification.
원문 언어: 영어
Predict quick protein structures with ESMFold-style workflows. Use when a task needs fast MSA-free folding, sequence triage, variant structure screening, or confidence review.
원문 언어: 영어
Use Evo2-style biological sequence models for generation, scoring, or variant-effect analysis. Use when a task asks about DNA/RNA/protein sequence likelihood, editing, design, or long-context biological modeling.
원문 언어: 영어
Use ESM2 protein language models for embeddings, mutation scoring, remote homology, or representation analysis. Use when a task needs protein embeddings, zero-shot variant scores, clustering, or sequence-function triage.
원문 언어: 영어
Compose a publication-grade multi-panel scientific figure from a claim, dataset, or draft result. Use when the task needs panel planning, consistent figure layout, figure review, or final figure assembly.
원문 언어: 영어
Apply scientific plotting and figure-quality rules to a single plot or panel. Use when drawing, cleaning, labeling, or reviewing plots for research artifacts.
원문 언어: 영어
Build a source-backed biomedical indication dossier. Use when a research task asks for disease biology, target rationale, patient segmentation, biomarkers, trials, drugs, competitive landscape, or translational evidence.
원문 언어: 영어
Design protein sequences around ligand or small-molecule contexts with LigandMPNN-style workflows. Use when a task asks for ligand-aware protein design, residue redesign, constraints, or design ranking.
원문 언어: 영어
Register or audit Feynman-managed model endpoints. Use when a research workflow needs a local or remote model service, endpoint health checks, credential refs, startup scripts, or inference routing.
원문 언어: 영어
Run or plan OpenFold3-style structure prediction workflows. Use when a task asks for open protein or complex prediction, setup, model comparison, or reproducibility around OpenFold-family outputs.
원문 언어: 영어
Shape the scientific story across a manuscript, abstract, figures, and evidence. Use when a task asks for paper structure, figure order, argument flow, missing analyses, or manuscript revision strategy.
원문 언어: 영어
Read, extract, and cross-check content across scientific PDFs. Use when a task needs methods, figures, tables, citations, accessions, or claims from multiple places in one or more papers.
원문 언어: 영어
Answer questions about Feynman's own product behavior, runtime, settings, commands, skills, connectors, and package state. Use when a response would claim what Feynman can do or how it is wired.
원문 언어: 영어
Design protein sequences from fixed backbone structures with ProteinMPNN-style workflows. Use when a task asks for backbone-conditioned sequence design, mutation suggestions, fixed residues, or design filtering.
원문 언어: 영어
Dispatch Feynman research notebook or experiment jobs to Modal. Use when a task has explicitly chosen Modal for bounded cloud compute, GPU jobs, or reproducible remote execution.
원문 언어: 영어
Run Feynman research jobs on SSH, Slurm, or lab hosts. Use when a task needs remote host setup, job submission, log harvest, artifact sync, or GPU/cluster execution outside Modal.
원문 언어: 영어
Use scGPT-style single-cell foundation model workflows. Use when a task asks for single-cell embeddings, perturbation prediction, cell annotation, batch transfer, or gene-program analysis.
원문 언어: 영어
Run scvi-tools single-cell workflows. Use when a task asks for scVI/scANVI setup, latent embeddings, batch correction, differential expression, cell annotation, or reproducible AnnData analysis.
원문 언어: 영어
Inspect the active Feynman workbench session, artifacts, execution log, settings, and provenance. Use when the task asks what happened in this session, which files were written, what tools ran, or what remains unverified.
원문 언어: 영어
Create or revise Feynman skills. Use when a research workflow needs a reusable on-demand capability, skill metadata, trigger wording, references, scripts, or skill validation.
원문 언어: 영어
Design or screen protein sequences for solubility-aware constraints with SolubleMPNN-style workflows. Use when a task asks for soluble protein design, expression-friendly variants, or solubility risk filtering.
원문 언어: 영어
Call a configured Feynman model endpoint and interpret its response. Use when a task needs inference from a registered endpoint, remote model API, local model service, or custom connector-backed predictor.
원문 언어: 영어
Search, read, and query research papers via Feynman's alphaXiv-backed alpha tools. Use when the user asks about academic papers, wants to find research on a topic, needs to read a specific paper, ask questions about a paper, inspect a paper's code repository,…
원문 언어: 영어
Bounded research experiment loop that tries hypotheses, measures benchmark evidence, keeps what works, and records what fails. Use when the user asks to optimize a research metric, run an experiment loop, improve model/retrieval/evaluation performance…
원문 언어: 영어
Execute research code inside isolated Docker containers for safe replication, experiments, and benchmarks. Use when the user selects Docker as the execution environment or asks to run code safely, in isolation, or in a sandbox.
원문 언어: 영어
Explain research, papers, or technical ideas in plain English with minimal jargon, concrete analogies, and clear takeaways. Use when the user says "ELI5 this", asks for a simple explanation of a paper or research result, wants jargon removed, or asks what…
원문 언어: 영어
Inspect visible research run state, scheduled research follow-ups when available, and durable watch artifacts. Use when the user asks what's running for a research workflow or wants research-run status.
원문 언어: 영어
Run explicitly chosen research benchmark or replication jobs on Modal's serverless infrastructure. Use when a Feynman research workflow needs burst remote GPU compute and the Modal CLI is available.
원문 언어: 영어
Preview Markdown, LaTeX, PDF, or code artifacts when preview commands are visible, or fall back to shell/browser tools. Use when the user wants to review a written artifact, export a report, or view a rendered document.
원문 언어: 영어
Plan a replication of a paper, claim, or benchmark, and execute only after an explicit environment choice. Use when the user asks to replicate results, reproduce an experiment, verify a claim empirically, or build a replication package.
원문 언어: 영어
Run a tough but constructive internal research critique of an AI research artifact. Use when the user asks for a review, critique, feedback on a paper or draft, or wants to identify weaknesses before submission.
원문 언어: 영어
Provision and manage GPU pods on RunPod for explicitly chosen long-running research experiments. Use when a Feynman replication, benchmark, or dataset-heavy research run needs persistent GPU compute with SSH access.
원문 언어: 영어
Recover prior Feynman work from session transcripts. Use the optional /search command only when it is installed and visible; otherwise search local session JSONL files directly.
원문 언어: 영어
Create a research watch baseline and optionally schedule follow-up checks when scheduling tools are visible. Use when the user asks to monitor a field, track new papers, watch for updates, or set up alerts on a research area.
원문 언어: 영어