esm-protein-language-model
Protein language models (ESM3, ESM C) for sequence generation, structure prediction, inverse folding, and embeddings. Design novel proteins, extract ML features, or fold sequences. Local GPU or Evolut…
Browse curated skills with source links, package snapshots, README assets and install signals in one calm, searchable catalog.
Protein language models (ESM3, ESM C) for sequence generation, structure prediction, inverse folding, and embeddings. Design novel proteins, extract ML features, or fold sequences. Local GPU or Evolut…
Parse/write FCS (Flow Cytometry) files v2.0-3.1. Events as NumPy, channel metadata, multi-dataset files, CSV/FCS export. Use FlowKit for gating/compensation.
WSI processing for digital pathology. Tissue detection, tile extraction (random, grid, score-based), filter pipelines for H&E/IHC. For dataset prep, tile-based DL, slide QC. Use pathml for multiplexed…
Generate exactly one child hypothesis that improves logical coherence, causal consistency, and assumption hygiene.
Generate exactly one grounded child hypothesis by strengthening evidence, specificity, and literature support.
Generate exactly one child hypothesis that preserves the core idea while reducing unnecessary complexity.
Generate exactly one hypothesis candidate through a structured scientific debate.
Run the initial review gate for a hypothesis.
Experiment Idea Planner
Introduction to literature search & review skills - systematic paper finding, screening, extraction, and citation traversal
Run the assumption decomposition and deep verification review for a hypothesis.
Generate exactly one child hypothesis by combining complementary strengths from multiple parent hypotheses.
Generate exactly one cross-parent child hypothesis by transferring a useful principle from one parent context into another.
Run the full literature-grounded review for a hypothesis.
Generate exactly one literature-grounded hypothesis candidate for the active round.
Evaluate a hypothesis against prior observations.
Figure Caption Reader
GitHub Research Maintainer
Run the iterative evolution loop from the current persisted run state until convergence.
Generate exactly one child hypothesis that is more experimentally and operationally feasible than its parent.
Generate exactly one divergent but still testable child hypothesis that challenges the shared assumptions of the parent set.
Generate exactly one hypothesis candidate by enumerating and combining testable assumptions.
Structured hypothesis formulation from observations. Use when you have experimental observations or data and need to formulate testable hypotheses with predictions, propose mechanisms, and design expe…
Judge one placement-tournament matchup between a candidate hypothesis and one opponent.