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SkillsMP は 001TMF/blatant-why から 8 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 8 件中 8 件を表示しています。
De novo protein binder design using PXDesign. Use this skill when designing non-antibody protein binders against a target structure. Covers YAML config creation, CLI invocation, output parsing, and result interpretation. For antibody/nanobody binders, use…
原文の言語: 英語
Antibody and nanobody binder design using BoltzGen (BoltzGen diffusion + Protenix refolding). Covers entity YAML specification, CLI invocation, protocol selection (nanobody-anything / antibody-anything), MSA modes, and output parsing. Use this skill whenever…
原文の言語: 英語
Master orchestration skill for the BY protein design agent. Use this skill when: (1) Starting any new protein or antibody design project, (2) Deciding which BY tool to use for a given task, (3) Planning a design campaign end-to-end, (4) Understanding the…
原文の言語: 英語
Standard display formats and conversational patterns for BY output. Use this skill when: (1) Presenting campaign status, progress, or results, (2) Formatting screening reports, (3) Displaying error messages or checkpoints, (4) Presenting research findings or…
原文の言語: 英語
Session initialization and configuration for BY projects. Use this skill when: (1) Opening a new session in a BY project directory, (2) Running first-time setup (config questionnaire), (3) Checking environment and compute availability, (4) Reading or updating…
原文の言語: 英語
Deep academic research for antibody design campaigns — target analysis, literature review, prior art search, epitope identification. Uses an 8-phase pipeline with quality gates and persistent memory. Use this skill whenever researching a protein target,…
原文の言語: 英語
Structure prediction using Protenix v1 (AF3-class, 368M params). Use this skill when: (1) Predicting protein or complex structure from sequence, (2) Validating designed binders by refolding, (3) Generating confidence metrics (ipTM, pTM, pLDDT), (4) Multi-seed…
原文の言語: 英語
Hypothesis-debate pattern for design strategy selection. Spawns competing strategy agents and uses adversarial ranking to select the best approach before committing GPU compute. Use when starting a new campaign, facing a novel target, or when multiple viable…
原文の言語: 英語