12건 · 관련도순
Binder design tool selection and workflow routing guidance. Use this skill when: (1) Deciding between BoltzGen, BindCraft, or RFdiffusion, (2) Planning a binder design campaign, (3) Understanding trade-offs between different approaches, (4) Selecting tools for specific target types. For specific tool parameters, use the individual tool skills (boltzgen, bindcraft, rfdiffusion, etc.).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Guidance for choosing the right protein binder design tool. Use this skill when: (1) Deciding between BoltzGen, BindCraft, or RFdiffusion, (2) Planning a binder design campaign, (3) Understanding trade-offs between different approaches, (4) Selecting tools for specific target types. For specific tool parameters, use the individual tool skills (boltzgen, bindcraft, rfdiffusion, etc.).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Map which elements of an assignment benefit from AI assistance vs. which AI use undermines. Use when redesigning tasks for AI-age classrooms or setting defensible AI use policies for specific assignments.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Guidance for choosing the right protein binder design tool. Use this skill when: (1) Deciding between BoltzGen, BindCraft, or RFdiffusion, (2) Planning a binder design campaign, (3) Understanding trade-offs between different approaches, (4) Selecting tools for specific target types. For specific tool parameters, use the individual tool skills (boltzgen, bindcraft, rfdiffusion, etc.).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
End-to-end protein design pipeline guide across preparation, generation, validation, and filtering. Use this skill when: (1) Starting a new protein design project, (2) Need step-by-step workflow guidance, (3) Understanding the full design pipeline, (4) Planning compute resources and timelines, (5) Integrating multiple design tools. For tool selection, use binder-design-tool-selection. For QC thresholds, use protein-design-qc.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Map which elements of an assignment benefit from AI assistance vs. which AI use undermines. Use when redesigning tasks for AI-age classrooms or setting defensible AI use policies for specific assignments.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Run a Meta-Harness-style optimization loop NATIVELY — automatically search over the scaffolding around a FIXED base model (memory, retrieval, context construction, prompt templates, summarization, tool-selection logic) by proposing candidate variants, scoring each on a cheap deterministic eval, and keeping a Pareto frontier of quality vs cost — using native Agent / Workflow / loop tools instead of a standalone Python harness. Use this whenever the user wants to optimize, evolve, tune, distill, or search over a harness, scaffold, prompt system, memory or retrieval policy, context-assembly code, or summarizer while keeping the model fixed; whenever they mention Meta-Harness, harness optimization, scaffold evolution, automatic prompt/memory optimization, an evolutionary or Pareto search over candidate implementations, or "make the harness/agent better without retraining"; and whenever the gain must come from the code AROUND the model rather than the model weights. Reproduces the Meta-Harness paper's method natively, with no claude_wrapper.py and no metered solver API.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when deciding whether Salesforce Reports, CRM Analytics, or Tableau is the right level of analytics tooling, and when reviewing or troubleshooting basic CRM Analytics designs. Triggers: 'CRM Analytics', 'Einstein Analytics', 'Tableau CRM', 'lens', 'dataset', 'dataflow', 'analytics dashboard', 'license requirement'. NOT for a detailed CRM Analytics vs Tableau licensing and platform comparison — use architect/crm-analytics-vs-tableau-decision. NOT for building the app, lenses and datasets — use admin/crm-analytics-app-creation.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Run a Meta-Harness-style optimization loop NATIVELY — automatically search over the scaffolding around a FIXED base model (memory, retrieval, context construction, prompt templates, summarization, tool-selection logic) by proposing candidate variants, scoring each on a cheap deterministic eval, and keeping a Pareto frontier of quality vs cost — using native Agent / Workflow / loop tools instead of a standalone Python harness. Use this whenever the user wants to optimize, evolve, tune, distill, or search over a harness, scaffold, prompt system, memory or retrieval policy, context-assembly code, or summarizer while keeping the model fixed; whenever they mention Meta-Harness, harness optimization, scaffold evolution, automatic prompt/memory optimization, an evolutionary or Pareto search over candidate implementations, or "make the harness/agent better without retraining"; and whenever the gain must come from the code AROUND the model rather than the model weights. Reproduces the Meta-Harness paper's method natively, with no claude_wrapper.py and no metered solver API.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Guidance for choosing the right protein binder design tool. Use this skill when: (1) Deciding between BoltzGen, BindCraft, or RFdiffusion, (2) Planning a binder design campaign, (3) Understanding trade-offs between different approaches, (4) Selecting tools for specific target types. For specific tool parameters, use the individual tool skills (boltzgen, bindcraft, rfdiffusion, etc.).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Select established CTF and security-research tools for authorized artifact triage, web inspection, reversing, crypto, forensics, networking, and exploit validation. Use when a user provides a CTF-style tool catalog, asks which tools to use, or needs safe usage instructions for common offensive/security tools.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when evaluating MCP-enabled agents, tool-using LLM systems, multi-server workflows, tool-selection quality, trajectory quality, task completion, or MCP security regressions.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.