1.4천건 · 관련도순
Design protein sequences while accounting for bound small molecules, nucleic acids, or metals. Use LigandMPNN to redesign binding pockets or metal-coordinating sites, and export sequence files alongside PDB structures with the designed sequences applied.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Searches for homologous protein sequences using MMseqs2 (fast, default) or BLAST (comprehensive, fallback). Trigger this whenever the user provides a protein sequence or FASTA file and asks to find homologues, sequence matches, or wants to infer protein function based on sequence similarity, but not when the user wants to infer protein function based on structural similarity.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
When the user wants to create or optimize an email sequence, drip campaign, automated email flow, or lifecycle email program. Also use when the user mentions "email sequence," "drip campaign," "nurture sequence," "onboarding emails," "welcome sequence," "re-engagement emails," "email automation," or "lifecycle emails." For in-app onboarding, see onboarding-cro.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Design and write email campaigns and sequences including onboarding flows, lifecycle campaigns, transactional emails, newsletters, broadcast sends, and launch and announcement emails. Use this skill whenever the user wants to write email copy, plan an email sequence, design an onboarding drip, or set up lifecycle email campaigns. Triggers on email sequence, drip campaign, onboarding email, lifecycle email, welcome email, transactional email, newsletter, email broadcast, launch email, announcement email, nurture sequence, abandoned cart, re-engagement, win-back. Also triggers when planning email automation flows or writing email subject lines for campaigns.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
When the user wants to create or optimize an email sequence, drip campaign, automated email flow, or lifecycle email program. Also use when the user mentions "email sequence," "drip campaign," "nurture sequence," "onboarding emails," "welcome sequence," "re-engagement emails," "email automation," or "lifecycle emails." For in-app onboarding, see onboarding-cro.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Run BLAST sequence similarity searches. Use when the user asks to BLAST a sequence, find similar sequences, identify a gene/protein, or do homology search. Triggers on "blast", "sequence similarity", "homology", "identify sequence".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
ESM2 protein language model for sequence scoring, embeddings, and plausibility checks. Use this skill when: (1) Computing pseudo-log-likelihood (PLL) scores, (2) Getting protein embeddings for clustering, (3) Filtering designs by sequence plausibility, (4) Zero-shot variant effect prediction, (5) Analyzing sequence-function relationships. For structure prediction, use chai1-structure-prediction or boltz-structure-prediction. For QC thresholds, use protein-design-qc.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Design protein sequences using ProteinMPNN inverse folding. Use this skill when: (1) Designing sequences for RFdiffusion backbones, (2) Redesigning existing protein sequences, (3) Fixing specific residues while designing others, (4) Optimizing sequences for expression or stability, (5) Multi-state or negative design. For backbone generation, use rfdiffusion or bindcraft. For ligand-aware design, use ligandmpnn. For solubility optimization, use solublempnn.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
When the user wants to create or optimize an email sequence, drip campaign, automated email flow, or lifecycle email program. Also use when the user mentions "email sequence," "drip campaign," "nurture sequence," "onboarding emails," "welcome sequence," "re-engagement emails," "email automation," or "lifecycle emails." For in-app onboarding, see onboarding-cro.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Protein language models through the EvolutionaryScale `esm` Python SDK. Generate and embed sequences with ESM3 (multimodal sequence, structure and function prompting), extract per-residue and mean-pooled embeddings with ESM C, fold sequences with ESMFold2, and run inference locally or against the Forge and Biohub hosted clients. Use this skill for protein representation learning, variant effect and mutational scanning from likelihoods, sequence generation and inpainting, structure prediction from sequence alone, and embedding features for downstream models. Also trigger on esm, ESM3, ESMC, ESM Cambrian, ESMFold2, `from esm.models`, ESMProtein, GenerationConfig, forge.evolutionaryscale.ai, biohub.ai, or ESM_API_KEY.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
When the user wants to create or optimize an email sequence, drip campaign, automated email flow, or lifecycle email program. Also use when the user mentions "email sequence," "drip campaign," "nurture sequence," "onboarding emails," "welcome sequence," "re-engagement emails," "email automation," or "lifecycle emails." For in-app onboarding, see onboarding-cro.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Write B2B cold emails and follow-up sequences that get replies. Use when the user wants to write cold outreach emails, prospecting emails, cold email campaigns, sales development emails, or SDR emails. Also use when the user mentions "cold outreach," "prospecting email," "outbound email," "email to leads," "reach out to prospects," "sales email," "follow-up email sequence," "nobody's replying to my emails," or "how do I write a cold email." Covers subject lines, opening lines, body copy, CTAs, personalization, and multi-touch follow-up sequences. For warm/lifecycle email sequences, see email-sequence. For sales collateral beyond emails, see sales-enablement.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
When the user wants to create or optimize an email sequence, drip campaign, automated email flow, or lifecycle email program. Also use when the user mentions "email sequence," "drip campaign," "nurture sequence," "onboarding emails," "welcome sequence," "re-engagement emails," "email automation," "lifecycle emails," "trigger-based emails," "email funnel," "email workflow," "what emails should I send," "welcome series," or "email cadence." Use this for any multi-email automated flow. For cold outreach emails, see cold-email. For in-app onboarding, see onboarding-cro.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Think through the consequences of consequences — not just what happens immediately, but what happens next, and next after that, across time. Load when a decision looks obviously good or obviously bad on initial read, when the user is optimising for a short-term outcome that might create a long-term problem, when unintended consequences are a concern, or when deep-thinking diagnoses a second-order frame. Triggers on "what are the downstream effects", "what happens after that", "unintended consequences", "think ahead on this", "long-term vs short-term", or "what comes after that". Based on Howard Marks second-level thinking and Farnam Street mental models. Most powerful for decisions with delayed consequences or systemic effects.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
When the user wants to create or optimize an email sequence, drip campaign, automated email flow, or lifecycle email program. Also use when the user mentions "email sequence," "drip campaign," "nurture sequence," "onboarding emails," "welcome sequence," "re-engagement emails," "email automation," or "lifecycle emails." For in-app onboarding, see onboarding-cro.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Write or refactor Remotion components so timing and full-size layout live on the content itself. Reduce redundant wrappers and organize related scenes or clips on shared Studio timeline rows while preserving timing and layout behavior.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Analyze biological sequences and microbial communities in Python. Process and align DNA, RNA, or proteins, build evolutionary trees, calculate diversity, visualize sample differences, test group differences, and convert common biological file formats.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Build evolutionary trees from related nucleotide or protein sequences using MAFFT with IQ-TREE or FastTree. Review branch support, a measure of evidence for each grouping, and produce tree summaries and optional visualizations.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Use Biohub’s Python SDK to complete protein sequences with ESM3, extract numerical sequence representations with ESMC, and predict all-atom structures with ESMFold2. The skill guides local or hosted inference, input validation, and output checks.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Plan and draft email sequences for onboarding, lead nurturing, launches, or customer return campaigns. Get complete email copy, send timing, behavior-based branches, exit rules, a flow diagram, and A/B testing suggestions with performance targets.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Turn cryptographic code or specifications into sequence diagrams showing who sends each message, which cryptographic operations occur, and where failures stop the exchange. Outputs include Mermaid source, a plain text diagram, and a protocol summary.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Look up structure metadata in RCSB PDB, search for entries by keyword, and retrieve sequences in FASTA, a text format for biological sequences. Receive concise summaries or short sequence previews, with raw output available on request.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Search nucleotide or protein sequences with NCBI BLAST, check job progress, and get compact summaries of the top matches. Use the job ID to retrieve results later, or request raw output saved to a file.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Retrieve protein and nucleotide sequences from NCBI databases using E-utilities. Supports direct accession lookup, CDS translation, gene+organism search, locus lookup, PubMed-linked sequences, patent protein extraction, and organism+length fallback search. Use when you need to fetch biological sequences by accession, gene name, locus tag, PubMed ID, or patent number.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.