「sequence」の検索結果

Think and work like an expert Molecular Geneticist. Use when a task calls for Molecular Geneticist judgment. Reasons from sequence-as-hypothesis, reference context (genome build, MANE/RefSeq transcript, HGVS), and allele-level molecular consequence through ACMG/AMP-ClinGen criteria, IGV/VEP/SpliceAI/gnomAD/ClinVar review, MIQE-compliant qPCR/ddPCR, and Sanger/NGS validation while treating allele dropout, pseudogene/paralog misalignment, FFPE deamination, contamination and barcode bleed, and transcript/build mismatch as first-class failure modes.

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Read and write compressed sequence files (gzip, bzip2, BGZF) using Biopython. Use when working with .gz or .bz2 sequence files. Use BGZF for indexable compressed files.

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Process multiple sequence files in batch using Biopython. Use when working with many files, merging/splitting sequences, or automating file operations across directories.

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Query UniProt protein database. Use when user asks about protein sequences, functions, annotations, domains, or protein identifiers. Triggers on "uniprot", "protein function", "protein sequence", "gene product", "protein info".

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Proteina-Complexa flow-based protein backbone generation with fold-conditioned sampling guidance. Use this skill when: (1) Generating de novo protein backbones with hierarchical fold conditioning, (2) Exploring long-chain backbone generation beyond standard diffusion baselines, (3) Using NVIDIA Proteina-style flow matching workflows for controllable backbone design, (4) Comparing flow-based backbone generation against RFdiffusion or BoltzGen, (5) Prototyping fold-guided backbone campaigns before sequence design. This skill is based on the public NVIDIA Digital Bio Proteina project and uses "Proteina-Complexa" as the BioClaw-facing skill label. For sequence design after backbone generation, use proteinmpnn or solublempnn. For QC thresholds, use protein-design-qc.

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Workflow for foundational sequence parsing, conversion, compression handling, and interval-aware file validation.

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Review SDS-PAGE or protein purification gel images using DNA sequence, protein sequence, base-pair length, expected protein size, and lane labels. Use when the user wants to judge whether a gel ran well, whether the main band matches the expected product, or whether there may be impurities, degradation, aggregation, or low expression.

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End-to-end binder design using BindCraft hallucination. Use this skill when: (1) Designing protein binders with built-in AF2 validation, (2) Running production-quality binder campaigns, (3) Using different design protocols (fast, default, slow), (4) Need joint backbone and sequence optimization, (5) Want high experimental success rate. For backbone-only generation, use rfdiffusion. For QC thresholds, use protein-qc. For tool selection guidance, use binder-design.

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Structure similarity search with Foldseek. Use this skill when: (1) Finding similar structures in PDB/AFDB databases, (2) Structural homology search, (3) Database queries by 3D structure, (4) Finding remote homologs not detected by sequence, (5) Clustering structures by similarity. For sequence similarity, use uniprot BLAST. For structure prediction, use chai or boltz.

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AlphaFold2 / AlphaFold-Multimer structure prediction for validation and confidence scoring. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction with AlphaFold-Multimer. For faster single-chain prediction, use esm2-sequence-scoring. For QC thresholds, use protein-design-qc.

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Solubility-optimized protein sequence design using SolubleMPNN. Use this skill when: (1) Designing for E. coli expression, (2) Optimizing solubility of designed proteins, (3) Reducing aggregation propensity, (4) Need high-yield expression, (5) Avoiding inclusion body formation. For standard design, use proteinmpnn. For ligand-aware design, use ligandmpnn.

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All-atom protein design using BoltzGen diffusion model. Use this skill when: (1) Need side-chain aware design from the start, (2) Designing around small molecules or ligands, (3) Want all-atom diffusion (not just backbone), (4) Require precise binding geometries, (5) Using YAML-based configuration. For backbone-only generation, use rfdiffusion. For sequence-only design, use proteinmpnn. For structure validation, use boltz.

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Kit (formerly ConvertKit) API integration with managed OAuth. Manage email subscribers, forms, tags, sequences, broadcasts, and custom fields. Use this skill when users want to manage their email marketing lists, create or update subscribers, manage tags, or work with email sequences and broadcasts. For other third party apps, use the api-gateway skill (https://clawhub.ai/byungkyu/api-gateway).

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Generate Mermaid architecture diagrams from your codebase. Produces C4 context and container diagrams, sequence diagrams for key flows, ER diagrams from database schemas, and module dependency graphs. Use when you need architecture diagrams, system diagrams, database ERD, sequence diagrams, dependency visualization, or C4 model diagrams.

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Generate Simulink Test Sequence blocks and harnesses from EARS requirements. Use when asked to create a test case from a requirement, generate tests from a .slreqx file, or verify a Simulink model with requirement-based tests.

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Apply to multi-step work (sweeps, migrations, runs of similar edits) and to how you stack commits and PRs. Break work into small units that each end in a verifiable state, check each before the next, and order delivery so the sequence proves itself to a reviewer.

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Cold email and outreach sequence design with personalization, deliverability optimization, follow-up cadence, and compliance (CAN-SPAM/GDPR). Use when the user asks about cold email, outreach sequences, email prospecting, cold outreach, or email deliverability for outbound.

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This skill should be used when the user asks to "cut to the beat", "fix the pacing", "edit this sequence", "sync cuts to music/BPM", "add a match cut / J-cut / L-cut", "build a speed ramp", or "plan a shot sequence / edit timeline". Covers editing rhythm, transitions, retiming, and translating an edit plan to an NLE or a code timeline (Remotion/AE frames).

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Generate sales outreach sequences, proposals, SOWs, investor pitch decks, and RFP responses. Covers cold email sequences, proposal structure, pricing tiers (Good-Better-Best), scope of work with exclusions, and 10-slide pitch deck.

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シミュレーションと数理最適化（モンテカルロ / Monte Carlo / 離散事象シミュレーション / DES / 待ち行列 / リスク分析 / シナリオ分析、線形計画 / LP / 整数計画 / MIP / スケジューリング / 配送計画 / VRP / 割当 / メタヒューリスティクス）を 実行したら必ずセットで出す図と値のルーター。SeedSequence.spawn, scipy.stats の rvs, simpy, SALib, pulp, LpProblem, mip.Model, ortools, pywraplp, cp_model, pyomo, cvxpy, linprog, milp がコードに現れたとき、または ユーザーが「シミュレーションして」「モンテカルロで見積もって」「待ち時間をシミュレートして」「最適化して」 「最適な割当を求めて」「スケジュールを組んで」と言ったときに使う。収束・標準誤差・ソルバー status・制約違反・ 感度分析に言及がなくても適用する。SKILL.md のルーティング表で手法を特定し、対応する references/<手法>.md を 読んでから実行する。MCMC は statistical-inference-diagnostics、ML のハイパラ探索は predictive-modeling-diagnostics を使う。

Design a mastery experience sequence that systematically builds student confidence in a skill they avoid. Use when students say 'I can't do this', avoid tasks, or show learned helplessness.

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Generate a Socratic questioning sequence that develops conceptual understanding through guided inquiry. Use when facilitating philosophical discussions, concept exploration, or critical examination.

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Design a learning sequence teaching prompt quality — comparing vague vs. refined prompts to show why specificity and context transform AI output. Use when students use AI without understanding why output quality varies.

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Generate a scaffolded sequence of practice problems with graduated difficulty and strategic variability. Use when creating worksheets, homework sets, or independent practice materials.

日本語の概要は準備中です。原文の説明を表示しています。