「sequence-design」の検索結果

Use when the user asks to "build a win-back campaign", "re-engage lapsed subscribers", "run a re-permission / re-consent sweep", or "sunset my dead list"; produces a closed-loop reactivation program — a lapsed-cohort definition, a staged offer ladder, a re-consent (re-permission) capture step, and a sunset-confirm / suppression rule. Owns none of the SEND-N sub-item notes: engagement-decay / sunset is email-sequence-designer's and preference-center / frequency options is preference-frequency-manager's — this skill references those notes, it does not re-emit them. Not for the general lifecycle flows (welcome/cart/post-purchase) — use email-sequence-designer; not for the preference-center / opt-down ladder — use preference-frequency-manager; not for the consent record itself — use consent-registry; not for computing EQS or the N1 veto — use email-quality-auditor. 流失召回/重新授权/沉默用户清理

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。

Use when the user asks to "build a win-back campaign", "re-engage lapsed subscribers", "run a re-permission / re-consent sweep", or "sunset my dead list"; produces a closed-loop reactivation program — a lapsed-cohort definition, a staged offer ladder, a re-consent (re-permission) capture step, and a sunset-confirm / suppression rule. Owns none of the SEND-N sub-item notes: engagement-decay / sunset is email-sequence-designer's and preference-center / frequency options is preference-frequency-manager's — this skill references those notes, it does not re-emit them. Not for the general lifecycle flows (welcome/cart/post-purchase) — use email-sequence-designer; not for the preference-center / opt-down ladder — use preference-frequency-manager; not for the consent record itself — use consent-registry; not for computing EQS or the N1 veto — use email-quality-auditor. 流失召回/重新授权/沉默用户清理

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。

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.

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。

Use when the user asks to "design a welcome flow", "set up an abandoned-cart sequence", "build a light re-engagement branch inside a lifecycle flow", or "plan a cold-outbound sequence"; produces general lifecycle automation flows (welcome, cart, browse-abandon, post-purchase, in-flow re-engagement, B2B cold outbound) with trigger, step timing, branch/exit conditions, goal, frequency governance (send caps, quiet hours, fatigue guardrail), a sunset path, and a SEND N-dimension score. Not for the closed-loop win-back / re-consent (re-permission) program on a lapsed cohort — use reactivation-specialist; not for writing each email's copy — use email-creative-builder; not for computing EQS or the N1 unsubscribe veto — use email-quality-auditor. 邮件自动化流程设计/购物车挽回/流失召回序列

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。

Use when the user asks to "write the email", "draft subject lines", or "build email creative"; produces the pre-click unit — subject-line variants + preheader, body copy, one clear CTA, and a plain-text alt — message-matched to the destination page and claims-ledger-aware. Not for pre-scoring or ranking subject-line variants (spam/truncation/render pre-score) — use subject-line-lab; not for scoring the email or computing EQS — use email-quality-auditor; not for the multi-step flow — use email-sequence-designer; not for the A/B test plan — use send-experiment-designer. 邮件文案/主题行/邮件创意

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。

Use when the user asks to "build a preference center", "set up a frequency opt-down ladder", "give people a step-down instead of unsubscribe", or "design a topic/cadence preference page"; produces a preference-center field spec, a frequency/topic opt-down ladder (down-tier paths that substitute for a hard unsubscribe), a preference-to-suppression mapping, and a SEND N-dimension sub-item note on preference-center / frequency options offered. Not for the lifecycle flow map or cadence governance — use email-sequence-designer; not for the consent/suppression record itself — use consent-registry; not for computing EQS or ruling the N1 unsubscribe veto — use email-quality-auditor. 邮件偏好中心/降频阶梯设计/退订替代降档

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。

Use when the user asks to "audit an email program" or "is this campaign safe to send"; runs a typed 20-item SEND profile with authentication, consent, opt-out, and claim veto checks on own evidence. Not for building deliverability setup — use deliverability-qa; not for designing lifecycle flows — use email-sequence-designer. 邮件质量审计/EQS评分/发送前放行

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。

Use when the user asks to "build a B2B cold-outbound sequence", "design reply-triage branching", "plan a domain warmup / sending throttle", or "make my outbound CAN-SPAM / opt-in compliant"; produces a multi-step outbound sequence with reply-triage branches (positive / objection / referral / not-now / opt-out), a warmup + send-throttle ramp schedule, jurisdiction opt-in/CAN-SPAM guardrails (guidance, not legal advice), and a SEND S-dimension read. Not for B2C lifecycle flows — use email-sequence-designer; not for the consent record — use consent-registry; not for computing EQS — use email-quality-auditor. B2B冷启动外联序列/回复分流/域名预热

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。

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。

Design a Concrete-Pictorial-Abstract learning sequence for a mathematical concept using manipulatives. Use when teaching maths through Singapore method or when students struggle with abstraction.

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。

Design a sequence where students compare AI's handling of the same question across disciplines, developing a mental model of where AI is reliable vs. distorting based on knowledge type.

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。

Generate a cascading hint sequence for a problem type, revealing progressively without giving answers. Use when designing tutoring dialogues or scaffolded worksheets.

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。

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.

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。

Design a structured outdoor learning sequence embedding curriculum objectives in an available outdoor space. Use when planning lessons in school grounds, parks, or local natural environments.

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。

Design a scope and sequence showing vertical and horizontal curriculum coherence across a programme or year. Use when building new programmes, restructuring subjects, or ensuring progression.

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。

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.

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。

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.

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。

Ligand-aware protein sequence design using LigandMPNN. Use this skill when: (1) Designing sequences around small molecules, (2) Enzyme active site design, (3) Ligand binding pocket optimization, (4) Metal coordination site design, (5) Cofactor binding proteins. For standard protein design, use proteinmpnn. For solubility optimization, use solublempnn.

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。

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.

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。

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.

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。

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.

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。

ESM2 protein language model for embeddings and sequence scoring. 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 chai or boltz. For QC thresholds, use protein-qc.

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。

Design a structured outdoor learning sequence embedding curriculum objectives in an available outdoor space. Use when planning lessons in school grounds, parks, or local natural environments.

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。

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.

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。