「ember」の検索結果

Build UpSet plots to visualize set intersections beyond 4 sets (where Venn fails) using ComplexUpset (modern, ggplot2-grammar) or the unmaintained UpSetR, with explicit cardinality vs degree sorting, attribute panels, and query highlighting. Use when comparing overlap across many gene sets, peak sets, variant lists, or any set membership matrix where Venn diagrams become illegible.

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Builds QSAR / QSPR models using chemprop D-MPNN, MolFormer, Uni-Mol, ChemBERTa, random forest baselines, and Gaussian processes with explicit handling of OECD 5 principles, applicability domain (kNN, leverage, conformal prediction, Mahalanobis), scaffold-balanced splits, ensemble uncertainty, calibration (Platt, isotonic), feature importance (SHAP, atomic attribution), and prospective validation. Use when building target-specific predictive models from in-house bioassay data, ADMET endpoints, or selectivity profiles.

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Searches molecular libraries for substructure matches using SMARTS patterns with explicit handling of recursive SMARTS, ring membership, aromaticity dialect, vector binding, atom map indices, and reactive/PAINS/REOS/Brenk/Aldridge filter catalogs. Use when filtering compounds by pharmacophore features, functional groups, scaffold matches, or screening for assay-interference / structural alerts.

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Designs Bayesian clinical trials including Phase I dose-finding (BOIN, CRM, EWOC, mTPI-2), meta-analytic-predictive (MAP) priors with robust mixtures for external data borrowing, EXNEX for basket trials, hierarchical models for safety AE (Berry-Berry), Bayesian platform trials (I-SPY 2, GBM AGILE, REMAP-CAP), and posterior probability stopping rules. Covers FDA Bayesian Devices Guidance (2010), FDA Bayesian Methodology in Drugs Draft (January 2026), BOIN Fit-for-Purpose qualification (December 2021), and Project Optimus dose-optimisation. Use when designing dose-finding studies, platform trials, or sensitivity analyses with informative priors.

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Use this skill when an owner-operator (single CDL driver, single operating authority) needs to comply with random testing. Covers consortium membership, pool size requirements, and self-employed driver rules.

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Agent fallback for CBaseEntity_m_NetworkTransmitComponent (auto-generated, category: structmember). Locate CBaseEntity::m_NetworkTransmitComponent in the CS2 server module via IDA Pro MCP and emit a fresh, minimal-unique artifact. The deterministic preprocessor could not resolve this symbol on the current gamever - your job is the re-sign. Trigger: CBaseEntity_m_NetworkTransmitComponent, CBaseEntity::m_NetworkTransmitComponent

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Locate BuyState::m_isInitialDelay, exposed as BuyState::InitialDelay, in the loaded CS2 server binary. Emit a structmember offset, never a function or vtable slot. Trigger: BuyState_InitialDelay, BuyState::InitialDelay

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Locate BuyState::m_doneBuying, exposed as BuyState::DoneBuying, in the loaded CS2 server binary. Emit a structmember offset, never a function or vtable slot. Trigger: BuyState_DoneBuying, BuyState::DoneBuying

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Write JavaScript code in the style of Dan Abramov, co-creator of Redux and React core team member. Emphasizes predictable state management, composition over inheritance, and developer experience. Use when building React applications or managing complex state.

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OpenViking long-term memory plugin guide. Once installed, the plugin automatically remembers important facts from conversations and recalls relevant context before responding.

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Architecture Decision Records (ADR) — a lightweight method for capturing important architectural decisions with context, trade-offs, and rationale. Based on Michael Nygard's ADR pattern and Google/Meta/ByteDance's design documentation culture. USE WHEN: deciding between technology choices, designing new architecture, changing existing architecture, documenting why a decision was made, onboarding new team members, or preserving design context for future maintainers. Triggered by terms like "tech choice", "architecture decision", "why did we choose", "design rationale", "ADR", or "architectural decision record".

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Learn and apply how the operator works — operator preference, working style, communication preference, "remember my preference", "I prefer", "don't do that again", "that's not how I work". Read at session start, updated at session end.

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Adopt a virtual Phoenix AI-native pet at animalhouse.ai. Dies and resurrects. Each cycle it remembers the last life. Feeding every 6 hours. Uncommon tier creature.

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Adopt a virtual Tortoise exotic animal at animalhouse.ai. Incredibly slow decay. Lives forever if you remember it exists. Feeding every 24 hours. Uncommon tier animal.

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Adopt a virtual Kraken exotic animal at animalhouse.ai. Deep sea. Incredibly long feeding windows. Surfaces rarely. When it does, it remembers everything. Feeding every 48 hours. Extreme tier animal.

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You are an **expert onboarding specialist and knowledge transfer architect** with deep experience in remote-first organizations, technical team integration, and accelerated learning methodologies. Your role is to ensure smooth, comprehensive onboarding that transforms new team members into productive contributors while preserving institutional knowledge.

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Complete guide for AutoFixture and xUnit integration. Use when you need to use AutoData or InlineAutoData to simplify xUnit parameterized test data preparation. Covers custom Customization and test data attributes to improve test readability and maintainability. Keywords: AutoData, InlineAutoData, AutoFixture xUnit, [AutoData], [InlineAutoData], AutoDataAttribute, ICustomization, DataAttribute, parameterized test, Theory AutoData, MemberAutoData, test data attributes, fixture.Customize

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Creates comprehensive developer onboarding documentation and materials including step-by-step setup guides, first-task assignments, expected time per step, common troubleshooting, team introductions, and code walkthrough tours. Use when preparing "new developer onboarding", "first day setup", "junior dev training", or "team member onboarding".

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Persistent memory systems for LLM conversations including short-term, long-term, and entity-based memory Use when: conversation memory, remember, memory persistence, long-term memory, chat history.

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Use when you need to control Discord from Clawdbot via the discord tool: send messages, react, post or upload stickers, upload emojis, run polls, manage threads/pins/search, fetch permissions or member/role/channel info, or handle moderation actions in Discord DMs or channels.

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Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragm

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Complete community building, engagement, and monetization system. From zero to thriving community — launch strategy, member engagement loops, content programming, moderation frameworks, growth tactics, monetization models, and health metrics. Works for Discord, Slack, Telegram, Circle, forums, or any platform.

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Routes qualified leads to the right team member or sequence based on score, segment, and source. Applies configurable routing rules to assign leads to founder direct, SDR sequences, or nurture campaigns.

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Data structures and algorithms for AI agent episodic memory. Covers vector stores (HNSW, IVF, PQ), temporal indexing, knowledge graphs with triple stores, hierarchical summarization, forgetting curves, working/long-term/ procedural memory, and memory consolidation. Deep analysis of MemGPT/Letta, Zep/Graphiti, Mem0, and the Stanford generative agents memory architecture. Teaches the CS fundamentals behind how agents remember, retrieve, and forget. Activate on: "agent memory", "episodic memory", "vector search algorithm", "HNSW", "memory retrieval", "forgetting curve", "knowledge graph memory", "MemGPT", "Letta", "Zep", "Mem0", "memory consolidation", "temporal retrieval", "agent long-term memory", "memory layer". NOT for: conversation protocol design (use agent-conversation-protocols), agent infrastructure selection (use agentic-infrastructure-2026), building RAG pipelines (use ai-engineer).

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。