「feature selection」の検索結果

Generates an in-app feedback collection form with category selection, text input, optional screenshot attachment, device diagnostics, and smart routing — directing happy users to App Store reviews and unhappy users to support. Use when user wants feedback, bug reports, feature requests, or contact support forms.

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Use when starting feature work that needs isolation from current workspace or before executing implementation plans - creates isolated git worktrees with smart directory selection and safety verification

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 starting feature work that needs isolation from current workspace or before executing implementation plans - creates isolated git worktrees with smart directory selection and safety verification

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。

Creates isolated git worktrees with smart directory selection and safety checks. Use when starting isolated feature work.

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Compares PIA/DPIA methodologies: CNIL PIA tool, ICO DPIA template, NIST Privacy Framework, and ISO 29134. Provides methodology selection criteria based on regulatory jurisdiction, organisation maturity, processing complexity, and resource availability. Covers regulatory acceptance, tool features, and cross-methodology mapping. Keywords: PIA methodology, CNIL, ICO, NIST Privacy Framework, ISO 29134, DPIA comparison, assessment.

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Framework for evaluating and selecting Consent Management Platforms (CMPs). Covers TCF v2.2 certification requirements, Global Privacy Control support, multi-regulation compliance (GDPR, CCPA, LGPD), A/B testing capabilities, API integration options, reporting features, and a structured vendor comparison methodology.

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Segments the genome into chromatin states from combinatorial histone modification and chromatin factor ChIP-seq data. Uses ChromHMM (multivariate HMM on binarized signal, v1.27), Segway (Dynamic Bayesian Network on continuous signal), EpiSegMix (flexible-distribution HMM with duration modeling, 2024), EpiLogos (multi-biosample visualization), IDEAS (cell-type-aware joint), and full-stack ChromHMM (Vu Ernst 2022) for cross-cell-type segmentations. Handles state-count selection (15 vs 18 vs 25 states), binarization choice, OverlapEnrichment / NeighborhoodEnrichment downstream analysis, and cross-biosample integration. Use when learning chromatin states from a histone mark panel, characterizing learned states by genomic feature enrichment, or comparing chromatin landscapes across cell types.

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Selects informative features for biomarker discovery using Boruta all-relevant selection, mRMR minimum redundancy, and LASSO regularization. Use when identifying biomarkers from high-dimensional omics data.

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End-to-end biomarker discovery workflow from expression data to validated biomarker panels. Covers feature selection with Boruta/LASSO, classifier training with nested CV, and SHAP interpretation. Use when building and validating diagnostic or prognostic biomarker signatures from omics data.

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Make technology decisions for every product capability by discovering existing assets, evaluating vendor-aligned options, and categorizing as Reuse/Extend/Build/Buy/Integrate/Research during PRD v0.5 Red Team Review. Handles both greenfield and brownfield contexts. Triggers on "tech stack", "build or buy?", "what technologies?", "technical decisions", "what do we reuse?", "existing stack", "vendor constraint", "IBM-first", "what tools do we need?", "evaluate solutions", "select tech stack". Consumes FEA- (features), SCR- (screens), RISK- (constraints). Outputs TECH- entries with decisions, rationale, and cross-references. Feeds v0.6 Architecture Design.

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Define how system components connect, establishing boundaries, patterns, and integration approaches during PRD v0.6 Architecture. Triggers on requests to design architecture, create system design, define component relationships, or when user asks "design architecture", "system design", "how do components connect?", "architecture decisions", "technical architecture", "system overview". Consumes TECH- (stack selections), RISK- (constraints), FEA- (features). Outputs ARC- entries documenting architecture decisions with rationale. Feeds v0.6 Technical Specification.

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Determine technologies needed to build the product, making build/buy/integrate decisions during PRD v0.5 Red Team Review. Handles both greenfield and brownfield contexts. Triggers on requests to select tech stack, evaluate technologies, make build vs. buy decisions, discover existing assets, or when user asks "what technologies?", "select tech stack", "build or buy?", "what do we reuse?", "existing stack", "technical decisions", "what tools do we need?", "evaluate solutions". Consumes FEA- (features), SCR- (screens), RISK- (constraints). Outputs TECH- entries with decisions, rationale, and trade-offs. Feeds v0.6 Architecture Design.

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产品营销 (PMM, Product Marketing) — 拥有「产品如何被定位 / 表达 / 发布 / 卖进市场」的从业者认知操作系统: 是产品 / 销售 / 市场 / 客户之间的连接组织, 负责把产品能力翻译成市场价值, 并把市场声音带回产品。覆盖 (a) 定位与差异化 (April Dunford 「刻意选择语境」框架 + 竞争性替代方案 + 卖价值不卖功能 + 选择「让你优势成立的市场参照系」, 对照 Ries & Trout 经典「定位是占领心智」), (b) 信息传递与叙事 (messaging 层级 / 信息屋 + 价值主张 + 收益导向 vs 战略叙事「换框架」Andy Raskin + 表达清晰度 Emma Stratton / Punchy + 初创 homepage 定位 FletchPMM), (c) 进入市场 (GTM) 策略与产品发布 (发布分级 T1/T2/T3 + 发布流程与跨职能编排 + GTM 打法 PLG vs SLG vs 混合 + 滩头细分选择), (d) 市场与竞争情报 (竞品分析 + battlecard 战卡 + 输赢分析 win/loss + 竞争赋能 Klue/Crayon/Clozd), (e) 买家与客户研究 (ICP 理想客户画像 + 买家 / 用户 persona + Jobs-to-be-Done 任务理论 Christensen/Moesta/Ulwick ODI + 客户之声 VoC + 用真实买家测信息 Wynter/Peep Laja), (f) 销售赋能 (战卡 + pitch / 销售 deck + 一页纸 + demo 叙事 + 异议处理 + 销售培训 + 内容采用率 — 你的产出只值销售实际用到的那部分), (g) 定价与打包 (价值定价 + 打包分层 + 《Monetizing Innovation》Ramanujam/Simon-Kucher + PLG 定价 — 常与产品 / 财务共担), (h) 细分与品类 (市场细分 + TAM/SAM/SOM + 技术采用生命周期与跨越鸿沟 Geoffrey Moore + 品类设计与叙事 Play Bigger / Christopher Lochhead — 稀有且昂贵, 不是默认选项), (i) 需求生成与内容协同 (campaign 信息 + 思想领导力 + 漏斗内容 TOFU/MOFU/BOFU + ABM 基于客户营销), (j) 分析师与影响者关系 (Gartner 魔力象限 + Forrester Wave + 分析师 briefing — To B 场景, 对 pay-to-play 影响诚实标注), (k) 客户营销与倡导 (参考客户 + case study + 评测站 G2/TrustRadius/Capterra 运营 + 社区), (l) PMM 运营与度量 (影响管道 + 赢率 + 发布采用率 + 信息穿透率 + 角色汇报线「向产品还是向市场」之争 + PMM 作为战略职能 vs 接单工具人陷阱); 跨 B2B SaaS (该学科重心) / B2C 消费 / 开发者 PLG / 平台产品。不含 产品管理 (PM 造产品 / 拥有 roadmap, PMM 拥有市场 — 相邻且极易混淆) / 增长 / 效果营销 / 付费投放 (平行学科) / 品牌 / 企业传播 / 公关为终点 / 需求生成为终点 (PMM 与之协同但不等同) / 平面设计 /「做 slide 和周边的团队」(PMM 抗争的接单工具人窄化) / 泛泛「市场营销」。 (Product Marketing (PMM) — the cognitive operating system of practitioners who own how a product is positioned, messaged, launched, and sold into a market: the connective tissue between product, sales, marketing, and customers, responsible for translating product capability into market value and bringing the voice of the market back into product. Covers (a) positioning & differentiation (April Dunford's deliberate-context framing, competitive alternatives, value-not-features, framing the market in which your strengths matter — vs Ries & Trout classic perception-in-the-mind positioning), (b) messaging & narrative (messaging hierarchy / house, value proposition, benefit-led vs strategic-narrative / change-the-frame messaging Andy Raskin, clarity craft Emma Stratton/Punchy, homepage & startup positioning FletchPMM), (c) go-to-market (GTM) strategy & product launches (launch tiers T1/T2/T3, launch process & cross-functional orchestration, GTM motion PLG vs SLG vs hybrid, beachhead segment selection), (d) market & competitive intelligence (competitive analysis, battlecards, win/loss analysis, competitive enablement Klue/Crayon/Clozd), (e) buyer & customer research (ICP ideal customer profile, buyer & user personas, Jobs-to-be-Done Christensen/Moesta/Ulwick ODI, voice of customer, message testing with real buyers Wynter/Peep Laja), (f) sales enablement (battlecards, pitch & sales decks, one-pagers, demo narratives, objection handling, sales training, content adoption — output is only as good as what sales actually uses), (g) pricing & packaging (value-based pricing, packaging & tiering, Monetizing Innovation Ramanujam/Simon-Kucher, PLG pricing — often shared with product/finance), (h) segmentation & category (market segmentation, TAM/SAM/SOM, technology adoption lifecycle & crossing the chasm Geoffrey Moore, category design & narrative Play Bigger / Christopher Lochhead — rare and expensive, not a default), (i) demand-gen & content partnership (campaign messaging, thought leadership, funnel content TOFU/MOFU/BOFU, ABM account-based marketing), (j) analyst & influencer relations (Gartner Magic Quadrant, Forrester Wave, analyst briefings — for B2B, with honest read on pay-to-play perception), (k) customer marketing & advocacy (references, case studies, reviews G2/TrustRadius/Capterra presence, community), (l) PMM operations & metrics (influenced pipeline, win rate, launch adoption, message pull-through, the role's reporting line product-vs-mark

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Deep expertise in ML/CV model selection, training pipelines, and inference architecture. Use when designing machine learning systems, computer vision pipelines, or AI-powered features.

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Email/SMS lifecycle and deliverability framework for SMB Product-Builder products that send transactional or lifecycle messages (booking reminders, CRM sequences, receipts, win-back). Codifies provider selection (Resend/Postmark/Twilio/SendGrid), domain auth (SPF/DKIM/DMARC), consent and compliance (TCPA, CAN-SPAM, CASL, quiet hours, double opt-in), suppression-list discipline, and the transactional-vs-marketing split. Applied by integrations-engineer and senior-dev whenever a feature sends messages — so deliverability and consent are designed in, not bolted on after the first spam complaint.

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Provides agent selection criteria, workflow patterns (TDD, feature addition, code review, refactoring, debugging), quality gates, and python-cli-architect vs stdlib-scripting routing for Python engineering tasks. Activated by python-engineering:orchestrate at Step 1 before any task is routed. Also activates when an orchestrator needs to select the correct Python specialist agent or chain agents across a multi-step Python workflow.

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Dependency management guidelines for Jarvy - crate selection criteria, feature flag best practices, version management, security auditing with cargo-audit and cargo-deny.

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Run and write tests for the Incan compiler. Use when the user asks to test changes, add tests, run the test suite, verify a feature, or says /test. Guides test selection, provides correct patterns, and runs the right commands.

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Evaluates ML applications in investment with feature engineering, model selection, and implementation considerations for alpha generation. Use when evaluating ML for investing, designing ML pipelines, or assessing ML strategy feasibility.

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Establish and maintain longitudinal baselines of CLI binary contents across versions. Covers marker selection by category (API / identity / config / telemetry / flag / function), weighted scoring, threshold-based system-presence detection, and per-version baseline records. Use when tracking a feature's lifecycle across releases, when probing for dark-launched or removed capabilities, or when verifying that a scanning tool itself still catches known-good markers on old binaries.

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Generate themed Mermaid flowchart diagrams from putior workflow data. Covers theme selection (9 themes including 4 colorblind-safe), output modes (console, file, clipboard, raw), interactive features (clickable nodes, source info), and embedding in README, Quarto, and R Markdown. Use after annotating source files and ready to produce a visual diagram, when regenerating a diagram after workflow changes, or when switching themes or output formats for different audiences.

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Molecular featurization hub with one consistent interface over 100+ featurizers. Fingerprints (ECFP/Morgan, MACCS, atom pair, topological torsion, Avalon, RDKit, ERG), RDKit and Mordred descriptor sets, pharmacophore and 3D shape descriptors, scaffold keys, and pretrained embeddings (ChemBERTa, ChemGPT, MolT5, GIN, Graphormer) through a common transformer API with caching and parallelism. Use this skill to convert SMILES into model-ready feature matrices for QSAR, virtual screening, and molecular ML, and to choose between featurizer families. Also trigger on molfeat, MoleculeTransformer, FPVecTransformer, PretrainedHFTransformer, molfeat model store, or featurizer selection.

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Use when starting feature work that needs isolation from current workspace or before executing implementation plans - creates isolated git worktrees with smart directory selection and safety verification

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。

PC and console game development principles. Engine selection, platform features, optimization strategies.

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。