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このリポジトリの skills
SkillsMP は lyndonkl/claude から 271 件の skill を収集しています。skill を開くとソースと詳細を確認できます。
lyndonkl/claude収集済み skill 271 件中 31 件を表示しています。
Creates concise, decision-ready product specifications (one-pagers and PRDs) that align stakeholders on problem, solution, users, success metrics, and constraints. Use when proposing new features/products, documenting product requirements, creating concise…
原文の言語: 英語
Creates strategic portfolio roadmaps that size and sequence initiative bets across time horizons (H1/H2/H3), balance risk profiles (core/adjacent/transformational), and set clear exit/scale criteria for disciplined resource allocation. Use when managing…
原文の言語: 英語
Conducts blameless postmortems that transform failures into learning opportunities by documenting timelines, quantifying impact, performing root cause analysis (5 Whys, fishbone diagrams), and defining corrective actions with owners and deadlines. Use when…
原文の言語: 英語
Transforms overwhelming backlogs into clear, actionable priorities by mapping items on a 2x2 effort-vs-impact matrix, identifying quick wins (high impact, low effort), big bets, time sinks, and fill-ins. Use when ranking backlogs, deciding what to do first,…
原文の言語: 英語
Identifies, assesses, and prioritizes project risks using probability-times-impact scoring (risk matrix), then assigns owners, mitigation plans, contingencies, and triggers to track risk evolution over the project lifecycle. Use when managing project…
原文の言語: 英語
Guides validation of ideas before full development using pretotyping (fake doors, concierge MVPs, Wizard of Oz) and prototyping at appropriate fidelity (paper, clickable, coded) to test assumptions about demand, pricing, and feasibility. Use when testing…
原文の言語: 英語
Anchors predictions in historical reality by identifying a class of similar past events and using their statistical frequency as a baseline (outside view) before analyzing case-specific details. Use when starting a forecast, establishing base rates, testing…
原文の言語: 英語
Systematically evaluates claims by triangulating sources, rating evidence quality (primary/secondary/tertiary), assessing source credibility, and reaching confidence-rated conclusions to prevent confirmation bias and reliance on unreliable sources. Use when…
原文の言語: 英語
Designs retrieval strategies for querying knowledge graphs in RAG systems, covering pattern selection (global-first, local-first, U-shaped hybrid), query decomposition for multi-hop reasoning, ranking and constraint configuration, and provenance tracking for…
原文の言語: 英語
Facilitates structured team reflection through retrospectives, post-mortems, after-action reviews, and weekly/quarterly reviews, producing root cause analysis and SMART action items with psychological safety. Use when conducting sprint retrospectives, project…
原文の言語: 英語
Plans backward from a fixed goal or deadline to the present, identifying required milestones, dependencies, critical path, and feasibility constraints to transform aspirational targets into actionable sequenced plans. Use when planning with fixed deadlines,…
原文の言語: 英語
Analyzes decisions from multiple stakeholder perspectives (engineering, product, legal, finance, users) to uncover blind spots, surface tensions, and synthesize alignment paths with explicit tradeoffs. Use when stakeholders have conflicting priorities, need…
原文の言語: 英語
Reviews scientific documents for logical clarity, argument soundness, and rigor by auditing hypothesis-data alignment, claim-evidence chains, quantitative precision, hedging calibration, and terminology consistency across any document type. Use when reviewing…
原文の言語: 英語
Composes and polishes professional scientific correspondence -- emails to collaborators, journal cover letters, and responses to peer reviewers -- ensuring clear communication, appropriate tone, explicit asks, and professional formatting for academic…
原文の言語: 英語
Guides systematic multi-pass review and editing of scientific manuscripts (research articles, reviews, perspectives) to improve clarity, structure, scientific rigor, and reader comprehension. Use when reviewing or editing research manuscripts, journal…
原文の言語: 英語
Detects and removes cognitive biases from reasoning using Julia Galef's Scout Mindset framework. Provides reversal tests, scope sensitivity checks, status quo bias tests, confidence interval audits, and full bias audits. Use when a prediction feels emotional,…
原文の言語: 英語
Systematically identifies vulnerabilities, threats, and mitigations for systems handling sensitive data using STRIDE methodology, trust boundary mapping, and defense-in-depth principles. Use when designing or reviewing systems with PII/PHI/financial/auth…
原文の言語: 英語
Guides learners to discover knowledge through strategic Socratic questioning and progressive scaffolding removal. Combines question ladders, misconception detectors, Feynman explanations, and worked-example fading to build durable understanding. Use when…
原文の言語: 英語
Provides frameworks for mapping stakeholder influence networks, designing team structures aligned with system architecture (Conway's Law), defining team interface contracts (APIs, SLAs, decision rights), and assessing capability maturity (DORA, CMMC, agile…
原文の言語: 英語
Develops business strategies grounded in rigorous competitive and market analysis using proven frameworks (Good Strategy kernel, Porter's 5 Forces, SWOT, Blue Ocean, Playing to Win, Value Chain Analysis, BCG Matrix). Use when developing strategy (market…
原文の言語: 英語
Guides collaborative discovery of hidden symmetries in ML data through structured domain analysis, coordinate system examination, transformation testing, and physical constraint identification. No group theory knowledge required. Use when ML engineers need to…
原文の言語: 英語
Maps identified symmetries to mathematical groups (cyclic, dihedral, symmetric, SO(3), SE(3), E(3)) for equivariant neural network architecture design, using taxonomy and foundations from Visual Group Theory. Use when candidate symmetries have been identified…
原文の言語: 英語
Provides empirical test protocols and metrics to validate whether hypothesized symmetries actually hold in data or models before committing to equivariant architecture. Includes invariance/equivariance testing, group structure verification, and distribution…
原文の言語: 英語
Synthesizes information from multiple sources into coherent insights and applies analogical reasoning to transfer knowledge across domains. Use when conducting literature reviews, integrating stakeholder feedback, reconciling conflicting viewpoints,…
原文の言語: 英語
Finds high-leverage intervention points in complex systems by mapping feedback loops, identifying system archetypes (fixes that fail, shifting the burden, tragedy of the commons, limits to growth), and ranking interventions by Meadows' leverage hierarchy. Use…
原文の言語: 英語
Adapts content for different audiences while preserving core accuracy, changing tone, depth, emphasis, and framing to match audience expertise and goals. Use when technical content needs business framing, strategic vision needs tactical translation, expert…
原文の言語: 英語
Transforms data into compelling visual narratives by applying narrative structure, annotation techniques, scrollytelling patterns, and honest framing to data journalism, presentations, and infographics. Use when creating data-driven articles or reports,…
原文の言語: 英語
Matches visualization types to data questions and creates narrated reports that highlight insights and recommend actions. Covers chart selection (comparison, trend, distribution, relationship, composition, geographic), perceptual best practices, and narrative…
原文の言語: 英語
Runs a comprehensive six-section quality checklist (content, structure, clarity, style, polish, final tests) before writing is shared or published, catching issues that revision and stickiness enhancement might miss. Use when performing final quality checks…
原文の言語: 英語
Applies a systematic three-pass revision system (Zinsser, King, Pinker, Clark) to existing drafts — Pass 1 cuts clutter, Pass 2 reduces cognitive load, Pass 3 improves rhythm. Use when revising, editing, or polishing drafts, cutting word count, tightening…
原文の言語: 英語
Applies the Heath brothers' SUCCESs model (Simple, Unexpected, Concrete, Credible, Emotional, Stories) to make messages memorable and persuasive, with systematic analysis, targeted improvements, and scoring (0-18 stickiness scorecard). Use when making…
原文の言語: 英語