Claude

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 articles, or perspectives, when user mentions manuscript, paper draft, article, research writing, journal submission, reviewer feedback, or needs to improve scientific writing.

Classifies each substacker section as healthy / drifting / candidate-for-prune based on post volume, engagement trend, and niche alignment. Produces table + 2-4 paragraph narrative. Used in every quarterly review. Trigger keywords — portfolio, section health, healthy drifting prune, section assessment, which section is carrying.

Checks a finished valuation for internal consistency across its artifacts — currency agreement between cash flows and discount rate, terminal growth against the riskfree ceiling, reinvestment that pays for the growth assumed, perpetual excess returns, capital weights and beta range, equity bridge arithmetic, tax rates, and the hard constraints a company's type imposes. Grades each finding ERROR, WARN or INFO and exits 1 on any error, so it can gate a pipeline. Use before accepting a DCF, when reviewing someone else's model, when a value looks too high, or when checking terminal value assumptions, growth-versus-reinvestment consistency, currency consistency, or an equity bridge that does not tie.

Builds structured abstraction ladders that translate high-level principles into concrete, actionable examples across 3-5 levels. Bridges communication gaps, reveals hidden assumptions, and tests whether abstract ideas work in practice. Use when explaining concepts at different expertise levels, moving between abstract principles and concrete implementation, identifying edge cases by testing ideas against scenarios, designing layered documentation, decomposing complex problems into actionable steps, or bridging strategy-execution gaps.

Takes one strategic question about substacker ("should we launch paid?", "is this section dead?", "are we writing for the wrong audience?") and produces the mandatory evidence + reasoning + downside triad plus a recommendation. Used 3 times per Growth Strategist review. Trigger keywords — uncomfortable question, strategic question, evidence reasoning downside, triad.

Applies a Bayesian haircut to a bid valuation for common-value auctions where winning is itself evidence the bidder over-estimated. Takes a raw valuation, a value-type classification (common_value / private_value / mixed), the number of informed bidders N, and a signal-dispersion estimate, and returns an adjusted valuation. Domain-neutral and reusable across fantasy FAAB, prediction markets, M&A bids, ad-auction budgets, and any generic bidding context. Use when user mentions "winner's curse", "common value auction", "valuation haircut", "adverse valuation", "Bayesian bid adjustment", or "over-paying in auction".

Assesses who actually controls a company and whether management is accountable — board independence, dual-class and voting structures, cross-holdings and pyramids, the marginal investor, and the gap between stated and real accountability. Use in a corporate finance analysis, when setting the objective function for a valuation, when assessing the odds that bad management gets replaced, or when valuing control.

Applies "what if" thinking to explore alternative scenarios, test assumptions, understand causal relationships, and prepare for uncertainty. Guides through counterfactual reasoning, scenario exploration, pre-mortem analysis, and stress testing decisions against alternative futures. Use when exploring alternative scenarios, testing assumptions through "what if" questions, understanding causal relationships, conducting pre-mortem analysis, stress testing decisions, or when user mentions counterfactuals, hypothetical scenarios, thought experiments, alternative futures, what-if analysis, or needs to challenge assumptions.

Classifies an opposing player, manager, or agent into one of a configurable archetype set using Bayesian inference over observed behavior (roster composition, transaction pattern, lineup moves, trade activity). Domain-neutral scaffold -- callers supply the archetype taxonomy (names, priors, characteristic feature distributions) and observed features; the skill returns a normalized posterior, MAP archetype, classification confidence, feature-contribution breakdown, and best-response hints. Use when modeling opponents, classifying player types, performing Bayesian archetype inference, producing opponent posteriors, or when user mentions opponent archetype, classify opponent, Bayesian archetype inference, player type classification, opponent modeling, or archetype posterior.

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 before sharing, publishing, or submitting writing, or when user mentions pre-publish, final check, ready to publish, last review, quality check, or about to share.

Applies Bayesian reasoning to systematically update probability estimates with new evidence, helping make better forecasts and avoid overconfidence. Use when making predictions or judgments under uncertainty, forecasting outcomes, evaluating probabilities, testing hypotheses, calibrating confidence, assessing risks with uncertain data, or when user mentions priors, likelihoods, Bayes theorem, probability updates, forecasting, calibration, or belief revision.

Classifies a company and routes it to the correct analytical treatment — life cycle stage, sector type, earnings status, ownership, distress markers, geography — then compiles the hard constraints that follow into classification.json. Use before any valuation begins, when deciding which valuation model applies, or when a company looks non-standard — a bank or insurer, a pre-revenue or loss-making firm, a distressed or declining business, a private company or an IPO, a cyclical or commodity producer, an emerging-market firm, or a multi-business group.

Creates rigorous, validated models of entities, relationships, and constraints for database schemas (SQL, NoSQL, graph), knowledge graphs, ontologies, API data models, and taxonomies. Covers relational, document, graph, event/time-series, and dimensional schema patterns with lifecycle modeling, soft deletes, polymorphic associations, and hierarchies. Use when user mentions "schema", "data model", "entities", "relationships", "ontology", "knowledge graph", or when data structures need formalization.

Guides clinical and health science research through PICOT question formulation, evidence hierarchy assessment, bias evaluation (Cochrane RoB 2, ROBINS-I), outcome prioritization, and GRADE certainty rating. Use when formulating clinical research questions, evaluating health evidence quality, prioritizing patient-important outcomes, conducting systematic reviews or meta-analyses, creating evidence summaries for guidelines, or assessing regulatory evidence.

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 citation. Use when designing retrieval pipelines, orchestrating search over knowledge graphs, or when user mentions retrieval strategy, search orchestration, query decomposition, multi-hop reasoning, provenance tracking, or citation in GraphRAG.

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 reporting (headline, pattern, context, meaning, action). Use when analyzing data for patterns, building dashboards, presenting metrics, monitoring KPIs, or when user mentions "visualize this", "what chart should I use", "create a dashboard", "analyze this data", "show trends", "report on".

Facilitates structured roleplay, debate, and synthesis to resolve decisions with multiple legitimate perspectives and inherent tensions. Surfaces assumptions that single-viewpoint analysis would miss and integrates competing priorities into coherent recommendations. Use when stakeholders have competing priorities (growth vs. sustainability, speed vs. quality), need to pressure-test ideas from different angles, explore tradeoffs between incompatible values, or synthesize conflicting expert opinions into coherent strategy.

Challenges plans, designs, and decisions from multiple adversarial perspectives to surface blind spots, hidden assumptions, and vulnerabilities before they cause real damage. Use when testing plans or decisions for blind spots, need adversarial review before launch, validating strategy against worst-case scenarios, building consensus through structured debate, identifying attack vectors or vulnerabilities, or when groupthink or confirmation bias may be hiding risks.

Creates visual maps that make implicit relationships, dependencies, and structures explicit through diagrams, concept maps, and architectural blueprints. Guides through identifying nodes and relationships, choosing visualization approaches, and validating completeness. Use when complex systems need visual documentation, mapping component relationships and dependencies, creating hierarchies or taxonomies, documenting process flows or decision trees, understanding system architectures, visualizing data lineage or knowledge structures, or when user mentions concept maps, system diagrams, dependency mapping, relationship visualization, or architecture blueprints.

Monitors external trends across PESTLE dimensions, detects weak signals of emerging change, develops scenario-based futures, and sets adaptive signposts for early warning. Use when scanning external trends for strategic planning, detecting early indicators of change, planning scenarios for multiple futures, setting signposts and indicators for early warning, or when user mentions environmental scanning, horizon scanning, trend analysis, scenario planning, strategic foresight, or futures thinking.

Creates explicit stakeholder alignment through negotiated working agreements, clear decision rights (RACI/DACI/RAPID), and conflict resolution protocols. Use when stakeholders need aligned working agreements, resolving decision authority ambiguity, navigating cross-functional conflicts, establishing governance frameworks, negotiating resource allocation, defining escalation paths, creating team norms, mediating trade-off disputes, or when user mentions stakeholder alignment, decision rights, working agreements, conflict resolution, governance model, or consensus building.

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 identify symmetries in their data, when user mentions data symmetry, invariance discovery, what transformations matter, or needs help recognizing patterns their model should respect.

Computes the best-response allocation of roster resources across categories in a Head-to-Head Categories matchup. Given our per-category capacity, the opponent's projected output, per-category win probabilities (from matchup-win-probability-sim), and a K-of-N winning threshold, classifies categories into pushed / contested / conceded buckets, emits per-category leverage weights for downstream lineup and streaming decisions, computes the resulting K-of-N win probability, and writes a plain-English rationale. Domain-neutral — portable to any fantasy sport with H2H Cats scoring (MLB 10-cat, NBA 9-cat, NHL 10-cat). Use when you need push/punt decisions, dominated-strategy elimination, leverage weights per cat, or best-response allocation; or when the user mentions "category allocation", "push or punt", "K of N cats", "dominated strategy elimination", "best response allocation", "Blotto fantasy", "leverage weights per cat", or "which cats to push".

Designs neural network architectures that respect validated symmetry groups, recommending architecture families (G-CNN, steerable CNN, e3nn), layer patterns, and implementation libraries. Use when you have validated symmetry groups and need equivariant architecture design, or when user mentions equivariant layers, G-CNN, e3nn, steerable networks, or building symmetry into a model.

Converts reported financial statements into valuation-ready numbers — capitalizes R&D into a research asset, capitalizes operating leases into debt, strips one-time items, normalizes cyclical or trough earnings, derives FCFF and FCFE, and computes invested capital, ROIC, economic value added and the diagnostic ratio pack. Use when cleaning up EBIT before a DCF, capitalizing research or leases, deciding whether a restructuring charge is really non-recurring, computing free cash flow to the firm or to equity, measuring return on invested capital, normalizing earnings for a cyclical or commodity company, or building the base-year operating income and capital base that a valuation model consumes.

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 entry, product launch, expansion, M&A, turnaround), conducting competitive analysis, making strategic decisions (build vs buy, pricing, positioning), planning strategic initiatives, or when user mentions strategy, competitive analysis, Porter's 5 Forces, SWOT, market positioning, or strategic frameworks.

Generates a single self-contained static HTML dashboard for a household finance JSON store, with embedded D3.js, inlined data, and six standard panels (net worth over time, monthly cash flow, recurring & subscriptions, goals progress, asset allocation, vigilance feed). Applies cognitive-design principles for visual hierarchy and visual-storytelling-design for narrative annotations. Use when emitting a weekly or monthly finance dashboard, or when user mentions weekly dashboard, household dashboard, finance HTML report, or static dashboard.

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 knowledge needs simplification, formal content needs casual tone, long-form needs summarization, internal content needs external framing, or cross-cultural adaptation is needed. Use when user mentions "explain to", "reframe for", "translate for [audience]", "adapt for [executives/engineers/customers]", or "same content, different audience".

Chains estimation, decision analysis, and storytelling to transform uncertain choices into clear, stakeholder-ready recommendations. Quantifies uncertain variables, applies expected value analysis to identify the best option, then packages the analysis into a persuasive narrative. Use when evaluating strategic options (build vs buy, market entry, resource allocation), quantifying tradeoffs, justifying investments, pitching to decision-makers, or when user mentions ROI analysis, expected value, business case, cost-benefit, or needs to combine estimation with persuasive communication.