1,486 件 ・ 関連度順
概要と使いどころ
Observational cosmology from Hubble's law to the CMB. Covers redshift, Hubble expansion, the cosmological parameters, the cosmic microwave background, large-scale structure, galaxy rotation curves and dark matter, Type Ia SNe and dark energy, and the current state of Lambda-CDM. Use when reasoning about the large-scale universe, interpreting cosmological surveys, or teaching the Big Bang evidence chain.
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。
Construct well-structured arguments using the hypothesis-argument-example triad. Covers formulating falsifiable hypotheses, building logical arguments (deductive, inductive, analogical, evidential), providing concrete examples, and steelmanning counterarguments. Use when writing or reviewing PR descriptions that propose technical changes, justifying design decisions in ADRs, constructing substantive code review feedback, or building a research argument or technical proposal.
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。
"Apply Service-Dominant Logic (Vargo and Lusch, 2004) and value co-creation principles to reframe exchange and value creation. Use this skill when the user needs to redesign value propositions around service exchange, analyze co-creation dynamics between firms and customers, shift from goods-dominant to service-dominant thinking, or when they ask 'how is value created with customers', 'what is our service logic', or 'how do we enable co-creation'.".
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。
Application-level business logic security testing for any domain. Takes an understanding-first approach: map the intended workflows before probing them. Covers: value/quantity logic abuse (negative, zero, overflow, rounding on any numeric field), workflow and state machine bypass (skipping required steps, forcing illegal state transitions, reusing one-time tokens), trust boundary violations (BOLA horizontal/vertical, BFLA, cross-tenant access, negative ownership attacks), idempotency and replay attacks (duplicate submissions, double-spend, same-reference reuse), multi-step flow integrity (checkout, registration, approval, verification), quota and rate limit bypass, time/date manipulation, and authorization code / reference number predictability. Domain-agnostic — applies to SaaS, e-commerce, banking, gaming, social platforms, APIs, or any multi-user application with stateful workflows. Chains from /pentester; chains into /param-fuzz when boundary violations or mass assignment are confirmed.
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。
Build psychological safety in your team using Simon Sinek's Leaders Eat Last Circle of Safety model and Amy Edmondson's research. Use when a leader says "my team doesn't speak up", "people are afraid to fail", "nobody challenges bad ideas", "people only tell me what I want to hear", "I want my team to take more risks", "there's a blame culture", "people hide mistakes", or "I want to create a safe environment". Also trigger when someone describes a team where postmortems turn into blame sessions, where people stay quiet in meetings and then vent privately, where mistakes are hidden until they become crises, or where the same person speaks 80% of the time in every meeting. Psychological safety is not about comfort - it is the condition that makes high performance possible.
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。
Expert in formal logic, informal logic, fallacies, and argumentation theory
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。
Estimates the minimum biological replicates (or cells/events) for a target power at a target FDR in genomics experiments using ssizeRNA, PROPER, powsimR for scRNA-seq, and pilot-data dispersion estimation from DESeq2/edgeR. Covers the biological-versus-technical replication distinction (technical replicates do not add degrees of freedom for biological inference), replicate-number-versus-sequencing-depth budgeting, scRNA-seq sample-versus-cell allocation under a pseudobulk model, and the critique that "n=3" is a publication convention rather than a power calculation. Use when budgeting a sequencing experiment, writing the sample-size justification in a grant, estimating replicates from pilot data, allocating a fixed budget between samples and depth, or planning scRNA-seq cohort size. For clinical-trial sample size see clinical-biostatistics/power-and-sample-size; for the power-given-n direction see experimental-design/power-analysis.
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。
Validates DAG structures, performs topological sorting, detects cycles, and resolves dependency conflicts. Uses Kahn's algorithm for optimal execution ordering. Activate on 'resolve dependencies', 'topological sort', 'cycle detection', 'dependency order', 'validate dag'. NOT for building DAGs (use dag-graph-builder) or scheduling execution (use dag-task-scheduler).
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。
Implement executable BDI reasoning with explicit negation, paraconsistent revision, trigger-based commitment updates, and abduction. Use for runtime agent semantics and conflicting desires. NOT for purely axiomatic modal logic, black-box planners, or classical logic without operational semantics.
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。
Detects deeply nested loops with duplicated inline logic and recommends extracting into small, named functions. Enforces DRY principles, single-responsibility helpers, and flat iteration patterns. Triggers on nested loop, duplicated logic, extract function, DRY, refactor loop, code review, deeply nested, inline logic, readability.
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 working with Azure Logic Apps — azure Logic Apps workflow run analysis, trigger history, connector management, error diagnostics, and workflow definition inspection via Azure CLI.
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。
Estimates the minimum biological replicates (or cells/events) for a target power at a target FDR in genomics experiments using ssizeRNA, PROPER, powsimR for scRNA-seq, and pilot-data dispersion estimation from DESeq2/edgeR. Covers the biological-versus-technical replication distinction (technical replicates do not add degrees of freedom for biological inference), replicate-number-versus-sequencing-depth budgeting, scRNA-seq sample-versus-cell allocation under a pseudobulk model, and the critique that "n=3" is a publication convention rather than a power calculation. Use when budgeting a sequencing experiment, writing the sample-size justification in a grant, estimating replicates from pilot data, allocating a fixed budget between samples and depth, or planning scRNA-seq cohort size. For clinical-trial sample size see clinical-biostatistics/power-and-sample-size; for the power-given-n direction see experimental-design/power-analysis.
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。
敵対的分析手法を統合したレビューの entry skill。認知バイアス対策の3手法 （Pre-mortem / War Game / Logic Torturing）と、宣言・主張と実態の乖離を突く claim-vs-actual 検出3パターン（Self-Contradiction / Refactor-Claim Audit / Cross-File Leakage）へルーティングし、通常のレビューでは見えない設計の盲点・ 防御の穴・論理の弱点・宣言と実装のズレを可視化する。
Estimates the minimum biological replicates (or cells/events) for a target power at a target FDR in genomics experiments using ssizeRNA, PROPER, powsimR for scRNA-seq, and pilot-data dispersion estimation from DESeq2/edgeR. Covers the biological-versus-technical replication distinction (technical replicates do not add degrees of freedom for biological inference), replicate-number-versus-sequencing-depth budgeting, scRNA-seq sample-versus-cell allocation under a pseudobulk model, and the critique that "n=3" is a publication convention rather than a power calculation. Use when budgeting a sequencing experiment, writing the sample-size justification in a grant, estimating replicates from pilot data, allocating a fixed budget between samples and depth, or planning scRNA-seq cohort size. For clinical-trial sample size see clinical-biostatistics/power-and-sample-size; for the power-given-n direction see experimental-design/power-analysis.
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。
Estimates the minimum biological replicates (or cells/events) for a target power at a target FDR in genomics experiments using ssizeRNA, PROPER, powsimR for scRNA-seq, and pilot-data dispersion estimation from DESeq2/edgeR. Covers the biological-versus-technical replication distinction (technical replicates do not add degrees of freedom for biological inference), replicate-number-versus-sequencing-depth budgeting, scRNA-seq sample-versus-cell allocation under a pseudobulk model, and the critique that "n=3" is a publication convention rather than a power calculation. Use when budgeting a sequencing experiment, writing the sample-size justification in a grant, estimating replicates from pilot data, allocating a fixed budget between samples and depth, or planning scRNA-seq cohort size. For clinical-trial sample size see clinical-biostatistics/power-and-sample-size; for the power-given-n direction see experimental-design/power-analysis.
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。
Hunting skill for business logic vulnerabilities. Built from 12 public bug bounty reports. Covers coupon-race-stacking (Instacart, Stripe, Reverb), negative-quantity-in-cart price tampering (Upserve, Eternal/Zomato), decimal/fraction price-field overflow (Shipt), client-side checkout amount trust on PayPal redirect (WordPress.org), price-per-unit mass-assignment (Krisp), and archived-price swap / cart-TOCTOU (Stripe). Use when hunting business logic — heavy emphasis on financial-impact-demonstrated cases.
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。
Djangoアプリの構成、API設計、データ取得、キャッシュの実装とレビューを支援します。環境別の設定分離や処理の整理、不要な問い合わせを減らす設計例を示します。
AndroidやKMPのモジュール構成と依存関係を整理し、業務処理・画面・データ取得を分けて設計するスキル。実装例をもとに各層の役割を明確にします。
Node.js、Express、Next.jsのAPIとデータ処理を設計・レビューするスキル。DBへの問い合わせ、キャッシュ、認証、エラー処理を整理します。
Use when reviewing stylesheets, component styles, and responsive behavior related to Use CSS logical properties for i18n and RTL support. Check the rendered layout across breakpoints and interaction states before proposing a fix.
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。
Manages biological datasets and models with LaminDB, including artifact registration, lineage tracking, schema validation, Bionty ontology annotation, query/search, collections, branches, storage, and workflow integrations. Use for reproducible biological data curation or a LaminDB lakehouse.
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。
Detects sophisticated cyber-physical attacks that follow the Stuxnet pattern of modifying PLC logic while spoofing sensor readings to hide the manipulation, using PLC logic integrity monitoring (Claroty xDome, Nozomi Guardian) and physics-based process anomaly detection. Use when hunting for IT-to-OT lateral movement or discrepancies between PLC program state and physical process behavior in ICS/SCADA 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。
Build reusable, composable n8n sub-workflows. Use when extracting shared logic, building anything multi-step or reused across workflows, or any workflow over ~10 nodes — and whenever the user mentions sub-workflows, Execute Workflow, reuse, shared/common logic, modular workflows, "Define Below" inputs, waitForSubWorkflow, mode each vs all, or exposing a workflow as an agent tool. Covers typed sub-workflow inputs, all-vs-each execution, verb-first naming for discovery, stateless vs stateful design, and splitting by input shape.
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。
Three-layer bias analysis on any URL, file, or text — auto-fetches the content and any cited study, then audits data-level biases, source conflicts of interest, and journalism-added distortions, separating what the data supports from what's editorialized. USE WHEN bias analysis, analyze bias, bias check, check this study, who funded this, is this source biased, fact-check article, methodological flaws, source credibility, what's wrong with this claim. NOT FOR psychological author analysis, research synthesis (use Research), entity due diligence.
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。