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概要と使いどころ
Design and implement adaptive testing systems using Item Response Theory (IRT). Use when working with computerized adaptive tests (CAT), psychometric assessment, ability estimation, question calibration, test design, or IRT models (1PL/2PL/3PL). Covers test algorithms, stopping rules, item selection strategies, and practical implementation patterns for K-12, certification, placement, and diagnostic assessments.
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。
Compare models with A/B testing for selection
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。
Other tools need perfect images. CellCog turns ideas into 3D models. From rough sketches, text descriptions, or product photos — production-ready GLB files for games, AR/VR, e-commerce, and 3D printing.
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。
Generate 3D models using each::sense AI. Create 3D assets from text or images for games, products, architecture, characters, vehicles, and more with PBR textures.
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。
Design and configure Microsoft Entra app registrations for secure authentication and authorization. Use this skill when users need OAuth, OpenID Connect, API permissions, app roles, or multi-tenant identity. Covers auth flows, credentials, API permissions, consent models, and zero-trust patterns.
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。
Search real-time news with bias scoring, get live stock/ETF/crypto data with AI analysis, price options with ML models, and synthesize balanced news perspectives via the Helium MCP server.
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。
Design robust, scalable database schemas for SQL and NoSQL databases. Provides normalization guidelines, indexing strategies, migration patterns, constraint design, and performance optimization. Ensures data integrity, query performance, and maintainable data models.
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。
Language models exhibit predictable degradation patterns as context length increases. Understanding these patterns is essential for diagnosing failures and designing resilient systems.
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。
Convert human-written CLAUDE.md into AI-native structured-label format. Battle-tested across 4 models. Same rules, fewer tokens, higher compliance.
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。
Design and operate Azure Storage services for durability, performance, and secure access. Use this skill when users ask about Blob, Files, Queues, Tables, or Data Lake storage patterns. Covers redundancy, tiering, access models, lifecycle policies, and compliance.
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。
Comprehensive regression model diagnostics and assumption checking. Use when validating regression models, checking assumptions (linearity, homoskedasticity, normality, independence), detecting outliers/influence, testing multicollinearity, or when user mentions residuals, heteroskedasticity, VIF, Cook's distance, or diagnostic plots.
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。
Auto-activate for msgspec, Struct, Meta, msgspec.json, msgspec.msgpack, tagged unions, enc_hook, dec_hook, convert(), or Litestar DTO shapes. Not for Pydantic or ORM models — use their stack-specific skill.
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。
Design and safely evolve data models, schemas, migrations, queries, indexes, and ORM data-access boundaries. Discover the existing engine and migration source before proposing syntax.
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。
Design and safely evolve data models, database schemas, migrations, queries, indexes, and ORM data-access boundaries. Use for data-model, migration, query, index, ORM schema, or datastore-selection tasks. Discover the existing engine and migration source before proposing syntax.
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 Redis caching with django-cacheops. This skill should be used when implementing caching, adding cache invalidation, optimizing API performance, modifying models that affect cached data, or debugging cache-related issues in the Django backend.
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。
Parallel code verification using multiple models with severity classification
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。
Choose what kind of knowledge to transfer between teacher and student models: response, feature, or relational, and decide among offline, online, self, or cross-modal distillation schemes. Best for distillation strategy selection, capacity-gap diagnosis, and transfer planning. Activate on "knowledge distillation", "teacher-student", "soft labels", "feature distillation", "online distillation", or "cross-modal transfer". NOT for generic compression checklists or unrelated training work.
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。
Deep analysis of knowledge distillation techniques for compressing large models into smaller efficient ones
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。
Monetization strategies for indie developers, solopreneurs, and small teams. Covers freemium models, SaaS pricing, sponsorships, donations, email list building, and passive income for developer tools, content sites, and educational apps. Activate on 'monetization', 'make money', 'pricing', 'freemium', 'SaaS', 'sponsorship', 'donations', 'passive income', 'indie hacker'. NOT for enterprise sales, B2B outbound, VC fundraising, or large-scale advertising (use enterprise/marketing skills).
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。
Framework for coordinating meaning across autonomous agents that use different conceptual models, vocabularies, and knowledge representations. Treats ontologies as first-class negotiable resources rather than hardcoded implementation details.
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 recognition-primed decision making to agents operating under time pressure, uncertainty, and ambiguous cues. Use for situation assessment, RPD-style action selection, and decision-support design in operational settings. NOT for batch optimization, static planning with full information, or tasks where exhaustive search is cheap and harmless.
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。
Tracks cumulative LLM costs across DAG execution and makes real-time decisions to stay within budget. Downgrades models, skips optional nodes, or stops early when cost exceeds thresholds. Use when managing execution budgets, analyzing cost breakdowns, or optimizing model routing for cost. Activate on "cost budget", "too expensive", "reduce cost", "cost optimization", "model downgrade", "budget exceeded". NOT for LLM model selection logic (use llm-router), pricing comparisons across providers, or billing/invoicing.
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 causal reasoning and mental model construction for diagnosis and prediction in complex domains
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。