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概要と使いどころ
Evaluates frontier market opportunities with liquidity constraints, custody risk, settlement infrastructure, and governance assessment. Use when analyzing frontier markets, assessing operational risk, or evaluating frontier market access.
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Evaluates signal half-life, turnover implications, and capacity constraints for systematic alpha factors. Use when analyzing signal persistence, evaluating factor decay, or estimating strategy capacity.
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Use when ingesting text samples to extract quantitative writing style metrics. Produces a structured JSON style profile with vocabulary patterns, sentence structure, tone markers, and formatting preferences. Invoke before voice-clone-generator to establish style constraints.
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。
Plan and freeze workflow execution by creating an Execution Contract, setting PLAN_FROZEN/CURRENT_ROLE, and updating PROJECT_STATE decisions and Top 3. Use when starting workflow delivery, re-planning after unfreeze, or when scope/constraints change before execution.
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Plan the UX and UI for a feature before writing code. Run a structured discovery interview, then produce a design brief (.design-context.md) that guides implementation. Establishes design direction, constraints, and strategy before any code is written. Triggers on plan the design, design brief, shape the UX, before we build, design discovery.
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Tested output format specifications for Claude prompts. Use when the user wants a specific deliverable structure — retrieving, reviewing, customizing, or building output formatting. Trigger on: "give me the Executive Brief format," "show me the output template for," "I need the Decision Matrix structure," "show me available output formats," "build a custom output format for." Provides 10 ready-to-use structures (executive briefs, technical designs, research summaries, implementation plans, decision matrices, competitive analyses, post-mortems, stakeholder updates, strategic memos, process documentation) plus a custom template, with per-section length guidance and format constraints tested against Claude formatting defaults. If unsure which format fits, use rootnode-block-selection first if available. Do NOT use for evaluating existing prompts (use rootnode-prompt-validation if available) or choosing reasoning/identity approaches (use rootnode-block-selection if available).
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。
AI pressure redirection — handling conflicting demands, tool failures, and competing constraints by blending with incoming force then reframing. Use when receiving contradictory instructions from different sources, during tool failure cascades where the planned approach becomes unviable, when scope pressure threatens to expand the task beyond what was asked, or when user frustration or correction needs to be absorbed rather than deflected.
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。
Perform capacity planning using historical metrics and growth models. Use predict_linear for forecasting, identify resource constraints, calculate headroom, and recommend scaling actions before saturation. Use before seasonal traffic spikes or product launches, during quarterly capacity reviews, when resource utilization trends upward, or before budget planning cycles.
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 ornamental patterns using modern and speculative aesthetics with colorblind-accessible color scales. Breaks free from historical period constraints to explore cyberpunk, solarpunk, biopunk, brutalist, vaporwave, and other contemporary genres. Includes CVD (Color Vision Deficiency) awareness and perceptually uniform scales (viridis, cividis, inferno). Use when creating ornamental designs in modern or genre-specific aesthetics, designing patterns that must be colorblind-accessible, or exploring hybrid motifs combining historical ornament with contemporary visual language.
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 receptive attention to extract intent beyond literal words. Maps active listening from counseling psychology to AI reasoning: clearing assumptions, attending to full signal, parsing multiple layers (literal, procedural, emotional, contextual, constraint, meta), reflecting understanding, noticing what is unsaid, and integrating the whole picture. Use when a user's request feels ambiguous, when context suggests something different from the literal words, when previous responses have missed the mark, or before beginning a large task where misunderstanding intent would waste significant effort.
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Implement policy-as-code enforcement using OPA Gatekeeper or Kyverno to validate and mutate Kubernetes resources according to organizational policies. Covers constraint templates, admission control, audit mode, reporting violations, and integrating with CI/CD pipelines for shift-left policy validation. Use when enforcing resource configuration standards, preventing security misconfigurations such as privileged containers, ensuring compliance before deployment, standardizing naming conventions, or auditing existing cluster resources against policies.
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。
Conduct a structured cross-project code review between two Claude Code instances via the cross-review-mcp broker. Each agent reads its own codebase, reviews the peer's code, and engages in evidence-backed dialogue — with QSG scaling laws enforcing review quality through minimum bandwidth constraints and phase-gated progression.
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。
AI unconstrained creative exploration — deliberately dropping structure to let associations form freely. The one skill that inverts the standard procedure format: instead of inputs, steps, and validation, dream opens space for emergence without evaluation. Use before design work, before naming things, before choosing between approaches, or anywhere that premature structure kills the best option.
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。
Solve Diophantine equations (integer-only solutions) including linear, quadratic, and Pell equations. Covers the extended Euclidean algorithm, descent methods, and existence proofs. Use when finding all integer solutions to ax + by = c, solving Pell's equation, generating Pythagorean triples, proving no integer solutions exist via modular constraints, or finding the fundamental solution from which all others are generated.
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Create multi-stage Dockerfiles that separate build and runtime environments for minimal production images. Covers builder/runtime stage separation, artifact copying, scratch/distroless/alpine targets, and size comparison. Use when production images are too large, when build tools are included in the final image, when you need separate dev and prod images from one Dockerfile, or when deploying to constrained environments like edge or serverless.
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。
Consolidated Symfony conventions (Symfony 6.4 / 7.x): attribute routing, controllers as services, autowiring/DI, Form types, Validation constraints, Voters for authorization, the Serializer/DTO contract, and Messenger. Apply when writing or reviewing Symfony backend code. Activated automatically by symfony-plugin/stack.md as a convention skill for the development phase. Apply when: writing or reviewing Symfony controllers, services, forms, validators, voters, and serialization. Do NOT use this skill for: - PHP language idioms (strict_types, enums, readonly) — see php-foundation:php-conventions. - Doctrine queries / entities — see symfony-plugin:doctrine-patterns. - Testing — injected by the QA phase (WebTestCase/KernelTestCase) + php-foundation:php-testing.
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。
Entity Framework Core patterns for ASP.NET Core projects: DbContext design, code-first entity configuration (Fluent API via IEntityTypeConfiguration<T>), relations (HasOne/HasMany, cascade/restrict/set-null), indexes and unique constraints, projection to DTOs (Select + AsNoTracking), avoiding N+1 (Include, AsSplitQuery), transactions, parameterized raw SQL (FromSql with FormattableString), and connection string from IConfiguration. Works alongside aspnet-core-plugin:aspnet-conventions. Use this skill to: - Design a clean DbContext with ApplyConfigurationsFromAssembly for scalable entity registration. - Configure entity properties (column types, max length, precision, nullability) via Fluent API. - Avoid N+1 query problems with explicit Include / projection to DTOs. - Use transactions correctly for multi-entity operations. - Write parameterized raw SQL safely when LINQ is not expressive enough. Do NOT use this skill for: - Migration generation commands (that is efcore-specialist's job in the database extra phase). - ASP.NET Core middleware, DI, or validation — see aspnet-core-plugin:aspnet-conventions. - C# language idioms — see csharp-foundation:csharp-conventions.
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。
Queries the NCATS Translator ARAX production API for bounded, typed, provenance-rich one-hop and endpoint-pinned two-hop biomedical knowledge-graph relationships. Use for Biolink-constrained RTX-KG2 lookup, explicit selected-provider ARAX federation, separate entity normalization, qualifier-aware graph traversal, and inspection of TRAPI edge bindings, publications, and knowledge-source provenance. Do not use for inference, ranking, open-ended pathfinding, clinical guidance, or sensitive queries.
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。
Cofold protein-ligand, protein-protein, and nucleic-acid complexes with Boltz-2, and predict binding affinity with its trained affinity head. Use this skill to build Boltz input YAML, run structure prediction with MSAs, pocket constraints, templates, and modified residues, screen compound libraries by cofolding, and interpret confidence scores (pLDDT, pTM, ipTM, PDE) and affinity output (binder probability and log10 IC50). Also trigger on Boltz, Boltz-1, Boltz-2, cofolding, boltz predict, affinity_pred_value, affinity_probability_binary, ipTM, or open-weights AlphaFold3 alternatives.
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 and optimise novel small molecules with REINVENT 4 — de novo sampling from a chemical language model, scaffold decoration with LibInvent, fragment linking with LinkInvent, and similarity-constrained analogue generation with Mol2Mol. Use this skill to set up reinforcement-learning or curriculum runs, compose a multi-parameter scoring function from docking scores, predictive models, and physicochemical desirability, and read the resulting sampled sets. Also trigger on REINVENT, LibInvent, LinkInvent, Mol2Mol, scaffold hopping, R-group replacement, linker design, chemical language model, or reinforcement-learning molecule optimisation.
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 writing no_std Cortex-M or RISC-V firmware in Rust with cortex-m-rt, probe-rs, defmt, RTIC, or a panic handler. Not for no_std library constraints: use rust-no-std.
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 onboarding to a repository, capturing costly conventions, constraints, or rationale, or improving AGENTS.md. Not for remote, credential, publish, deploy, or irreversible changes.
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Audit and migrate specs to the Guarantees + Constraints format. Use when asked to review spec quality, find non-conforming specs, or batch-migrate specs from the old Behaviors/Interfaces format.
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。
Migrate workloads from Google Cloud Platform to AWS — including AI and agentic workloads regardless of cloud provider. Triggers on: migrate from GCP, GCP to AWS, move off Google Cloud, migrate Terraform to AWS, migrate Cloud SQL to RDS, migrate GKE to EKS, migrate Cloud Run to Fargate, Google Cloud migration, migrate from OpenAI to Bedrock, move off OpenAI, switch from ChatGPT API to AWS, migrate from Gemini to Bedrock, migrate LangChain to Bedrock, migrate LangGraph to AWS, migrate agentic workloads to AWS, move AI workloads to AWS, migrate my AI app to AWS. Runs a 6-phase process: discover GCP resources from Terraform files, app code, or billing exports, clarify migration requirements, design AWS architecture, estimate costs, generate migration artifacts, and collect optional feedback. Clarify must finish before Design, Estimate, or Generate. Includes AI provider migration guidance (for example, OpenAI to Amazon Bedrock) by selecting closest-fit Bedrock model families for required modality, latency/quality targets, context windows, and cost constraints. Model mapping is compatibility-guided, not 1:1 parity; validate prompts, tool-calling behavior, and eval metrics before cutover. Do not use for: Azure or on-premises migrations to AWS, AWS-to-GCP reverse migration, general AWS architecture advice without migration intent, GCP-to-GCP refactoring, or multi-cloud deployments that do not involve migrating off GCP.
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。