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Perform controlled dismantling of rigid system structures while preserving essential capabilities (imaginal discs). Covers rigidity mapping, dissolution sequencing, knowledge extraction, interface archaeology, and safe decomposition of technical debt and organizational calcification. Use when assess-form classified the system as PREPARE or CRITICAL, when a system is so calcified that incremental change is impossible, when technical debt blocks all forward progress, or before adapt-architecture when the current form must be softened before it can be reshaped.

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Decommission a validated computerized system at end-of-life. Covers data retention assessment by regulation, data migration validation (mapping, transformation, reconciliation), archival strategy, access revocation, documentation archival, and stakeholder notification. Use when a validated system is being replaced, reaching end-of-life without replacement, vendor support is discontinued, multiple systems are consolidating, or regulatory changes render a system obsolete.

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Design an acoustic levitation system that uses standing waves to trap and suspend small objects at pressure nodes. Covers ultrasonic transducer selection, standing wave formation between a transducer and reflector, node spacing and trapping position calculation, acoustic radiation pressure analysis, and phased array configurations for multi-axis manipulation. Use when designing contactless sample handling for chemistry, biology, materials science, or demonstration purposes.

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Evolve SKILL.md files from agent execution traces using a three-stage pipeline: trajectory collection from observed runs, parallel multi-agent patch proposal for error and success analysis, and conflict-free consolidation of overlapping edits via prevalence-weighting. Based on the Trace2Skill methodology.

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Create R-based pictogram glyphs for skill, agent, or team icons in the visualization layer. Covers concept sketching, ggplot2 layer composition using the primitives library, color strategy, registration in the appropriate glyph mapping file and manifest, rendering via the build pipeline, and visual verification of the neon-glow output. Use when a new entity has been added and needs a visual icon for the force-graph visualization, an existing glyph needs replacement, or when batch-creating glyphs for a new domain.

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AI context assessment — evaluating problem malleability, mapping structural rigidity versus flexibility, analyzing transformation pressure, and estimating capacity to adapt. Use when a complex task feels stuck and it is unclear whether to push through or pivot, before a significant approach change to assess whether the current reasoning structure can support it, when accumulated workarounds suggest the underlying approach may be wrong, or as a periodic structural health check during extended multi-step tasks.

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Build a custom MCP (Model Context Protocol) server that exposes domain-specific tools to AI assistants. Covers server implementation in Node.js or R, tool definitions, the resources and prompts primitives, transport configuration, and testing with Claude Code. Use when you need to expose custom functionality beyond what mcptools provides, when building specialized domain-specific AI integrations, or when wrapping existing APIs or services as MCP tools.

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Analyze an arbitrary codebase to identify functions, APIs, and data sources suitable for exposure as MCP tools, producing a tool specification document. Use when planning an MCP server for an existing project, auditing a codebase before wrapping it as an AI-accessible tool surface, comparing what a codebase can do versus what is already exposed via MCP, or generating a tool spec to hand off to scaffold-mcp-server.

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Analyze a tensegrity system by identifying compression struts and tension cables, classifying type (class 1/2, biological/architectural), computing prestress equilibrium, verifying stability via Maxwell's rigidity criterion, and mapping biological tensegrity (microtubules, actin, intermediate filaments). Use when evaluating tensegrity in architecture, robotics, cell biology, or any system with isolated compression in continuous tension.

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Detect missing glyphs, icons, and HD variants by comparing registries against glyph mapping files, icon directories, and manifests. Reports gaps for skills, agents, and teams across all palettes.

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Evaluate a system's current structural form, identify transformation pressure, and classify transformation readiness. Covers structural inventory, pressure mapping, rigidity assessment, change capacity estimation, and readiness classification for architectural metamorphosis. Use before any significant architectural change to understand the starting point, when a system feels stuck without clear reasons, when external pressure from growth or tech debt is mounting, or as periodic health checks for long-lived systems.

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Spring Boot coding conventions: REST controller design, service layer, dependency injection, configuration, DTO patterns, exception handling, and Bean Validation. Apply on any Spring Boot project. Works alongside spring-boot-plugin:spring-data-jpa for persistence and java-foundation:java-conventions for domain objects. Use this skill to: - Structure REST endpoints with @RestController, proper HTTP semantics, and DTO mapping. - Enforce constructor injection and @ConfigurationProperties over @Value on fields. - Write @Service classes with transactional boundaries at the right layer. - Handle errors consistently with @RestControllerAdvice and ProblemDetail (RFC 9457). Do NOT use this skill for: - JPA entity and repository patterns — see spring-boot-plugin:spring-data-jpa. - Spring Security configuration — see the security phase injection in stack.md. - Build tool configuration — see java-foundation:build-tooling.

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Doctrine ORM best practices for Symfony: entity mapping as source of truth, repositories, DQL/QueryBuilder with parameters, N+1 prevention with fetch joins, relations (cascade/fetch/orphanRemoval), lifecycle events, batch processing, and generated migrations. Apply when: writing or reviewing Doctrine entities, repositories, and queries. Activated automatically by symfony-plugin/stack.md as a convention skill for the development and database phases. Do NOT use this skill for: - PHP language idioms — see php-foundation:php-conventions. - Controllers/services/forms — see symfony-plugin:symfony-conventions.

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Angular forms: Reactive Forms (preferred — typed FormGroup/FormControl since Angular 14, FormBuilder, custom + async validators, FormArray, multi-step) and Template-driven (`[(ngModel)]` + FormsModule). Validation strategies, server error mapping, accessibility. Use this skill to: - Build Reactive Forms with typed FormGroup/FormControl. - Use FormBuilder для concise syntax. - Implement custom synchronous and async validators. - Wire FormArray for dynamic field lists. - Map server errors back to form fields. - Pick Reactive vs Template-driven (prefer Reactive). Do NOT use this skill for: - General conventions (see angular-conventions). - State management beyond forms (see angular-state-and-rx). - Routing (see angular-routing). - Testing forms (see angular-testing).

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Modern Java idioms for any JVM project (Java 17+): records, sealed types, text blocks, pattern matching, Optional discipline, streams, immutability, null safety, package layout, var usage, and code organisation. Apply when the project is a Java 17+ project. Stack-agnostic — referenced by every Java plugin in the marketplace. Use this skill to: - Write self-documenting, immutable-by-default value types with records. - Model closed type hierarchies with sealed classes and interfaces. - Handle optionality explicitly with Optional instead of returning null. - Write expressive pipelines with Stream API without performance footguns. - Lay out packages consistently (feature-first or layer-first) and keep class responsibilities tight. Do NOT use this skill for: - Framework-specific idioms (Spring annotations, JPA mappings — those live in framework plugin skills). - Build tooling (Maven/Gradle) — see java-foundation:build-tooling. - Testing patterns — see java-foundation:jvm-testing.

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当用户想判断一个问题是否能用监督学习解决,或试图把业务问题转化为AI问题时调用。 用户在问"AI能不能做X"、"这算不算机器学习问题"、"我们的业务场景适合用AI吗"时使用。 不适用于: 纯数据分析洞察、无明确输入输出的探索性问题、聚类等非监督学习场景。

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Use when debugging with LLDB on macOS, FreeBSD, or Linux-clang, mapping GDB commands to LLDB, Xcode or VS Code integration, LLDB Python scripting, or debugging Swift and Objective-C.

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Use when reducing header bloat and compilation cascades with Include What You Use, interpreting IWYU reports, mapping files, forward declarations, or CMake integration.

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Use when loading core files in GDB or LLDB, enabling core dump generation via ulimit or coredumpctl, mapping symbols with debuginfod, or extracting backtraces from production segfaults.

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Use when building static archives with ar, stripping or converting binaries, mapping crash addresses with addr2line, or demangling C++ symbols. Not for ELF analysis: use elf-inspection.

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Use when the user asks to set up the agent environment. Covers credentials, placeholders, tool access checks, a uv venv, a pack install, or a role-model mapping file. Not for unattended runs.

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Design or implement the smallest complete executable evidence for a governed Proto UI behavior. Use for conformance cases, regression tests, cross-layer verification, test-entity mappings, failure-path coverage, or validation-plan design. Do not use tests to decide unsettled semantics.

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Compute a unified outer hull around a group of hex tiles that includes multiple disconnected sub-clusters separated by 1-tile gaps. Use when: (1) rendering nested hierarchy hulls (e.g. "package" hull wrapping multiple "sub-region" hex islands), (2) the naive boundary trace outlines each island separately instead of as one bag, (3) flood-fill from outside walks THROUGH the 1-hex gaps and marks them exterior, (4) heuristic "non-adjacent same-group neighbours" over-fills L-shape concavities, (5) axial-only opposite-pair filling misses non-axial bridges between islands. Solution: morphological close (dilate + erode) on the hex grid — fills exactly the interior 1-hex gaps without affecting exterior corners, then trace boundary on the closed shape.

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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.

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