Design skills

Create or adapt Claude Code agent definitions. Use when creating an agent, changing subagent configuration, selecting agent scope, or designing a specialized delegation role.

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Works backward from the stated goal through its prerequisite chain — what must exist to reach the goal, then what each of those requires, back to the current state — classifying each prerequisite as AVAILABLE, DERIVABLE, or MISSING, and blocking planning until nothing is MISSING. Use before creating plans, delegating to agents, or defining acceptance criteria. Activates on specs, PRDs, tickets, RFCs, architecture designs, and multi-step engineering tasks. Always pass `#N` or an inline goal — invoking with no argument and no inline context returns BLOCKED. Integrates with CoVe-style planning pipelines.

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Use when orchestration or planning agents are producing task plans, task prompts, or TASK.md instructions that must be unambiguous, verifiable, and resistant to hallucination. Applies CLEAR (Concise, Logical, Explicit, Adaptive, Reflective) to structure and write agent task prompts, then adds CoVe (Chain of Verification) checks where accuracy risk is meaningful. Activates on draft task prompts, swarm plans, migration tasks, and multi-step plans requiring independently executable steps.

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Use when the PLAN artifact from SAM Stage 2 needs contextualization against actual codebase state — grounds the design plan in reality by performing scope analysis (NEW/MODIFY/COMPLETE classification), conflict detection between plan assumptions and codebase patterns, and resource mapping to concrete file paths and integration points. Produces an updated ARTIFACT:PLAN registered via MCP with a Contextualization section appended.

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Badge design and selection knowledge base for shields.io badges in README files. Use when writing or updating READMEs, choosing badge layouts, selecting badge styles, adding project health indicators, or picking Simple Icons logo slugs. Covers shields.io URL encoding rules, static vs dynamic badge selection, style variants (flat/flat-square/for-the-badge/social/plastic), layout patterns (two-tier/inline/centered), project-type badge sets for Python/JS/Rust/Claude plugins, color reference, non-obvious logo slugs, and common anti-patterns to avoid.

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Enforce anti-AI UI design rules based on the Uncodixfy methodology. Use when generating HTML, CSS, React, Vue, Svelte, or any frontend UI code. Prevents generic AI aesthetic — soft gradients, glassmorphism, hero sections in dashboards, oversized rounded corners, and decorative copy. Applies constraints from Linear/Raycast/Stripe/GitHub design philosophy for functional, honest, human-designed interfaces. Triggers on UI generation, dashboard building, component creation, CSS styling, landing page design, or any task producing visual interface code.

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Execute the implementation planning workflow using the plan template to generate design artifacts.

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Design, create, update, and verify Vapi Squads and documented handoff tools through the public API. Use for choosing a single assistant versus a multi-assistant Squad, persistent or transient members, entry-member ordering, specialization boundaries, context engineering, variable extraction, model-specific handoff patterns, assistant-version pins, and safe Squad updates.

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Design, create, inspect, update, attach, detach, preview, execute, and verify reusable Vapi Structured Outputs through public API or Server SDK workflows. Use for post-call extraction, typed call artifacts, AI-versus-regex extraction, JSON Schema design, backfilling existing calls, or retrieving structured results programmatically.

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Design, create, run, monitor, and maintain Vapi Simulations for assistants and squads. Use for turning plain-language requirements into a coverage plan, simulation personalities, scenarios, structured-output success criteria, simulations, suites, extending an existing suite, chat or voice runs, tool mocks, target variables, lifecycle webhooks, regression coverage, launch readiness, CI quality gates, run-result analysis, and simulation API validation errors. Do not use for fixed-turn mock-conversation Evals unless the user is deciding between Evals and Simulations.

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Cut AI tells from any writing. Use for every piece of prose that ships - docs, READMEs, commit messages, PR descriptions, release notes, changelogs, UI copy, chat answers - and as a final pass before handing work back. Triggers - "unslop", "de-slop", "remove the slop", "sounds like ChatGPT", "make it sound human", "too wordy", "cut the fluff". Must always apply.

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Drafts implementation plans with TDD, documentation updates, and repository verification commands before coding. Use when the user asks for an implementation plan, /create-plan, or structured pre-implementation design for work in encero workspaces (e.g. Incan, IncQL).

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Hand a question about Anabasis v2 to an outside Oracle model as one task document and one zip. The Oracle reads GitHub; the zip carries what it cannot fetch: campaign logs and kept harnesses through `bun run runs zip`, a reader's output, predictions with how each resolved, patches for commits never pushed. Use when the operator asks to prepare an Oracle question, a report request, a problem statement or a second opinion on a run, a reading or a design, or to send the latest runs to an outside model. Produces local files; uploading is the operator's.

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Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration. Use when: build agent, AI agent, autonomous agent, tool use, function calling.

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Master AI agent fundamentals - architectures, ReAct patterns, cognitive loops, and autonomous system design

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Designing features for the "Action Era" that are AI-accessible by default

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Comprehensive guide for building AI workflows, agents, and workforce systems with AgenticFlow. Use when designing workflows with various node types, configuring single agents, or orchestrating workforce collaboration patterns.

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Create and manage AI agent skills following best practices. Use when creating new skills, optimizing context, designing multi-agent systems, or implementing progressive disclosure patterns.

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Designs the cognitive blueprint of an agent before code generation.

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Batteries-included agent component for React/Next.js from ui.inference.sh. One component with runtime, tools, streaming, approvals, and widgets built in. Capabilities: drop-in agent, human-in-the-loop, client-side tools, form filling. Use for: building AI chat interfaces, agentic UIs, SaaS copilots, assistants. Triggers: agent component, agent ui, chat agent, shadcn agent, react agent," agentic ui, ai assistant ui, copilot ui, inference ui, human in the loop

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Designs multi-agent systems with coordinated agent swarms, task distribution, inter-agent communication, and emergent collective behavior.

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Creates new agent skills following modern best practices with proper structure and documentation. Use when asked to build a new skill, organize skill resources, design skill descriptions, or validate skill structure for portability across Copilot platforms.

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Expert in designing, orchestrating, and managing multi-agent systems (MAS). Specializes in agent collaboration patterns, hierarchical structures, and swarm intelligence. Use when building agent teams, designing agent communication, or orchestrating autonomous workflows.

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Designs multi-step agent workflows with tool usage, retry logic, state management, and budget controls. Provides orchestration diagrams, tool execution order, fallback strategies, and cost limits. Use for "AI agents", "agentic workflows", "multi-step AI", or "autonomous systems".

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