chaos
Use to design failure scenarios for resilience testing. AI generates failure modes tailored to the actual stack — DB outage, slow downstream, partial deploy, region failover. Output is a runbook of "what should happen if X fails".
What this skill does
# chaos Designed failure injection. Tailored to the actual stack — not generic "kill a pod". Outputs scenarios + expected behaviour + observability assertions. ## Method 1. **Stack pass.** Detect: DB(s), queues, caches, external APIs, regions, deploy strategy, feature flags. Foundation for tailored scenarios. 2. **Failure-mode brainstorm** (per dependency): - **Slow** — adds latency - **Down** — fully unreachable - **Wrong** — returns valid-looking but incorrect data - **Flaky** — alternates pass/fail - **Partial** — some operations succeed, some fail 3. **For each scenario:** - Trigger (how to reproduce in test/staging) - Expected user-visible behaviour (graceful? degraded? error message?) - Expected observability signal (alert? log line? metric?) - Recovery path (auto? manual?) 4. **Prioritise.** P0 (user-facing data loss / security), P1 (degraded UX), P2 (internal noise). 5. **Output runbook.** One scenario per page; runnable. ## Output shape ``` Stack: <detected — DB, queues, caches, deps> Scenario #1 [P0] <name — e.g. "primary DB unreachable"> Trigger: <iptables block | toxiproxy | feature flag> Expected UX: <what user sees> Expected obs: <alert name, log line, metric> Recovery: <auto | manual + steps> Test: <how to assert it works as expected> Scenario #2 ... ``` ## Rules - **Prod-realistic, not toy.** Don't propose chaos that can't be triggered in a real environment. - **Expected behaviour is concrete.** "Graceful degradation" is not enough — name the actual signal. - **Observability is part of the contract.** If failure has no signal, that's a finding (add observability before chaos). - **Don't actually run chaos in prod without explicit approval.** Output is a plan; execution is gated. ## Anti-patterns - Generic "kill a pod" scenarios that ignore the real architecture - "It should fail gracefully" without naming the gracefulness signal - Test-only scenarios (real chaos lives in staging at minimum) - Recommending chaos in services without prior load testing ## When NOT to use - New service, no production traffic — load-test first - Already running game-day frameworks — don't reinvent - Single-machine local dev — chaos is for distributed systems ## Subagent dispatch - `Explore` to detect the stack - `Plan` to sequence the scenarios - `general-purpose` for runbook generation ## Value over native CC CC will list failure modes. CC won't naturally tailor to the actual stack, prioritise by impact, or produce runbook-shape output with observability assertions. The structure IS the value.
Related in Design
contribute
IncludedLocal-only OSS contribution command center. Auto-refreshes the user's in-flight PR and issue state on invoke so conversations start with full context — no need to brief Claude on what's in flight. Helps the user find issues to contribute to on GitHub, builds per-repo dossiers of what each upstream expects (CLA, DCO, branch convention, AI policy, draft-first, review bots, issue templates), runs deterministic gates before any external action so AI-assisted contributions don't reach maintainers as slop. State is markdown-only: candidate files at ~/.contribute-system/candidates/, repo dossiers at ~/.contribute-system/research/, append-only event log at ~/.contribute-system/log.jsonl. No database, no cloud calls. Use when the user asks about their PRs / issues / contributions, wants to find new work to take on, claim an issue, build/refresh a repo's dossier, or draft a Design Issue or PR. Trigger with "/contribute", "what's my PR status", "find a contribution", "claim issue X", "draft a Design Issue for Y", "refresh dossier for Z".
architectural-analysis
IncludedUser-triggered deep architectural analysis of a codebase or scoped subtree across eight modes — information architecture, data flow, integration points, UI surfaces, interaction patterns, data model, control flow, and failure modes. This skill should be used when the user asks to "diagram this codebase," "map the architecture," "show the data flow," "give me an ERD," "trace control flow," "find the integration points," "verify the layout pattern," "audit the UX architecture," or any similar request whose primary deliverable is mermaid diagrams plus cited reports under docs/architecture/. Dispatches haiku/sonnet sub-agents in parallel for per-mode exploration, then verifies every citation mechanically before any node lands in a diagram. Not for one-off prose explanations of code (use code-explanation) or for high-level system design from scratch (use system-design).
mcp
IncludedModel Context Protocol (MCP) server development and tool management. Languages: Python, TypeScript. Capabilities: build MCP servers, integrate external APIs, discover/execute MCP tools, manage multi-server configs, design agent-centric tools. Actions: create, build, integrate, discover, execute, configure MCP servers/tools. Keywords: MCP, Model Context Protocol, MCP server, MCP tool, stdio transport, SSE transport, tool discovery, resource provider, prompt template, external API integration, Gemini CLI MCP, Claude MCP, agent tools, tool execution, server config. Use when: building MCP servers, integrating external APIs as MCP tools, discovering available MCP tools, executing MCP capabilities, configuring multi-server setups, designing tools for AI agents.
react-native-skia
IncludedDesign, build, debug, and optimise high-polish animated graphics in React Native or Expo using @shopify/react-native-skia, Reanimated, and Gesture Handler. Use when the user wants canvas-driven UI, shaders, paths, rich text, image filters, sprite fields, Skottie, video frames, snapshots, web CanvasKit setup, or performance tuning for custom motion-heavy elements such as loaders, hero art, cards, charts, progress indicators, particle systems, or gesture-driven surfaces. Also use when the user asks for fluid, glow, glass, blob, parallax, 60fps/120fps, or GPU-friendly animated effects in React Native, even if they do not explicitly say "Skia". Do not use for ordinary form/layout work with standard views.
plaid
IncludedProduct Led AI Development — guides founders from idea to launched product. Six capabilities: Idea (discover a product idea), Validate (pressure-test the idea against fatal flaws, problem reality, competition, and 2-week MVP feasibility), Plan (vision intake + document generation), Design (translate image references into a design.md spec), Launch (go-to-market strategy), and Build (roadmap execution). Use when someone says "PLAID", "plaid idea", "help me find an idea", "product idea", "idea from my business", "idea from my expertise", "plaid validate", "validate my idea", "pressure-test", "is this idea good", "find fatal flaws", "validate the problem", "plan a product", "define my vision", "generate a PRD", "product strategy", "plaid design", "design from image", "translate image to design", "create design.md", "extract design tokens", "plaid launch", "go-to-market", "launch plan", "GTM strategy", "launch playbook", "plaid build", "build the app", "start building", or "execute the roadmap".
nextjs-framer-motion-animations
IncludedAdds production-safe Motion for React or Framer Motion animations to Next.js apps, including reveal, hover and tap micro-interactions, whileInView, stagger, AnimatePresence, layout and layoutId transitions, reorder, scroll-linked UI, and lightweight route-content transitions. Use when the user asks to add, refactor, or debug Motion or Framer Motion in App Router or Pages Router codebases, especially around server/client boundaries, reduced motion, LazyMotion, bundle size, hydration, or route transitions. Avoid for GSAP-style timelines, WebGL or 3D scenes, heavy scroll storytelling, or CSS-only effects unless Motion is explicitly requested.