Scaffold the Antithesis harness for customers running Kubernetes: Convert helm charts, kustomize templates, or raw Kubernetes manifests into a minimized set of manifests for Antithesis. Verify the Kubernetes manifests are in the correct shape with snouty validate and a local cluster deploy. Hands it off to antithesis-launch if the user wants to submit a run. At the end, the user is ready to run antithesis-workload to build tests. If the desired setup is docker-compose, defer to the antithesis-setup skill.

Search across all timelines in an Antithesis test run to find events, correlate property failures, and answer temporal questions about ordering and causation (e.g., did event A always precede failure B? do failures occur even without a preceding fault?).

Launch an Antithesis run with snouty by discovering the harness layout, building the right Docker Compose config, running `snouty validate`, bailing on validation failure, and then submitting `snouty launch` with sane metadata. Use when the user wants to send, submit, or launch an Antithesis run. This skill takes duration in minutes as input.

Use Antithesis documentation efficiently for product, workflow, and integration questions. Prefer the snouty docs CLI when available, and otherwise request markdown versions of documentation pages directly.

Authenticate `agent-browser` against an Antithesis tenant and read data from Antithesis web pages (triage reports, runs page, logs viewer, causality reports). Only invoke this skill when explicitly requested by another skill or the user. Requires interactive browser authentication and is unsuitable for fully headless workflows.

Guides instrumentation of GenAI/LLM applications with OpenTelemetry for Honeycomb, including content capture and agent failure detection. Trigger phrases: "instrument my GenAI app", "add tracing to LLM calls", "trace AI agent", "instrument OpenAI", "instrument Anthropic", "GenAI observability", "trace tool calling", "LLM token usage", "instrument embeddings", "trace MCP", "GenAI metrics", "instrument LangChain", "add GenAI spans", "capture prompts", "capture LLM responses", "enable GenAI content capture", "streaming tracing", "trace streaming responses", or any request about instrumenting GenAI/LLM applications.

Whole-period consistency WORKBOOK - run independent-source checks across Finance OS data (cross-endpoint agreement, balance-sheet identity, cross-grain roll-ups, scenario/period integrity). Validates the data pipeline and mappings, not source systems. NOT a per-metric layer comparison - that is the dev-only cross-layer-reconcile harness.

Comprehensive FP&A financial-analysis intelligence workbook for a FULL FISCAL YEAR — Excel, up to 10 sheets, with ranked auto-detected insights, findings and recommendations, and professional formatting; no PowerPoint. For a one-month KPI snapshot use dashboard; for an executive deck use insights. Self-contained — discovers the client's tables and fields on its own, no profile or setup step required.

Use when the user wants to explain or visualize a codebase on a Miro board — produces a minimal, notation-correct set of architecture / structure / behavior diagrams (flowchart, UML class, UML sequence, ERD) plus a short companion document, grounded in real repo artifacts.

Use when the user wants to explain or visualize a codebase on a Miro board — produces a minimal, notation-correct set of architecture / structure / behavior diagrams (flowchart, UML class, UML sequence, ERD) plus a short companion document, grounded in real repo artifacts.

How to write modern Swift well — modeling with value types, Swift 6 data-race safety and approachable concurrency (@concurrent, main-actor-by-default, actors, task groups), protocols and generics (some vs any), API design, performance and ARC, Swift Testing, macros, and the modern language features agents don't know about yet. Use when writing, reviewing, or migrating Swift, or when a concurrency error, a hang, a data race, a retain cycle, or a performance problem needs fixing.

Build multiple genuinely different versions of a UI piece you describe, rendered behind a visual picker so you can flip through them live and promote the one that feels right. Only runs when explicitly invoked; it does not trigger on its own.

Pick the right library for a given frontend task from a curated, opinionated list — numbers, OTP inputs, charts, command menus, virtualization, drag and drop, toasts, state, styling, and more. Only runs when explicitly invoked; it does not trigger on its own.

Survey a codebase's animation and motion code as a senior motion advisor, then produce a prioritized audit and self-contained implementation plans for other agents (or cheaper models) to execute. Read-only on source code — it plans improvements, it does not apply them. Use when the user asks to "improve the animations", "audit the motion", "make this app feel better", or wants a roadmap of animation fixes rather than a review of a single diff.

Search a codebase or UI for places that don't animate but should, and reject everything that shouldn't. Read-only; it proposes motion with exact values, it does not implement it. Use when the user asks "what could be animated here?" or wants to "make this feel more alive". For fixing existing animations, use improve-animations or review-animations instead.

Guide to Sonner, the React toast library — install and wire up the Toaster, pick the right toast() call, promise and loading toasts, updating, dismissing and persisting toasts, styling, theming and icons, positioning and multiple toasters. Use when working with Sonner or troubleshooting it — toasts that don't appear, appear twice, lose their styles, ignore Tailwind classes, sit behind a modal, or don't follow dark mode.

Build an animation from scratch, making the decisions in the order that determines whether it feels right — should it animate at all, what purpose, which tool, which properties, which curve and duration, how it interrupts, how it exits. Writes the implementation. Use when asked to animate something, add motion, make a component feel alive, or build a transition. For critiquing existing motion use review-animations; for auditing a whole codebase use improve-animations.

Build animations in React Native and Expo, making the decisions in the order that determines whether they feel right — should it animate, which thread it runs on, which properties, spring or timing, how the gesture hands off, how it degrades. Writes the implementation with Reanimated, Gesture Handler, Expo Router and expo-haptics. Use when animating anything in an Expo app, adding gestures, sheets, screen transitions, press feedback or haptics, or fixing motion that stutters on device. For web animation use `animate`.

**CRITICAL**: Use this skill for ALL CVE remediation workflows. DO NOT use individual skills piecemeal for end-to-end remediation. Use when users request: - CVE remediation playbooks or security patch deployment - Multi-step remediation (validation → context → playbook → execution) - Batch remediation across multiple systems or CVEs - End-to-end CVE management (analysis + remediation + verification) - Prioritizing and remediating CVEs (not just listing them) - Emergency security response with immediate remediation plans DO NOT use for simple queries: - "List critical CVEs" → Use `/cve-impact` skill - "What's the CVSS score for CVE-X?" → Use `/cve-impact` or `/cve-validation` - Standalone impact analysis without remediation → Use `/cve-impact` This skill orchestrates 6 specialized skills (cve-impact, cve-validation, system-context, playbook-generator, playbook-executor, remediation-verifier) for complete remediation workflows.

**CRITICAL**: Use for ALL CVE discovery and listing. DO NOT call get_cves directly. Use when: "show critical CVEs", "CVEs on hostname X", "remediatable vulnerabilities", "impact of CVE-X", risk assessment. NOT for remediation (use `/remediation`). System-level: FIRST reply = pagination prompt (Step -1). Parsing: scripts/01-cve-response-parser.py.