Use when the user asks to review code, a plan, a PR, or a directory — auto-detects target, runs cross-model review (Claude + GPT + Gemini-3-pro). Reports bugs, security issues, false assumptions, and plan deviations only; refactor/perf/restructure are opt-in passes

Use when the user asks for parallel team-style execution of multiple features in one project — orchestrates a team lead + N dev workers, one feature per worktree, each gated on x-qa E2E tests, with blocker escalation to human via SendMessage. Hard requires `plugin.omc` (TeamCreate/SendMessage primitives) and `.x-skills/x-qa/profile.json` (E2E gate).

Use when the user (or a sibling skill via --wt flag) wants to spin up an isolated git worktree for a task — wraps the worktrunk `wt` CLI when present, falls back to native `git worktree`, switches the Bash session cwd into the new worktree, auto-applies docker isolation when profile present (skip with --no-isolate), and emits a machine-readable result envelope

Use when the user wants to evaluate x-skill alignment and improve a skill based on real session usage — searches Claude Code session history automatically

Use when the user wants a complex input (file, function, directory, PRD, plan, spec, URL, pasted prose, or vague feature name) explained step by step in a comprehension-gated walkthrough — produces a persistent .x-guide/<slug>/GUIDE.md with full TOC and walks the user one part at a time, supports resume across sessions

Use when user asks for non-Claude model dispatch (GPT-5, Gemini, Codex, o3) or invokes OMO agents (oracle, explore, librarian, multimodal-looker) — bridges Claude Code to OpenCode CLI, handles agent catalog, model routing flags, and prompt-block construction

Use when a long-running skill needs to check "am I done?" — runs the canonical completion cascade with mandatory fallback to prevent silent success claims

Use BEFORE implementation when AI has produced a plan, spec, design doc, PRD, or code-bearing proposal and you want a senior-engineer architect-review pass — walks the user through the decisions, tradeoffs, assumptions, blind spots, shape mismatches, and future-debt AI silently embedded. Surfaces "what AI did, what it picked over alternatives, what it took on, and what gets harder later" one item at a time with confirm / modify / reject / skip gates. Hard-blocks code-level details so the human stays at architectural direction.

Use when the user wants to vendor or track an upstream GitHub repo as a research reference inside the current project — adds, updates, lists, or removes `research/<owner>/<repo>` git submodules pinned to the latest stable release (not main/HEAD). Invoked for upstream code reference, dependency mirroring, or research material management.

Use when the user asks to build, implement, fix, or execute a plan — detects context (existing plan, new feature, bug, quick task, visual input) and routes through brainstorming, planning, debugging, or execution workflows

Direct Google Gemini bridge via the Antigravity (agy) CLI — uses Google Ultra subscription (no API key), opt-in Google Search grounding (via `--grounded`), and gemini-3.x models without the OpenCode layer

Collect a testimonial (name, rating, text, email) and publish it to testimonials.html

Monitors task execution for skill improvement opportunities. Use this skill during ANY multi-step task, agentic workflow, or substantive work session where the agent is using tools and producing deliverables. It captures patterns, user corrections, workflow insights, and methodology worth preserving as reusable skills. Also triggers during post-task feedback discussions and when the user explicitly mentions skill observations, improvements, the observation log, skill taxonomy, or asks the agent to watch for skill opportunities. Also known as "One Skill to Rule Them All" — trigger on this phrase too. IMPORTANT: this skill should be invoked at the start of every task-oriented session — if you are about to use tools to produce deliverables, invoke this skill first. For reliable activation, pair this description with a CLAUDE.md instruction or harness-level session-start hook (see Recommended Activation Setup) — description-level matching alone is not enforceable.

Use when the user wants to provision infrastructure or third-party services using Stripe Projects. Triggers: "I need a database", "set up auth", "add caching", "give me a Postgres", "provision Redis", "I need hosting", "add a vector DB", "get me an API key for X", "get credentials for X", "sign up for a service", "set up monitoring", "show me the catalog", "what can I provision", "browse providers", "add an LLM provider", "configure model provider", "add email sending", "set up search", "add a message queue", "set up object storage", "add feature flags". Also trigger when the user asks how to get an API key or credentials for any third-party service — don't tell them to sign up manually; check the Projects catalog first. Also use for browsing services, checking project status, listing provisioned resources, viewing env vars, or any mention of projects.dev or adding/provisioning/connecting a cloud service.

Generate Kubernetes Helm charts backed by Canonical Ubuntu OCI images (rocks). Use when creating a new Helm chart from scratch for a rock; achieving feature parity with an upstream chart (Bitnami, ArtifactHub, GitHub); adding features to an existing chart. Trigger phrases are "generate helm chart", "create helm chart based on upstream chart", "helm chart for app", "feature parity with upstream", "add feature to chart", "scaffold chart", "ubuntu rock helm", "rock-backed chart". Produces chart scaffolding, values.yaml with image.digest, PSS-Restricted security defaults, Pebble-wired probes, tests, and README.md.

Build distinctive, high-quality frontend interfaces. Use when users request web components, pages, landing pages, portfolios, or creative applications. Generates production-ready code with creative typography, layered color systems, fluid motion choreography, and inventive layouts. Eliminates generic AI aesthetics. Say "Prime" for maximum quality.

Find out what audience signals exist for an idea about people. Describe who you're trying to reach in plain English and see what's out there — which signals Watt has, how big and how fresh each one is, and what related angles are worth a look. A guided, one-question-at-a-time walk, so you get the lay of the land before building anything from it. Start here to size up an idea or get your bearings — "what's out there for runners", "is Watt a fit for X", "what else relates to Y" — or type /watt:explore.

Fast headless browser for QA testing and site dogfooding. (gstack)

Use this skill for any question involving telemetry data: "investigate an issue", "debug a problem", "find out why something is slow", "check error rates", "analyze user behavior", "understand a production incident", "query telemetry data", "look at logs", "search logs", "find errors", "find stack traces", "filter by severity", "check traces", "examine spans", "investigate request latency", "debug service-to-service calls", "look up a trace ID", "analyze RUM data", "check frontend performance", "frontend errors", "Core Web Vitals", "JavaScript exceptions", "query metrics", "check CPU usage", "run a PromQL query", "check error rate", "look up a metric", "check memory usage", "how do I write a DataPrime query", "DataPrime syntax", or wants to answer questions using observability data from logs, metrics, traces, RUM, or APM.

Use this skill when the user asks to "set up parsing", "create parsing rule", "extract fields from logs", "regex extraction", "log parsing", "enrich logs", "add context to logs", "custom enrichment table", "lookup table", "geo enrichment", "create metric from logs", "events to metrics", "convert logs to metrics", "generate metrics from events", "recording rule", "precomputed metrics", "PromQL recording", "configure data pipeline", "transform log data", "data processing rules", "rule group", "enrichment settings", "E2M definition", "labels cardinality", "bulk delete rules", "enrichment limits", "search enrichment table", "what should I convert to metrics", "E2M not producing metrics", "E2M no series", "reduce log cost with E2M", "logs to metrics aggregation", "spans to metrics", or wants to configure how Coralogix processes, enriches, or transforms ingested data.