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Showing 40 of 2,853 collected skills.
Use when learning or applying the TLA+ ecosystem end-to-end: selecting VS Code, Toolbox, or CLI; choosing PlusCal vs raw TLA+; writing specs above the code level; structuring properties and cfg files; running SANY, TLC, Apalache, or TLAPS; debugging…
Build mock-free integration and E2E tests that hit real databases, real APIs, and real services with structured logging. Use when: replacing mocked tests with real-DB tests, transaction rollback isolation, test data factories, structured JSON-line test…
Compose evidence-backed pages (lessons, technical explainers, run reviews, sales letters, dashboards, feedback surfaces, document collections) as single-file HTML in the lev.now shell. By default, write a content-specific GenUI page, inspect it in light and…
Automate the user's logged-in Chromium browser: read pages, fill forms, scrape data, operate tabs, test a UI, or debug a website. Requires the bsk CLI and browser extension.
Use when guiding decision interviews that produce a design artifact in .lev/pm/designs before proposal or implementation planning.
Use when compiling captured or designed intent into a Lev plan (human runbook, migration, architecture plan, multi-slice roadmap), deepening or reviewing a plan, changing plan state under `.lev/pm/plans/`, reviewing slice readiness, rendering a slice map, or…
Use when you need an evidence-first provenance report from `cass`, repo docs, `.lev`, `~/.agents/diagrams`, `qmd`, or Grep/Glob tools.
Use when routing tracked work through lifecycle lanes, resolving workstream context, or deciding which lifecycle skill owns the next entity transition.
Typed tick loops with FlowMind validation and parallel tick dispatch. Use when running /ll, recurring pipelines, quality loops, or autonomous scheduler ticks.
Use when inventorying conversation state into durable artifacts with an operator-visible source-fidelity table, routing faithful items toward interview, planning, proposal, or execution, or deep-processing a brain dump.
Use when routing intent into Lev lifecycle work, clarifying an uncertain next step, or invoking and diagnosing the Lev CLI and runtime.
Operate desktop applications on macOS, Windows and Linux through native accessibility trees, using the agent-desktop CLI. Use when an AI agent must see or act on a desktop GUI: click buttons, fill forms, read UI state, navigate menus, toggle checkboxes,…
Architecture reasoning with trade-offs, ADRs, C4, and fitness functions. Use when user asks for system design, tech selection, architecture review, or ADR drafts.
Use when a long brainstorm or brain dump needs deep capture, prior-art routing, and durable next states.
Use when the operator explicitly invokes /horizon or skill://horizon for a long-horizon planning brief, or when lev-plan loads the method as its conditional planning reference.
Use when shaping an idea with the wizard, synthesizing related concepts, defining a candidate ontology, or resuming an idea in .lev.
Use when compiling aligned simple intent or a source-faithful Lev plan into a reviewable slice map or one execution-ready Lev task. Compatibility alias for lev-plan; it does not create or repair broad plans.
Use when current external facts, source-backed comparisons, investigations, or recent community evidence are needed.
Use when triaging a scoped set of pull requests as a merge train - gate each PR for readiness, bounce what is not ready, merge what is ready into one integration branch, then review the whole result once with two models and an architect, fix blocking…
Checkpoint and synchronize the current Git checkout and dirty submodules using Git directly. Semantically merge clear conflicts and ask only when intent is unresolved. Default scope is all dirty work; paths after -- narrow it. --handoff returns a copyable…
Use the codebase knowledge graph for structural code queries. Triggers on: explore the codebase, understand the architecture, what functions exist, show me the structure, find code from plain words, search by meaning, who calls this function, what does X…
Use when working with macOS GUI apps, desktop automation, background clicks, drag or canvas tasks. Agents run JavaScript in one open session through scripts/cu.mjs (code mode, no MCP registration); the engine is OpenAI's Codex Computer Use when the ChatGPT…
Atlassian, Rovo, Robo, Teamwork Graph, TWG, Jira, Confluence; REA reverse engineering, MCP-to-CLI integration; GitHub, gh, issues, pull requests; Notion, ntn, pages, databases; Slack, chat, messaging, Block Kit; Exa, web search, semantic search, contents;…
Maintain a local JSON-backed progress view with live refresh, nested work lists and linked evidence. Use for browser status reports and long-running work dashboards; execution stays with the existing work owner.
Use when designing UX/product flows, researching users or personas, conducting Agentic user research with day-in-the-life simulations, creating wireframes or IA, working with Pencil visuals, or designing agentic CLI UX.
Use when a task, agent brief, UX route, CDO plan, wave, or sprint needs relevant local skill material, or when selected material's applicability and loading must be checked.
Use when coordinating a multi-step goal across agents, skills, Lev lifecycle routes, scheduling, research, implementation, review, or human decisions.
Use when source-faithful planning artifacts need simple host-only, standard, or deep enrichment before SDLC proposal review or a non-coding domain handoff.
Use when the user asks to prove, spike, prototype, POC, validate a production path, compare landing zones, or decide whether an idea should move to propose, monitor, reject, or archive.
Use when verified work is ready for QA, acceptance, checkpointing, learning, recommendation, and handoff.
Run and supervise Lev-aware coding workers across Codex, Claude Code, Gemini, OpenCode, Pi, OmO Native (omo), or oh-my-pi (omp), including CAAM identity handling and single-worker or coder-reviewer SDD modes.
Use when running execution-ready task slices, dispatching verified `lev exec` flows, or reviewing implementation work in the Exec lane.
Convert an explicit user request into a Codex goal and pass its chosen coder, Lev, omo/omp, SDD, or Fable execution flags to $coder.
Use when adversarially reviewing behavior with dynamic e2e evidence: hostile scenario matrices, misleading-success and harness-error checks, bounded QA cycles, cleanup evidence, and repair requests returned to the caller.
Use when selecting UX research, wireframes, UI implementation or review, mobile design, visual direction, graphics, animation, or video methods.
Build inclusive interfaces with WCAG compliance and best practices.
Generate AI images with GPT-Image-2, FLUX, Gemini, Grok, Seedream, Reve and 50+ models via inference.sh CLI. Models: GPT-Image-2, FLUX Dev LoRA, FLUX.2 Klein LoRA, Gemini 3 Pro Image, Grok Imagine, Seedream 4.5, Reve, ImagineArt. Capabilities: text-to-image,…
AI Native Product Designer role - LLM-first workflow, AI code prototyping, Figma as finish line, self-serve research, outcome ownership. Use for leveling, workflow design, and AI readiness reviews. Includes quick reference, full rubric appendix, checklist,…
AI product engineering for shipping LLM features to production.
Help users define AI product strategy. Use when someone is building an AI product, deciding where to apply AI in their product, planning an AI roadmap, evaluating build vs buy for AI capabilities, or figuring out how to integrate AI into existing products.
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