/onboarding

name: onboarding
description: "Guides a first-time omo user through setup and the core workflow. Use when a new user asks to get started with omo."

onboarding - the first conversation with omo

Purpose

This skill introduces OmO through six lanes, in order: the feature tour, migration help, session archaeology, value mapping, memory recording (which runs through the whole flow, not at the end), and the first-session init-deep proposal. Three of the lanes are opt-in. When the user declines one, move on without argument and without repeating the offer.

Detect the user's language from their message and respond in that language for the rest of the conversation. The skill is written in English; your output is not. Match them exactly, including tone.

Inside the onboarding lanes, use Senpi-native tools only: `read`, `bash`, `edit`, `write`, the `memory` tools, the `onboarding_session_history`, `onboarding_understand` and `onboarding_memory_apply` tools, `tool_search` to find one of those when it is not in your tool list, and skill invocations. Once a lane hands off to another skill (init-deep, or a workflow the user starts), that skill's own tool contract governs: use the tools it requires, such as `create_goal`, `eval`, `task` and the question tool.

Your context carries the `Onboarding scan` block: the engine already ran the one scan the desktop app and `omo setup` use, with one row per item it found on this computer (sign-ins, providers, MCP servers, skills, model choices) and a `Value summary` line. Recent sessions are not in it: they arrive on a later turn as a short background summary, and `onboarding_session_history` has more. When you show the user what omo found, print those rows, one per item, and quote the value summary as computed; never recount by hand. Never assume a tool from another agent product exists here.

Be concrete, never generic. "omo caches your context" is a failure of this skill. "Your last week of Claude Code sessions read 4.7M tokens from cache at a 78% hit rate; here is what that would have cost cold" is the bar.

Entry modes

• **Context on the first prompt:** answer the user's actual request fully first. Then greet them

briefly in their language and offer the tour in one line. Wait for acceptance before starting the lanes below; weave useful onboarding into their task without replacing it.

• **Explicit tour:** `--onboard`, a request for the tour or getting started, or acceptance of the

offer starts the lanes immediately. In TUI, `--onboard` starts the guided conversation at launch; in RPC, it applies to the first real user prompt.

1. Feature tour

Open by introducing yourself and giving a short tour of what omo adds on top of a plain coding agent. The catalog below is baked in at authoring time because the user's machine has no omo or senpi source tree to explore. Present it conversationally, three to five highlights at a time, and let the user ask for depth on any item. Do not dump the whole list as a wall of text.

The baked catalog:

• **Eleven-agent roster**: primary workers, plan specialists, architecture consultation, codebase

explorers, research librarians, and focused review agents are routed by the work rather than forced through one general-purpose persona.

• **The Senpi component layer**: startup config and migration, native status, onboarding,

init-deep advising, anonymous telemetry, ultrawork arming, ulw-execute continuation, ulw-loop continuation, todo fan-out reminders, fallback architecture, comment checking, ast-grep, LSP, task delegation, memory, and live config watching cooperate as independent components.

• **Senpi-native skills**: `init-deep`, `ultrawork`, `ulw-loop`, `ulw-plan`, `ulw-research`,

`hyperplan`, `coding-agent-sessions`, and `give-me-tips` provide reusable workflows that the agent reads and follows only when relevant.

• **Three MCP tiers**: built-in servers, user or project `.mcp.json` servers, and skill-embedded

servers give projects a layered tool surface without forcing every integration into core.

• **Team mode**: cooperating agent sessions can share work, messages, and task state when the

selected omo harness exposes that surface.

• **Goal and boulder state**: durable objective and work-plan state let long work resume from

recorded progress instead of relying on conversation context alone.

• **Ultrawork and the `ulw` keyword**: don't try to memorize the workflows up front — you'll

pick them up by using them. For now, remember one word: `ulw`. Put `ulw` in your prompt and the agent gets sharper: it plans, researches, loops, and fans out work with evidence-bound continuation until the goal is actually proven. Whenever something makes you curious — a tip you saw, a feature you want explained — ask `give-me-tips` and it walks you through it.

• **Mass-ulw**: `mass ulw` opens dependency-ordered multi-agent DAG orchestration — many child

agents run in parallel waves, some waiting on others, so big jobs finish faster. Combined requests such as `mass ulw research` load both mass-ulw and ulw-research at once.

• **The fallback architect**: refusal metadata can route the unresolved engineering question

through an architecture consultation lane while the active model continues execution.

• **Memory**: dedicated memory tools record durable user and project facts so later sessions begin

with the right stack, preferences, and working habits.

• **Telemetry**: privacy-bounded anonymous lifecycle signals and local preview commands make omo

behavior measurable and auditable, with documented opt-outs.

• **Init-deep**: hierarchical `AGENTS.md` generation, snapshot state, local or committed mode, and

later drift detection keep project instructions aligned with the codebase. On larger repositories init-deep runs through mass-ulw's DAG map-reduce, so the work is spread across parallel scanner and writer agents instead of one session.

• **Tips with a live source of truth**: lis