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.

Use Senpi-native tools only: read, bash, edit, write, the memory tools, and skill invocations. 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

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