Onboarding

onboarding - the first conversation with omo

Purpose

This skill runs the first conversation a new omo user ever has. You are the guide. Walk the user 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 first reply 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.

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: run senpi --list-tips during the tour, then read and follow give-me-tips for any visible tip the user wants explained from the implementation.
• Interactive UI primitives: real pickers, confirms, inputs, notifications, editors, custom views, and widgets let components ask structured questions instead of burying choices in prose.
• Re-running this tour: onboarding auto-starts once, ever. The user can bring it back any time with the senpi --onboard flag, or shut the auto-start off with the omo-senpi-onboarding-disabled flag.
• The init-deep advisor: after this first session, omo watches each project for AGENTS.md coverage gaps and drift, and proposes an init-deep run only when the numbers justify one. On this first session, you carry that proposal yourself in lane 6.

While the user reacts to the tour, start lane 5: record what you learn about them through the memory tools as you learn it.

2. Migration help

Ask whether the user is coming from another coding agent and would like their setup carried over. This lane is opt-in. If they say no, skip to lane 3.

If they say yes, scrape their existing configuration from as many sources as exist on this machine. Check at least:

• Claude Code: ~/.claude/settings.json, project and global CLAUDE.md files, MCP server definitions in .mcp.json or settings.
• Codex: ~/.codex/config.toml, any AGENTS.md files it manages.
• OpenCode / oh-my-openagent: ~/.config/opencode/opencode.json, ~/.config/opencode/oh-my-opencode.jsonc, project .mcp.json, and existing AGENTS.md files.
• Anything else the user names.

Read what you find, then present one concrete migration plan: which settings map to ~/.omo/omo.json[c], which MCP servers move to the project .mcp.json, which CLAUDE.md content becomes project AGENTS.md content, and which personal facts belong in memory instead of files. Show the plan and WAIT for the user to accept it. Apply nothing before they say yes. If they accept part of it, apply that part only. Record their agent-product history and migration choices through the memory tools.

3. Session archaeology

Ask the user, in your own voice and their language, a question that means: "may I look through your previous coding-agent sessions?" Phrase it naturally; do not read that sentence out like a script. This lane is opt-in. If they say no, skip to lane 4 and base it on nothing.

If they say yes, drive the coding-agent-sessions skill: read its SKILL.md and follow it. Use the bundled finder to list sessions across every platform present on the machine, then go deep on the interesting ones. Mine for:

• how they actually work: hands-on-the-wheel back-and-forth versus long one-shot delegations,
• their planning stance, feeding the seed described in lane 5: opening-message shape (requirements and constraints enumerated upfront, or vague starts?), how they answer agent questions (pick an offered option, answer several at once holistically, redirect, or delegate the decision), how they approve (a verbal yes versus commanding execution), and whether the style shifts by repo or domain,
• what frustrates them: repeated corrections, abandoned sessions, prompts that read as annoyed, in any language,
• how much they run in parallel, and how long their longest sessions run,
• which repos, stacks, and models dominate their history.

Weave in migration suggestions where the history invites them, lightly. When a pattern you find maps to an omo feature from the tour, say so with the evidence: "you corrected the agent about your test runner in nine sessions; memory ends that" lands, a generic pitch does not. Record every durable finding about the user through the memory tools as you go.

4. Value mapping + savings

From the session data gathered in lane 3, give the user quantified estimates of what omo's caching would have been worth on their real workload. Compute, do not guess:

• Quantify token and cost savings only from Senpi JSONL v3 sessions. Use other agent stores for session counts, duration, concurrency, and qualitative work-pattern analysis, not token savings.
• Fetch https://models.dev/api.json. Treat its top level as the provider map. Each provider owns a models object, and each model cost uses input, output, cache_read, and cache_write USD prices per one million tokens. If the API is unavailable, every session has insufficient pricing data.
• Process session files in chronological filename order. Process entries inside a file in JSONL line order. A malformed timestamp skips that entry. Entries whose timestamps differ by at most one millisecond retain JSONL line order.
• Read usage only from message.usage. Map input, cacheRead, cacheWrite, and output exactly. The three input categories are disjoint: total_input_tokens = input + cacheRead + cacheWrite. A session whose total input is zero is skipped.
• Attribute each usage entry to message.provider plus message.model when both are present. Otherwise use the latest model_change provider and modelId. Usage before any model information uses the first later model_change in that file when one exists; otherwise skip it.
• Resolve prices first by exact provider/model composite identity. If that misses, search for an exact model id across provider keys sorted alphabetically, then model keys sorted alphabetically. The first deterministic hit wins. If any required price field is missing, mark that entire session as insufficient data.
• Aggregate usage by provider and model, then compute grand totals. The caching rate is cacheRead / (input + cacheRead + cacheWrite) * 100.
• Compute actual cost as (input * price.input + output * price.output + cacheRead * price.cache_read + cacheWrite * price.cache_write) / 1_000_000. Compute omo savings as cacheRead * (price.input - price.cache_read) / 1_000_000.
• Round only final aggregated values with Math.round(value * 100) / 100. Never round per message, model, or session. Label the result estimate.
• Use real correction or frustration prompts found in lane 3 to demonstrate omo's language-agnostic intent routing. Explain, in the user's language, how a phrase such as "no, that is not what I meant" or its actual non-English equivalent is treated as corrective steering for the active task rather than misread as a disconnected request. Tie each example to the observed transcript and the omo feature that addresses it.

Label the result as an estimate and give exactly one line of methodology, in this shape: "Estimate from your local Senpi session logs: message-level model attribution, models.dev input/output/cache read/cache write pricing, summed across sessions, rounded at the end." One line, then the numbers, then stop.

If the user skipped lane 3, skip this lane too; there is no data to map.

5. Memory recording

This lane has no fixed position: it runs through the entire flow. Whenever any lane teaches you something durable about the user, write it through the memory tools at that moment, not in a batch at the end. Worth recording:

• their host and machine facts relevant to future work,
• their language and communication style,
• which agent products they came from and what they kept from them,
• their stacks, main repos, and working patterns from lane 3,
• their stated preferences and every accept or decline decision from this conversation.

Write durable personal and cross-project facts through the memory tool into system/human.md. Write repository-specific stack, commands, constraints, and migration decisions through the memory tool into system/project.md. Do not edit those backing files directly.

Planning-stance observations from lane 3 are the one exception: write them as SEEDS under a ## Seed heading in system/human/planning-style.md (create the block through the memory tool if absent), never into system/human.md. Seeds are inferences and MUST stay weak:

• Phrase every seed as a hypothesis ("appears to enumerate upfront and delegate the rest"), never a conclusion, and set confidence: low ALWAYS - never medium or high, however consistent the history looks. Behavior in another tool reflects that tool's affordances, not necessarily this user; omo's own planning sessions will confirm or replace these within a few runs.
• Record full provenance on every seed line: the source harness name(s) (e.g. Claude Code, Codex, OpenCode), the model(s) under which the pattern was mainly observed, the session ids mined, and today's date as seeded:. Format: - [YYYY-MM-DD] (seed) appears to <hypothesis + context> <!-- src: <harness>/<session-id>,...; harness: <names>; model: <models>; seeded: YYYY-MM-DD; pattern: inferred-from-external-sessions; confidence: low -->
• Seeds carry interaction STYLE only - never session content, code, file paths, or secrets.

Record facts, not narration. "Prefers Korean, migrated from Claude Code, works one repo at a time in long sessions" is a memory. "The user went through onboarding today" is not.

6. First-session init-deep proposal

On the true first session the advisor component stays quiet, so this proposal is yours to carry. Before saying anything about it, run the same eligibility gate the advisor uses, in this order:

1. The current directory must be inside a git repository. Check with git rev-parse --show-toplevel. Not a repo: ineligible.
2. There must be candidate directories worth documenting: directories within depth 3 of the repo root (excluding node_modules, .git, dist, build, vendor, .next, __pycache__, .venv, target, coverage, third_party) holding at least 8 source files or at least 500 lines of source directly in them. Zero candidates: ineligible, regardless of anything else.
3. Compute coverage: a candidate counts as covered when it or an ancestor up to the repo root has an AGENTS.md. The missing ratio is uncovered candidates over all candidates. Below 0.50: ineligible. A new project with candidates and no AGENTS.md anywhere is missing ratio 1.0 and eligible.

If any gate fails, skip this lane SILENTLY. Do not mention AGENTS.md, do not explain why you are not proposing, do not hint that a check ran. Close the conversation warmly instead.

If all gates pass, ask in the user's language: "want me to set up AGENTS.md for this project?" This is opt-in. On yes, read the init-deep skill at its SKILL.md path and follow it. On no, record the decline through the memory tools and finish the conversation gracefully: a short send-off in their language, an invitation to come back with senpi --onboard, and nothing more.

After init-deep finishes, tell the user in their language that init-deep ran through mass-ulw's DAG orchestration — the work was spread across parallel child agents in dependency-ordered waves rather than done by one session. Then introduce mass-ulw: mass ulw is how you bring that same multi-agent orchestration to any big job, and combined requests such as mass ulw research, mass ulw loop, and mass ulw plan load mass-ulw alongside each of those workflows.