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Run npx skillmds@latest add arcasilesgroup/ai-onboard in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

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Is AI Onboard free to use?

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Who published AI Onboard?

arcasilesgroup (@arcasilesgroup) published this skill. Their other Agent Skills are listed on their SkillMD profile.

AI Onboard by arcasilesgroup — Agent Skill

Name: AI Onboard
Author: arcasilesgroup

AI Onboard

Onboards humans to a project: architecture tours, topic search, decision archaeology, structured new-team-member orientation. Read-only — never modifies code. Trigger for 'where does auth happen', 'what is the architecture here', 'history of this decision', 'onboard me to this repo', 'tour the codebase'. Not for agent session bootstrap; use /ai-start instead. Not for code-level explanation; use /ai-explain instead.

File contents

Onboard

Onboards humans to a project through architecture tours, topic search, decision archaeology, and structured new-team-member orientation. Read-only — never modifies code; use for questions like "where does auth happen" or "tour this codebase".

Quick start

/ai-onboard tour                # architecture overview
/ai-onboard find auth           # find where auth happens
/ai-onboard history DEC-003     # decision archaeology
/ai-onboard onboard             # structured new-member onboarding

Workflow

Dispatch the ai-onboard agent (.claude/agents/ai-onboard.md) for any tour / find / history / onboard request touching >= 1 subsystem — strictly read-only. Mode procedures (§10.7 Clean Code — clarity over cleverness):

Mode	Procedure
`tour`	Map dirs/entry points/config; detect stack; ASCII diagram (boundaries, deps, data flow); explain key patterns; `git log --oneline` for evolution; flag gotchas; suggest next paths.
`find [topic]`	Search source+config+docs; check `decision-store.json` + `.ai-engineering/specs/`; present `file:line` refs + context; answer "where does X happen?".
`history [decision]`	Search `decision-store.json`, `git log --all --grep`, `specs/`; reconstruct what was known + constraints + alternatives; assess current relevance; do NOT recommend — present analysis, let the developer decide.
`onboard`	Map structure; identify stack; discover patterns; find key files; review `.ai-engineering/standards/`; Socratic checkpoint per phase (max 2 questions); personalize to the developer's interest.

Pitfalls: never decide for the developer (present tradeoffs); never write code during a tour; cap Socratic questions at 2 per interaction; match teaching to the developer's level (Bloom's cues).

Examples

Example — architecture tour for a new team member

User: "give me an architecture tour of this repo, I'm new"

/ai-onboard tour

High-level overview, module ownership map, key boundaries, ASCII data flow, suggested deeper-dive paths. Read-only.

Integration

Calls: /ai-explain (3-tier depth). Reads: decision-store.json, framework-events.ndjson, manifest.yml. See also: /ai-start (agent bootstrap), /ai-explain (code-level), /ai-research (external evidence).

$ARGUMENTS

---name: ai-onboarddescription: Onboards humans to a project: architecture tours, topic search, decision archaeology, structured new-team-member orientation. Read-only — never modifies code. Trigger for 'where does auth happen', 'what is the architecture here', 'history of this decision', 'onboard me to this repo', 'tour the codebase'. Not for agent session bootstrap; use /ai-start instead. Not for code-level explanation; use /ai-explain instead.---# OnboardOnboards humans to a project through architecture tours, topic search, decision archaeology, and structured new-team-member orientation. Read-only — never modifies code; use for questions like "where does auth happen" or "tour this codebase".## Quick start```/ai-onboard tour # architecture overview/ai-onboard find auth # find where auth happens/ai-onboard history DEC-003 # decision archaeology/ai-onboard onboard # structured new-member onboarding```## WorkflowDispatch the `ai-onboard` agent (`.claude/agents/ai-onboard.md`) for anytour / find / history / onboard request touching >= 1 subsystem —strictly read-only. Mode procedures (§10.7 Clean Code — clarity overcleverness):| Mode | Procedure ||------|-----------|| `tour` | Map dirs/entry points/config; detect stack; ASCII diagram (boundaries, deps, data flow); explain key patterns; `git log --oneline` for evolution; flag gotchas; suggest next paths. || `find [topic]` | Search source+config+docs; check `decision-store.json` + `.ai-engineering/specs/`; present `file:line` refs + context; answer "where does X happen?". || `history [decision]` | Search `decision-store.json`, `git log --all --grep`, `specs/`; reconstruct what was known + constraints + alternatives; assess current relevance; do NOT recommend — present analysis, let the developer decide. || `onboard` | Map structure; identify stack; discover patterns; find key files; review `.ai-engineering/standards/`; Socratic checkpoint per phase (max 2 questions); personalize to the developer's interest. |Pitfalls: never decide for the developer (present tradeoffs); never writecode during a tour; cap Socratic questions at 2 per interaction; matchteaching to the developer's level (Bloom's cues).## Examples### Example — architecture tour for a new team memberUser: "give me an architecture tour of this repo, I'm new"```/ai-onboard tour```High-level overview, module ownership map, key boundaries, ASCII dataflow, suggested deeper-dive paths. Read-only.## IntegrationCalls: `/ai-explain` (3-tier depth). Reads: `decision-store.json`,`framework-events.ndjson`, `manifest.yml`. See also: `/ai-start` (agentbootstrap), `/ai-explain` (code-level), `/ai-research` (externalevidence).$ARGUMENTS

Frequently asked questions