TeamAI day 0 and repo lifecycle: create a team repo as admin, join an existing team as a member, manage members, roles, MCP and env, and uninstall. Loaded on demand by the teamai discovery stub.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
You run the commands; the user only makes choices when you ask. They may not know Git — never explain branches, commits or clones.
node --version # must be >= 20
teamai --version # install once with: npm install -g teamai-cli
Then pick the flow. A user setting up a new team becomes its admin and creates
the repo; a user joining an existing team needs a repo URL from their admin. A
would-be member without a URL is still the join flow — join-member.md tells
them how to ask for it. Do not send them to the create-repo flow because the URL
is missing.
| The user wants to… | Load this |
|---|---|
| Set up TeamAI for a team from scratch (create the repo) | {SKILL_DIR}/references/setup-admin.md |
| Join their team, with or without a repo URL | {SKILL_DIR}/references/join-member.md |
| Publish or update skills, rules, MCP, env; invite members; manage roles | {SKILL_DIR}/references/manage-admin.md |
| Remove TeamAI from this machine | {SKILL_DIR}/references/uninstall.md |
| Publish one skill or contribute a doc | "$(teamai skill path core)/references/contribute-member.md" |
| Anything that breaks along the way | "$(teamai skill path core)/references/troubleshooting.md" |
Supported Git providers are Tencent TGit, GitHub, GitLab and CNB;
{SKILL_DIR}/references/setup-admin.md carries the detection probe, the sign-in
and create-repo URLs, and the per-provider caveats, and points at
{SKILL_DIR}/references/provider-tgit.md for everything TGit-specific.
https://github.com/your-org/your-repo). Never the owner/repo short form.--agent to restrict the
install. Let teamai init set up every AI tool already installed (omitting
--agent gives an interactive picker; select all detected tools). After init,
report which agents were set up — in the user's language, which tools now
auto-start TeamAI, which detected tools were skipped and why (e.g. CodeBuddy
design), and any installed hooks that still need trust (e.g. Codex with
automatic trust disabled or unavailable). Verify the real per-tool result with
teamai doctor and teamai hooks list.teamai init does not pull. It saves the config and installs hooks. It does
not copy team skills, rules or MCP. In workspace scope it creates a tool directory
only for a tool named with --agent, or picked when a person runs it in a
terminal. Those resources arrive on that tool's next session start, or when you
run teamai pull. Run from an agent shell (no terminal), a workspace-scope init
without --agent creates no tool directory: the directory appears when the user
opens that tool in the project, and that session start pulls.teamai doctor. Every setup or onboarding flow ends by running
it and resolving what it reports before you call the job done.Every public command and every flag, including the flags --help hides, is listed in
teamai skill get core --full under references/commands.md. Do not guess a flag:
there is no member-invite flag, for instance — inviting happens on the Git
platform's website, as manage-admin.md describes.
In the files below, {SKILL_DIR} is the directory teamai skill path setup prints; a reference file you open on its own writes that directory as SKILL_DIR in braces.
| File | When to load it |
|---|---|
{SKILL_DIR}/references/setup-admin.md | Creating a team repo: provider detection, auth, repo creation, first push. |
{SKILL_DIR}/references/join-member.md | Joining an existing team from a repo URL. |
{SKILL_DIR}/references/manage-admin.md | Day-to-day admin: publishing resources, roles, projects, MCP, env, members. |
{SKILL_DIR}/references/uninstall.md | Removing TeamAI from a machine or from one agent. |
{SKILL_DIR}/references/provider-tgit.md | Tencent TGit: reachability probe, gf install and login, repo creation on init. |
teamai skill get setup --full prints this skill with all five appended.
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
TeamAI daily workflow: route a /teamai request, sync with pull and push, inspect status, diagnose with doctor, and reach the specialized workflows. Loaded by the teamai discovery stub.
日本語の概要は準備中です。原文の説明を表示しています。
Turn a session into a team learning: summarize what was solved, discovered or worked around, and publish it to the team knowledge base with `teamai contribute`. Loaded on demand by the teamai discovery stub, and by the friction reminder that ends a session worth sharing.
日本語の概要は準備中です。原文の説明を表示しています。
Make every team AI native — TeamAI syncs a team's AI skills, rules, docs and env across AI coding tools. Use when the task operates on team-shared AI configuration or team knowledge: setting up a team repo, joining one, managing members, syncing with pull or push, or checking team status. Also use to build or query a codebase knowledge base for a large multi-repo project (architecture analysis, architecture reverse-engineering, code-to-knowledge, team-wiki-codebase, architecture wiki), and to share what a session taught you back to the team (share session learnings, contribute a learning, share what I learned with my team), including after a friction reminder. Triggers include "set up teamai", "join the team repo", "sync team skills", "team wiki", "share what I learned", and running /teamai. Talking about a team needs no skill; operating on what the team shares does.
日本語の概要は準備中です。原文の説明を表示しています。
Make AI truly understand large codebases: for multi-repository, multi-microservice projects that have evolved over years, run architecture reverse-engineering + a Graph RAG graph + multi-language AST to compress a huge codebase into a structured knowledge base, where every conclusion traces back to a code line and every relation carries a confidence label. Suited to projects with 10+ repositories or microservices that AI cannot understand globally by reading the code directly. Triggers: architecture analysis, architecture reverse-engineering, codebase knowledge base, code-to-knowledge, architecture wiki, large multi-repo codebase. Loaded on demand by the teamai discovery stub.
日本語の概要は準備中です。原文の説明を表示しています。