core
免费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.
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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.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Summarize what this AI coding session taught you and push it to the team knowledge base.
Write the document in Simplified Chinese, as earlier releases required. Commands, flags, URLs, paths and code identifiers stay as they are.
teamai contribute --file <path> --title "<title>"Copy the template, the frontmatter field table and the tag taxonomy from
{SKILL_DIR}/references/doc-template.md. The frontmatter is required: it is what
makes the document searchable.
# After writing the summary to /tmp/session-summary.md
teamai contribute --file /tmp/session-summary.md --title "Debugging K8s pod startup timeouts"
teamai-learnings branch, under learnings/, with no pull requestteamai pullA member asking to publish a skill ("share this xxx skill with my team") is a
different flow, and it does not need recall: it lives in the core skill, at
"$(teamai skill path core)/references/contribute-member.md". This file
is for turning a session into a learning.
In the files below, {SKILL_DIR} is the directory teamai skill path share 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/doc-template.md | Writing the learning document: template, frontmatter fields, tag taxonomy. |
teamai skill get share --full prints this skill with its reference appended.
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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.
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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.
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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.
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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.
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