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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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  • SKILL.md2.8 KB
  • references/doc-template.md1.1 KB

SKILL.md(原文)

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Contribute — share what a session taught you with the team

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.

When to Use

  • When teamai suggests this session has valuable content worth sharing
  • When you've solved a tricky problem and want to document the solution
  • When you've discovered a useful workflow or pattern
  • After a long session with diverse tool usage

How It Works

  1. Summarize: review the tools used, the problems solved and the patterns found in this session
  2. Write the document: a Markdown document (language as above) covering:
    • What the task or problem was
    • The key decisions and why they were made
    • The solution, workaround or pattern discovered
    • Which tools or skills proved especially useful
    • Pitfalls and things to watch out for
  3. Save it: write the document to a temporary file
  4. Push it to the team: run teamai contribute --file <path> --title "<title>"

Document Template

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.

Example

# After writing the summary to /tmp/session-summary.md
teamai contribute --file /tmp/session-summary.md --title "Debugging K8s pod startup timeouts"

Important

  • Run this as a sub-agent (Agent tool) to avoid polluting the main session's context
  • The document is pushed to the team repo's teamai-learnings branch, under learnings/, with no pull request
  • Team members will see it on their next teamai pull
  • Keep summaries concise and actionable — this is a knowledge base, not a diary

Publishing a reusable skill instead

A 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.

References

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.

FileWhen to load it
{SKILL_DIR}/references/doc-template.mdWriting the learning document: template, frontmatter fields, tag taxonomy.

teamai skill get share --full prints this skill with its reference appended.

レビュー

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。

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core

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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.

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。

Tencent/teamai-cli5,5012026年10月15日 更新

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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.

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。

Tencent/teamai-cli5,5012026年10月15日 更新

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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.

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。

Tencent/teamai-cli5,5012026年10月15日 更新

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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.

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。

Tencent/teamai-cli5,5012026年10月15日 更新

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