TeamAI — Make Every Team AI Native

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The shared foundation for how your team works, learns, and improves with AI.

TeamAI turns individual AI capabilities into shared team capabilities — across agents, machines, and team members.

Contributors

Thanks to everyone who has contributed to TeamAI!

Made with contrib.rocks.

Quick Start

Send this one line to your AI tool:

Install the teamai skill: https://github.com/Tencent/teamai-cli/tree/main/skills/teamai , load the teamai skill, then set up TeamAI for my team from scratch.

Once TeamAI is set up, just talk to the /teamai skill in your AI tool:

Set up a team from scratch

/teamai Help me set up TeamAI for my team from scratch

Join a team

/teamai Help me join my team's TeamAI, repo URL is https://github.com/yourorg/yourrepo

Share a skill with the team

/teamai Share my xxx skill with the team

Open the dashboard

/teamai Open the TeamAI dashboard

Prefer the command line? (manual setup)

Install

npm install -g teamai-cli

Team admin / solo user

Create a shared-experience repo on your git host (GitHub, GitLab, GitCode, CNB, TGit, or a private Git service), grant write access to team members, then run teamai init https://github.com/yourorg/yourrepo.

No team repo yet? Start from a template pre-loaded with production-ready skills, rules, and review agents. Browse the teamai-hub org, click Fork, then teamai init against your new repo.

Team members

# Choose one, depending on where you want resources installed

# Project-scope init (default, resources installed under the project directory)
cd /path/to/my-project
teamai init https://github.com/yourorg/yourrepo

# Or, user-scope init (resources installed under ~/)
teamai init https://github.com/yourorg/yourrepo --scope user

Once initialized, every AI session automatically pulls the latest skills / rules and other Harness updates published by admins — no manual sync needed.

Full usage guide: docs/usage-guide.md (中文版) — covers everything from team creation to day-to-day use.

Product architecture

Team Execution × Team Context (beta) × Team Improvement (beta):

Layer	Job	In this CLI today
Team Execution	Make every agent work the team's way	init / pull / push, skills, rules, agents, hooks, MCP, env
Team Context (beta)	Make every agent understand the team	recall, learnings, codebase graph, teamwiki...
Team Improvement (beta)	Make every execution improve the team	friction-based share-learnings, sessions, digest, dashboard...

Overview

Agent	Team Execution							Team Context (beta)			Team Improvement (beta)
	skills	rules	docs	env	agents	hooks	mcp	learnings	codebase	teamwiki	usage	sessions	dashboard
Claude Code	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓
Codex	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓
Cursor	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓
GitHub Copilot CLI	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	—	—	—
CodeBuddy	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓
WorkBuddy	✓	✓	✓	✓	—	✓	✓	✓	✓	✓	✓	✓	✓
OpenCode	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	—	—	—
OpenClaw	✓	✓	✓	✓	—	—	—	✓	✓	✓	—	—	—
Hermes	✓	—	✓	✓	—	—	—	✓	✓	✓	—	—	—
DeepSeek Harness	✓	—	✓	—	—	—	—	✓	✓	✓	—	—	—
Qoder	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓
Kiro	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓
ZCode	✓	—	✓	—	✓	✓	✓	✓	✓	✓	✓	✓	✓
Oh My Pi	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	—	—	—

Git providers — GitHub · GitLab · GitCode · CNB · TGit · private Git service.

Distribution Controls

Team-wide settings an admin configures once and delivers to every member on teamai pull:

Capability	Command	What it does
Projects	teamai projects	Bind a working directory to one or more logical projects so it syncs that project's skills, knowledge, and isolated learnings. Orthogonal to roles.
Roles	teamai roles	Define role → namespace mappings so each member syncs only the skills for their role.
Tags	teamai tags	Tag skills / rules so members subscribe to just the tags they need.
Sources	teamai source	Subscribe to additional skill repos — other teams' public repos, or shared/public repos within your own org; subscribed skills sync automatically on pull.

Learnings isolation: learnings/ at the repo root is shared with everyone; learnings/<project-id>/ is project-private. See the usage guide.

Team Execution

One Team. One Harness. Every Agent.

TeamAI keeps skills, rules, docs, and hooks in a shared git repo and distributes them to every member's local AI tools through a "push → review & merge → pull" flow — with support for subscribing to other teams' or shared repos' Harness.

How It Works

teamai push → create branch + MR → reviewer approves + merges
                                         ↓
              SessionStart hook → teamai pull → synced to local AI tools

What Gets Shared

Each resource is delivered to every agent:

Resource	In the team repo	Notes
Skills	skills/<name>/SKILL.md
Rules	rules/*.md
Docs	docs/	Foundational project docs; not all loaded by default (progressive disclosure)
Agents	agents/<name>.yaml, agents/<namespace>/<name>.yaml	Root agents reach everyone; a namespace directory ships only to roles/projects that list it under agents:
Culture	culture.md	Team mission, values, and working principles — injected into each agent's CLAUDE.md / AGENTS.md so every session inherits them
CLAUDE.md	claudemd/*.md
Env	env/	Shared team-level environment variables and switches; do not put secrets here
Hooks	hooks/hooks.yaml	Each hook may carry roles: to reach only members holding one of those roles
MCP	mcp/mcp.yaml	Each server may carry roles: to reach only members holding one of those roles
Packages	teamai.yaml	Currently npm packages and Claude Code plugins only
Models	—	Not implemented for every provider yet

For file formats and full workflows, see the Usage Guide.

Team Context (beta)

Every agent understands how the team works.

Beyond distributing the Harness, TeamAI organizes accumulated team experience and code structure into a searchable knowledge base that the AI recalls automatically when needed.

Automatic Experience Sharing

When a session ends, the Stop hook scores it by friction — signals that the session hit something worth remembering: you interrupted or corrected the AI, denied a tool call, or the AI had to retry failing tools. A long-but-routine session (lots of tool calls, no friction) does not trigger; a session where you actually fought a problem does. If the score is high enough, the AI suggests:

[teamai] This session may contain a problem worth documenting: you interrupted the AI twice, the AI retried failing tools 8 times.

Task: Fix duplicate project-level Hook injection

Consider running /teamai-share-learnings to summarize what you learned and share it with your team.

The hint names the non-zero friction signals that triggered it and, when available, includes a redacted, single-line summary of the first task. The /teamai-share-learnings skill summarizes the session and pushes a learning document directly to the team repo. Each session is prompted at most once. Teams can switch the hint off with sharing.contributeHint.enabled: false in teamai.yaml (members: contributeHintEnabled in local config) while keeping the rest of the Stop hook.

Team Knowledge Recall

Let the AI automatically search accumulated team knowledge before a task. This feature is off by default and must be enabled explicitly — teams can set sharing.recall.enabled: true in teamai.yaml as the default, and members can override locally:

teamai recall enable     # on: deploy the teamai-recall subagent + inject guidance rules
teamai recall disable    # off: remove the subagent and rules
teamai recall status     # show effective state (team default + user override)

Search runs via a subagent: once enabled, teamai pull deploys the built-in teamai-recall subagent into each AI tool's agents/ directory. The AI invokes it before a task — the subagent extracts keywords, runs the search, reads the matched source files, and returns a structured summary of team knowledge. The subagent first runs a relevance precheck (teamai recall --check) and skips retrieval entirely when the task is unrelated to team knowledge. Under the hood it shells out to the teamai recall command, which you can also run manually:

$ teamai recall "port conflict"
[1/2] MR review caught a port-conflict bug ★1 [user]
Author: member-a | Score: 18.5 | Tags: troubleshooting, networking

[2/2] Deployment configuration best practices [project]
Author: member-b | Score: 12.0 | Tags: deploy, config
Matched: conflict | Missing: port

Codebase Knowledge Graph

teamai import parses source repos into a structured graph under teamwiki/, enabling structurally-aware retrieval:

teamai import --from-repo https://github.com/org/repo
teamai import --from-org myorg              # batch import all repos
teamai codebase --extract /path/to/repo     # local extract into teamwiki/
teamai codebase --deep-enrich --project my-service --output /path/to/repo # generate deep knowledge docs
teamai codebase --reconcile --output /path/to/repo # map product docs to code pages
teamai codebase --lint --output /path/to/repo # check the locally extracted graph

Extract writes teamwiki/evidence/code/<project>/_manifest.json even when AI enrichment is skipped or produces nothing, so --deep-enrich can start.

The graph stores components, interfaces, configs, and cross-repo import edges. teamai recall uses it for graph-boosted re-ranking. When a recall hit comes from a codebase page, the result includes a Sources: line listing the relevant source file paths — giving agents a direct starting point for code changes instead of re-exploring the repo.

Edges come from two tracks that run together, with AST results taking precedence on overlap:

• AST track (TypeScript/JavaScript, Python, Go): a WASM tree-sitter parser resolves import/require, call sites, and TS implements clauses to precise file-to-file DEPENDS_ON / REFERENCES / IMPLEMENTS edges (tagged code-ast, with confidence weights).
• Heuristic track (all languages, including Java/Rust): regex-based extraction (tagged code-heuristic), which also covers languages the AST track does not.

The WASM parser is a pure-JavaScript dependency — no native toolchain is required. If it fails to load for any reason, extraction falls back to the heuristic track and records an AST_UNAVAILABLE gap. Set TEAMAI_SKIP_AST=1 to force heuristic-only extraction.

Team Improvement (beta)

Every execution makes the entire team smarter.

Maintenance

As skills and knowledge accumulate, prune what the team no longer uses. teamai recall maintenance archives low-confidence learnings and flags stale skills, rules, and docs for cleanup or updates:

teamai recall maintenance --prune --dry-run      # preview
teamai recall maintenance --prune --archive      # archive unused learnings
teamai recall maintenance --update-quality       # draft updates for stale skills / docs

Insight into how the team actually uses its AI tools, and a starting point for turning session friction into shared skills, rules, and knowledge:

Capability	Command	What it shows
Usage	teamai digest	Weekly team digest — 7-day success, prompt, active-time, estimated cost, cache, and correction trends, plus lifetime totals.
Sessions	teamai session save	Privacy-scrubbed per-session summaries (tool sequence, prompt turns, interventions) that feed the digest's Session Highlights.
Dashboard	teamai dashboard	Unified Overview / Team Execution / Team Context / Team Improvement views with local live sessions, 7-day trends, estimated cost per session, English/Chinese, and light/dark/system themes.
KB Health	teamai dashboard → Team Context / Team Improvement	Coverage by type, top-recalled and silent entries, last-recall month distribution, author contributions, and maintenance; the full /kb-report remains available.

Commands

Command	Description
teamai init	Initialize: OAuth login, link repo, register member, inject hooks
teamai pull	Pull team resources and inject into local AI tools
teamai push	Push local resources to a branch and open a Merge Request
teamai packages [install] [target]	Install declared npm packages and Claude plugins; with a target, also update teamai.yaml. Bare teamai packages installs everything; teamai packages install <target> adds one
teamai status	Show local vs team repo diff and resource counts, including namespaced skills and nested docs
teamai contribute	Share session experience to the team repo's teamai-learnings branch
teamai recall <query>	Search the team knowledge base (BM25 + graph-boost)
teamai recall enable/disable/status	Toggle or check recall state
teamai recall promote [learningId]	Promote a high-confidence learning to formal knowledge (skills/rules/docs)
teamai recall maintenance	Maintain knowledge base health: prune low-confidence learnings, writeback confidence scores, flag stale entries
teamai import	Import knowledge (--dir, --from-repo, --from-org, --from-repo-list, --from-mr)
teamai codebase --extract [path]	Extract code facts and build the local graph under teamwiki/
teamai codebase --deep-enrich	Generate deep knowledge docs from extracted evidence
teamai codebase --reconcile	Reconcile product documentation with extracted code knowledge
teamai codebase --lint	Knowledge graph health check
teamai ci extract-mr --url <url>	CI: extract knowledge from MR, post comments, write after merge
teamai members	List team members
teamai projects	Bind a working directory to one or more logical projects
teamai roles	Manage team roles and namespaces
teamai tags	Manage tag-based skill/rule filtering
teamai skill exclude add/remove/list	Manage skills excluded from local sync (usage guide)
teamai source	Manage skill subscription sources (other teams or your org's shared repos)
teamai remove <type> <name>	Remove a resource and open MR
teamai session save	Record a privacy-scrubbed session summary to a monthly log (--push feeds digest)
teamai digest	Generate weekly team usage digest
teamai doctor	Diagnose configuration issues (--json for CI, hooks and agents)
teamai uninstall	Remove all teamai resources and hooks

License

MIT

Contributing

PRs are welcome! Please read CONTRIBUTING.md first.