Tencent/teamai-cli

TeamAI — Make Every Team AI Native

English | 中文 | 日本語 | 한국어 | ไทย

TeamAI manages your team's skills, rules, MCP, and knowledge across Claude Code, Codex, CodeBuddy, WorkBuddy, OpenCode, Cursor, and other AI agents.

Contributors

Thanks to everyone who has contributed to TeamAI!

Made with contrib.rocks.

Quick Start

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	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓
CodeBuddy	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓
WorkBuddy	✓	✓	✓	✓	—	✓	✓	✓	✓	✓	✓	✓	✓
OpenCode	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	—	—	—
OpenClaw	✓	✓	✓	✓	—	—	—	✓	✓	✓	—	—	—
Hermes	✓	—	✓	✓	—	—	—	✓	✓	✓	—	—	—
DeepSeek Harness	✓	—	✓	—	—	—	—	✓	✓	✓	—	—	—
Qoder	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓
Kiro	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓
ZCode	✓	—	✓	—	✓	✓	✓	✓	✓	✓	✓	✓	✓

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// 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//SKILL.md | | | Rules | rules/*.md | | | Docs | docs/ | Foundational project docs; not all loaded by default (progressive disclosure) | | Agents | agents/.yaml, agents//.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//_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 | Web dashboard showing live sessions and local 7-day trends compared with the prior 7 days. | | KB Health | teamai dashboard → KB Health | Built-in dashboard page reporting knowledge-base usage & health — coverage by type, top recalled entries, silent entries, recall trend, author contributions, and a maintenance console. |

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 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 team repo | | teamai recall | 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 | 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 | 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 | | teamai uninstall | Remove all teamai resources and hooks |

License

MIT

Contributing

PRs are welcome! Please read CONTRIBUTING.md first.