mcpField
A
ae-team

ae-team

github.com/thinkingaiagenticengineAgent workflows21859 installsUpdated 6d ago
Repository

About ae-team

ae-team is a Skill for managing AI Agent teams and running TeamRuns through the AE CLI. It supports team configuration and multiple execution modes, with commands to monitor runs and retrieve their results and artifacts.

Key features

  1. 01List, create, update, and delete teams
  2. 02Generate team configuration drafts with AI
  3. 03Browse built-in team templates
  4. 04Start, watch, chat with, or cancel TeamRuns
  5. 05Reply to waiting runs and retrieve results

Use cases

  1. 01Create a team configuration from a goal description
  2. 02Run an existing team on a task and monitor progress
  3. 03Continue a multi-turn team session after a question
  4. 04Retrieve artifacts from a completed TeamRun

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/thinkingaiagenticengine/ae-cli --skill ae-team

After installing, type/ae-teamin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

references/
ai-generate.md
create.md
delete.md
list-projects.md
list-templates.md
list.md
run-artifacts.md
run-cancel.md
run-chat.md
run-reply.md
run-result.md
run-start.md
run-watch.md
update.md

Details

From the project README

ae-team

CRITICAL — Before running any +<command> command, you MUST first read the corresponding references/<command>.md. The reference filename equals the command name without the leading +, for example +run-start → references/run-start.md. CRITICAL — Never guess team IDs, run IDs, or config JSON structures. Always use +list or +list-templates to discover real resources first. CRITICAL — For the core Agent workflow (find → start → poll → artifacts), follow Workflow A in the Typical Workflows section below.

Global AE CLI Rules

AE CLI (ae-cli) is the command-line tool for the AE (Agentic Engine) analysis platform.

Global parameters:

ParameterDescription
--format <json|table>Output format. Default is JSON.
--jq <expr>jq filter expression for JSON output.
--host <url>Override the active AE host. Available on every command and may be placed after the subcommand, e.g. ae-cli team +<command> --host <url>.
--yesSkip confirmation for high-risk-write (delete) operations.
--dry-runShow request details without executing.

Output and errors:

  • Successful commands return machine-readable JSON by default. Envelope may include optional _notice.host_compat.
  • Failed commands return { "ok": false, "error": { "type": "...", "message": "...", "hint": "..." } } and exit non-zero.
  • CRITICAL — Host compat (do this first): If stderr or _notice.host_compat has a version warning, open the user reply with ⚠️ and quote npm i -g / npx skills add (or update-cluster) lines verbatim, then list projects/results. Never omit when summarizing. Soft tip; ok: true may still include the notice.

Safety constraints:

  • Read commands (+list, +list-templates, +list-projects, +run-result, +run-artifacts, +ai-generate) can execute directly once required IDs are known.
  • Ordinary write commands require explicit user intent but no CLI confirmation. Only high-risk-write commands use the confirmation gate and may receive --yes after explicit user authorization.
  • Never invent team IDs, run IDs, agentId values, mcpServerIds, skillIds, knowledgeBaseIds, project IDs, or any resource identifiers. Discover them with list commands or accept them from the user.

When to Use

Use ae-team for all AI Agent Team work:

  • Team management: list available teams, create/update/delete a team, generate a team config draft with AI, browse templates.
  • TeamRun execution: start a run, interact in chat mode, cancel a run, reply to a waiting run, poll result until completion, retrieve artifacts.

If the user's intent is data analysis, audience management, metadata governance, or DataOps, switch to ae-analysis / ae-dataops / ae-engage.

Command Format

ae-cli team +<command> [options]

All commands live under the team service. Quick help:

ae-cli team --help
ae-cli team +list --help
ae-cli team +run-start --help

Tool Groups (13)

Team Management (7)
  • +list (doc) — list all visible teams
  • +create (doc) — create a new team with a TeamConfig
  • +update (doc) — patch one or more fields of an existing team
  • +delete (doc) — delete a team (409 if runs are active)
  • +ai-generate (doc) — AI-generate a team config draft from a goal description
  • +list-templates (doc) — browse built-in team templates
  • +list-projects (doc) — list projects available to the current user
TeamRun Execution (7)
  • +run-start (doc) — start a new TeamRun
  • +run-watch (doc) — stream a TeamRun via SSE (preferred over polling)
  • +run-chat (doc) — chat with a team (multi-turn, auto-resume)
  • +run-cancel (doc) — cancel a running TeamRun
  • +run-reply (doc) — reply to a run in waiting_user state
  • +run-result (doc) — get the full result of a TeamRun (fallback polling)
  • +run-artifacts (doc) — list artifacts produced by a TeamRun

TeamRun Status Reference

StatusDescription
pendingQueued, waiting to start
runningActively executing
waiting_userPaused — +run-watch exits with code 2; read pendingQuestion from stdout, present to user, then call +run-reply
waiting_approvalPaused — waiting for approval
pausedManually paused
completedFinished successfully
failedExecution failed
cancelledCancelled by user

Terminal statuses: completed, failed, cancelled. Poll +run-result until one of these is reached.

Typical Workflows

Workflow A — Start an existing team and wait for results
# 1. Discover available teams
ae-cli team +list

# 2. (Optional) Discover project IDs if needed
ae-cli team +list-projects

# 3. Start a run
ae-cli team +run-start --team-id <team_id> --input "分析上周用户留存数据"

# 4. Stream until done (blocks; no polling needed)
ae-cli team +run-watch --id <run_id>
# exit 0 → completed/partial_success → go to step 5
# exit 1 → inspect the terminal entity or error; for connection errors, use +run-result before considering a new run
# exit 2 → waiting_user → go to step 4a

# 4a. Handle waiting_user: read pendingQuestion from stdout, get user's answer, reply, re-watch
ae-cli team +run-reply --id <run_id> --input "<user_answer>"
ae-cli team +run-watch --id <run_id>   # repeat until exit 0 or 1

# 5. Retrieve artifacts
ae-cli team +run-artifacts --id <run_id> --include-content true
Workflow B — AI-generate a config, then create and run
# 1. Generate a draft config
ae-cli team +ai-generate --prompt "需要一个分析用户行为并自动生成留存报告的团队"

# 2. Create the team (paste / adjust the returned config)
ae-cli team +create --name "留存分析团队" --config '<config_json>'

# 3. Start a run
ae-cli team +run-start --team-id <new_team_id> --input "分析本月留存"
Workflow C — Multi-turn chat
# First turn
ae-cli team +run-chat --team-id <team_id> --input "帮我分析DAU趋势"

# If run status is waiting_user, reply:
ae-cli team +run-reply --id <run_id> --input "请重点分析周末下降原因"

# Continue same session
ae-cli team +run-chat --team-id <team_id> --session-id <session_id> --input "给出优化建议"
Workflow D — Use a template to create a team
# 1. Browse templates
ae-cli team +list-templates --locale zh

# 2. Create from a template's config
ae-cli team +create --name "我的分析团队" --config '<template_config>'

Quick Verification

ae-cli team --help

Automation lifecycle

For Agent or team automation schedules, immediate execution, stop, retry, deletion, history and read-only results, use ae-automation. Existing Agent automation aliases remain compatible.

Comments

Markdown supported