// Plan and execute multi-agent workflows using lionagi's CLI: li o flow (DAG pipelines), li o fanout (parallel workers), and li play (playbook invocations). Use when a task needs multiple agents working in parallel or staged phases.
| name | orchestrate |
| description | Plan and execute multi-agent workflows using lionagi's CLI: li o flow (DAG pipelines), li o fanout (parallel workers), and li play (playbook invocations). Use when a task needs multiple agents working in parallel or staged phases. |
| allowed-tools | ["Bash","Read","Write","Glob","Grep"] |
Plan and execute multi-agent workflows using lionagi's CLI.
| Situation | Command |
|---|---|
| Single task, one agent | li agent MODEL PROMPT |
| Same prompt to N independent workers | li o fanout MODEL PROMPT -n N |
| Staged pipeline with dependencies | li o flow MODEL PROMPT |
| Pre-saved parametric workflow | li play NAME [PROMPT] |
If all subtasks are independent (no output feeds another), use fanout.
If any subtask depends on the output of another, use flow.
li play NAME is sugar for li o flow -p NAME.
# Single agent
li agent claude "Write unit tests for auth.py"
# Fan out 4 parallel workers + synthesize
li o fanout claude "Review this codebase for security issues" -n 4 \
--with-synthesis --save ./out --yolo --bypass
# DAG flow — dry-run first, then execute
li o flow claude "Audit auth, implement fixes, verify with tests" \
--dry-run --effort high
li o flow claude "Audit auth, implement fixes, verify with tests" \
--with-synthesis --save ./flow-out --yolo --bypass
# Run a saved playbook
li play security-audit "JWT middleware" --save ./out
li play list # list available playbooks
control=True.agent_id across ops preserves memory.{save_dir}/{agent_id}/, downstream reads from ../{dep_id}/.depends_on is mandatory for every non-root op.--dry-run before executing — preview the DAG before committing.For detailed documentation, read these companion files in this skill directory:
li agent, li o fanout, li o flow, li play, li team, li invoke| Component | Path |
|---|---|
| CLI entrypoint | lionagi/cli/main.py |
| Flow engine (FlowPlan, FlowOp, FlowAgent) | lionagi/cli/orchestrate/flow.py |
| Fanout engine | lionagi/cli/orchestrate/fanout.py |
| Argparse definitions | lionagi/cli/orchestrate/__init__.py |
| Agent CLI | lionagi/cli/agent.py |
| Teams | lionagi/cli/team.py |
| Invocations | lionagi/cli/invoke.py |
| Scheduler engine | apps/studio/server/scheduler/engine.py |
Systematic debug workflow: research → orchestrate agents → escalate. Suggest when: stuck after 2-3 attempts, unfamiliar tooling, tempted to "try random things", or errors don't match documentation.
Author lionagi playbooks — reusable YAML workflow templates that define parametric agent tasks. Playbooks live at ~/.lionagi/playbooks/ and run via li play <name>. Use when: creating reusable workflows, parameterizing agent tasks, or setting up repeatable pipelines.
Multi-perspective PR review procedure. Plan a minimal DAG of specialists scoped to what the PR actually touches, synthesize with a critic, post ONE consolidated comment per verbosity tier. Pull before any structured PR review so the methodology stays consistent.
General-purpose code review checklist. Use when reviewing any code change without a narrower specialization. Complements security-review, pr-review (multi-perspective), and the other review skills — pull this when you need the standard correctness/quality rubric without a specific angle.
Threat-model rubric for a focused security review of a code change. Use when reviewing auth / input validation / crypto / secrets / supply chain, or as the "security" dimension of a multi-perspective PR review. Pull this skill before starting so severity calibration and coverage stay consistent.
Orchestrate multi-play shows: decompose a complex goal into sequential plays (each a li play invocation), gate each output for quality, adapt the plan based on results, and merge work into an integration branch. Shows are first-class entities in Lion Studio with dedicated UI at /shows.