Author lionagi playbooks — reusable YAML workflow templates that define parametric agent tasks. Playbooks live at ~/.lionagi/playbooks/ and run via the `play.submit` MCP verb (or `li play <name>` from a lionagi checkout). Use when: creating reusable workflows, parameterizing agent tasks, or setting up repeatable pipelines.
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Author lionagi playbooks — reusable YAML workflow templates that define parametric agent tasks. Playbooks live at ~/.lionagi/playbooks/ and run via the `play.submit` MCP verb (or `li play <name>` from a lionagi checkout). Use when: creating reusable workflows, parameterizing agent tasks, or setting up repeatable pipelines.
allowed-tools
["Bash","Read","Write","Glob","Grep"]
Authoring Lionagi Playbooks
A playbook is a .playbook.yaml file that defines a reusable, parametric agent
workflow. Install it in ~/.lionagi/playbooks/ on the machine the MCP server runs
on — that filesystem step is the same whichever interface runs the playbook.
To run it, call the mcp__plugin_orchestrate_lion__request tool with play.submit. Ask for
the fingerprint first, naming the playbook — play.submit's schema depends on which playbook
you name, because a playbook's own declared arguments are resolved into it:
{"ops":[{"op":"play.submit","args":{"playbook":"hello","prompt":"what is a monad?"},"schema_fingerprint":"<from the help call above>"}]}
The fingerprint is a sibling of args, never a key inside it. Omit it and the op is
refused with stale_schema and no run starts; nest it inside args and it is not read, so
the same refusal repeats and the failure reads as idempotent.
If you're working inside a lionagi checkout, the CLI equivalent is
li play <name> "<prompt>", which expands to li o flow -p <name> "<prompt>".
Minimum Viable Playbook
name:hellodescription:Greetandansweraquestion.model:claude-code/sonnet-4-6prompt:|
You are a patient teacher. Explain the following topic in plain language,
with one concrete example.
Run it with play.submit:
{"ops":[{"op":"play.submit","args":{"playbook":"hello","prompt":"what is a monad?"},"schema_fingerprint":"<from help={\"verb\": \"play.submit\", \"playbook\": \"hello\"}>"}]}
Inside a lionagi checkout: li play hello "what is a monad?"
The positional text is appended to the prompt with a blank line because the
template contains no {input} placeholder. That is the only behaviour difference
from a template that declares {input} explicitly.
Authoring Checklist
Filename: <name>.playbook.yaml
Location: ~/.lionagi/playbooks/
name: equals the filename stem — nothing validates the two against each other, and the
CLI resolves by filename, so a mismatch is silent rather than an error
description: is one clear sentence
Choose routing deliberately: model: and agent: are optional; when both are omitted,
flow uses its default orchestrator configuration. If agent: is set, verify that profile
exists on the machine running the MCP server
prompt: references only declared args keys and optional {input}
Every args entry declares type, default, and help. A supplied type is validated;
omitting these fields yields str, null, and a generated help line respectively
No dashed keys inside args:
team_mode and team_attach are not both set
workers is 1–32 if set; max_ops is 0–50 if set
If reactive follow-ups are enabled with a nonzero max_ops, leave room below the cap;
the initial plan and spawned assignments share the same budget
Dry-run check: play.submit with dry_run: true plans without executing (call
help=true first to confirm the argument name for your published server version)
Help check (lionagi checkout only): li play <name> --help always lists fields from
args:; controls the usage line and can define fallback flags when no
block exists. Over MCP, qualified playbook help resolves the custom arguments
Companion Files
File
Contents
field-reference.md
Complete field table, args schema, template interpolation rules, team semantics, reserved names, pitfalls, source code reference
examples.md
Three annotated example playbooks (minimal, audit, pr-review) plus the examples directory index
patterns.md
Orchestration patterns and advanced composition strategies