debug
Systematic debug workflow: research → orchestrate agents → escalate. Suggest when: stuck after 2-3 attempts, unfamiliar tooling,…
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
$ npx -y skills add ohdearquant/lionagi --skill playbook --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/playbookContext preview
The summary Claude sees to decide when to auto-load this skill.
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
name: playbook description: > 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]
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:
{"help": {"verb": "play.submit", "playbook": "hello"}}{"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>"`.
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name: hello description: Greet and answer a question. model: claude-code/sonnet-4-6 prompt: | 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.
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CLI resolves by filename, so a mismatch is silent rather than an error
flow uses its default orchestrator configuration. If `agent:` is set, verify that profile exists on the machine running the MCP server
omitting these fields yields `str`, null, and a generated help line respectively
the initial plan and spawned assignments share the same budget
`help=true` first to confirm the argument name for your published server version)
`args:`; `argument-hint` controls the usage line and can define fallback flags when no `args:` block exists. Over MCP, qualified playbook help resolves the custom arguments
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| 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 |
lionagi is a governed multi-agent orchestration framework. Build agent workflows in Python with typed, inspectable state, or run them straight from the terminal with the li CLI: single agents, parallel fan-outs, and DAG flows where an orchestrator plans
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