claude

orchestrate

Multi-agent orchestration for lionagi: plan DAG workflows, author playbooks, run multi-play shows with quality gates.

412 on GitHubby HaiyangLiv1.1.0Apache-2.0updated yesterday
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Plugin orchestrate from marketplace lionagi in ohdearquant/lionagi. Commands read from the repo's manifest — yesterday.

This plugin runs code on your machine — it starts local MCP servers. The exact commands are listed below; read them before installing.

What it installs

Skills (12)

  • debug

    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.

  • fanout

    Fan one task out to multiple independent lionagi workers through `fanout.submit`, optionally synthesizing their answers. Use when several parallel perspectives can work without dependency handoffs.

  • flow

    Plan and run a dependency-aware lionagi DAG through `flow.submit`, including dry runs and reactive follow-up capacity. Use when work has stages, dependencies, parallel branches, or a final synthesis step.

  • orchestrate

    Plan and execute multi-agent workflows through lionagi's orchestration MCP tool: flow.submit (DAG pipelines), fanout.submit (parallel workers), agent.submit (one agent), and play.submit (saved playbooks). Use when a task needs multiple agents working in parallel or staged phases.

  • play

    Run an existing lionagi playbook through the orchestration MCP server and retrieve its background result. Use when a saved `.playbook.yaml` workflow should be executed with concrete input or declared arguments.

  • playbook

    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

  • pr-review

    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.

  • review

    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.

  • security-review

    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.

  • show

    Orchestrate multi-play shows: decompose a complex goal into sequential plays (each a play.submit run through the plugin's MCP server), gate each output for quality, adapt the plan based on results, and merge work into an integration branch. Shows are rows in Studio's state database, readable over it

  • summarize

    Mid-session context capture and proactive decision/pattern capture. Use when: significant progress made but session continues, approaching context limits, switching topics, checkpoint learnings, significant decisions made, patterns emerge, or session is winding down. Lighter than a full session summ

  • tdd

    Guide test-driven development workflow. Suggest when: "test first", "TDD", "write tests before", "red green refactor" mentioned, or when implementing features or bug fixes where a test-first approach is beneficial. Enforces Red-Green-Refactor cycle.

Subagents (2)

  • critic

    Adversarial quality gate that hunts for flaws and returns a formal verdict. Use before shipping an artifact, when a result looks too good, or when a claim needs evidence rather than agreement.

  • orchestrator

    Decomposes a goal into a multi-agent DAG, runs it, and synthesizes the results. Use when work needs several agents in a dependency order rather than one pass, or when a task should be split, dispatched and recombined.

MCP servers (1)

  • lion

    runs: uvx --from lionagi[mcp] li mcp serve

View source on GitHub khive.ai

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