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Arcanea-LabsArcanea-Labs/Starlight-Intelligence-System/skills/orchestration/multi-agent-coordination/SKILL.md

orchestration/multi-agent-coordination

Skill10 starsChanged 2 months agoMITView on GitHub

Use when multiple Starlight agents need to collaborate on a related task — handles agent selection, task decomposition, dependency management, and result aggregation across the council. Default skill for Starlight Orchestrator.

1 other project uses a version of this.

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The file

We split this skill into the rules it gives an agent

3 rules. About 0 are common, 3 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈3

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Default for: Starlight Orchestrator

    line 15
  2. Distinctive:

    Check for conflicts or contradictions

    line 44
  3. Distinctive:

    Ensure no agent's contribution is lost

    line 47

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Multi-Agent Coordination

"Many minds, one mission. Coordination is the multiplier."

When This Skill Activates

  • Tasks requiring multiple agents to collaborate
  • Keywords: "coordinate", "agents", "parallel", "team", "collaborate"
  • Default for: Starlight Orchestrator

What This Skill Does

Manages the coordination of multiple Starlight agents working on related tasks. Handles agent selection, task decomposition, dependency management, and result aggregation.

Procedures

Procedure 1: Agent Selection

  1. Analyze the task for domain requirements
  2. Match domains to agent expertise (see Agent Registry)
  3. Assess if agents need to work in parallel or sequence
  4. Calculate total token budget for all agents
  5. Confirm agent selection is minimal (no unnecessary agents)
  6. Brief each agent with their specific sub-task

Procedure 2: Task Decomposition

  1. Break the main task into discrete sub-tasks
  2. For each sub-task, identify: owner agent, dependencies, expected output
  3. Create dependency graph
  4. Identify critical path
  5. Optimize for parallel execution where possible
  6. Set checkpoints for progress monitoring

Procedure 3: Result Aggregation

  1. Collect outputs from all agents
  2. Check for conflicts or contradictions
  3. Apply the Synthesis Protocol (see core/SYNTHESIS_PROTOCOL.md)
  4. Merge into unified response
  5. Ensure no agent's contribution is lost
  6. Present coherent result to user

Integration Points

  • Vault: Operational Vault (coordination patterns)
  • Agents: Orchestrator (primary), Prime (council mode)
  • Engine: Orchestration Engine (core/ORCHESTRATION_ENGINE.md)

Quality Criteria

  • Were the minimum necessary agents used?
  • Were dependencies correctly identified?
  • Were parallel opportunities exploited?
  • Was the final output coherent, not fragmented?

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