Skip to content
cccskills
FreePublished on GitHub

orchestrator

Coordinate multiple AI agents in Diverga for parallel research, reviews, and methodology comparisons. Manage team creation, progress, and results, or run VS Arena debates where different research perspectives critique recommendations before the user chooses an approach.

OriginalShow the original description

Unified Agent Teams orchestrator for Diverga v12.0.0. Manages Agent Teams creation, VS Arena debate, and subagent dispatch. Single entry point for all parallel/debate workflows. Replaces research-orchestrator and vs-arena skills. Triggers: orchestrator, agent team, create team, parallel agents, debate, competing, collaborate, VS Arena

Run score Not tested

See how to install

Good for

  • Comparing research methods from different perspectives
  • Reviewing a methodology section from several angles
  • Retrieving papers from multiple databases in parallel
  • Assigning research tasks to a custom agent team

Files included (1)

  • SKILL.md9.4 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

Diverga Orchestrator

Role

Execution layer (HOW). Receives agent IDs, decides execution strategy, manages lifecycle.

Does NOT handle:

  • Paradigm detection (research-coordinator)
  • Checkpoint enforcement (research-coordinator)
  • Agent selection logic (research-coordinator)

No circular dependency: coordinator calls orchestrator, never reverse. Orchestrator receives agent IDs and executes; it does not call back into the coordinator.


Invocation

  1. Explicit: /diverga:orchestrator
  2. Natural language: "create team", "debate", "parallel agents", "run VS Arena", "compare methods", "competing hypotheses"
  3. Auto: research-coordinator delegates when parallel or debate execution is needed

Prerequisite Handling

Orchestrator does NOT enforce research checkpoints. It trusts the caller (coordinator or user) to have resolved prerequisites.

  • When called by coordinator: all prerequisites already resolved. Orchestrator receives agent IDs + context + execution mode.
  • When called directly by user: if .research/ context exists, read it; otherwise proceed without checkpoint gating.
  • Orchestrator's own checkpoint: only token cost confirmation before team creation.

Decision Logic

Request received
  |
  v
Check config.agent_teams.enabled
  |
  +-- false --> Always use subagents (Task with run_in_background)
  |
  +-- true --> Check CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS env var
       |
       +-- not set --> Subagent fallback + warn user
       |
       +-- set --> Evaluate scenario
            |
            +-- Inter-agent debate needed? --> Agent Teams
            |     (VS Arena, cross-method comparison, competing hypotheses)
            |
            +-- Parallel independent work? --> Agent Teams
            |     (multi-DB fetch, parallel review, concurrent analysis)
            |
            +-- Sequential pipeline? --> Subagents
            |     (G5->G6->F5 humanize, single agent tasks)
            |
            +-- Simple single agent? --> Direct Task dispatch

Agent Teams Mode

When config.agent_teams.enabled = true AND CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1 is set.

Team Lifecycle

  1. Evaluate scenario -- determine if Teams adds value over subagents
  2. Create team -- natural language prompt to Claude Code (conversational team creation, NOT raw TeamCreate API call)
  3. Spawn teammates -- each teammate receives an agent-specific prompt with research context
  4. Monitor progress -- track teammate status
  5. Collect results -- gather outputs from all teammates
  6. Present at checkpoint -- deliver results for user review
  7. Cleanup -- delete team after user selection or task completion

VS Arena Protocol (7 Stages)

Stage 1: Context Collection

Gather research context from diverga_project_status or user input:

  • research_question (from CP_RESEARCH_DIRECTION or user)
  • paradigm (from CP_PARADIGM_SELECTION or user)
  • research_field (e.g., Education, Psychology, HRD)
  • target_journal (if available)
  • key_variables (if available)

Stage 2: Persona Selection

Select 3 of 5 personas (V1-V5) based on paradigm:

RuleAlways include 1 persona differing from CP_PARADIGM_SELECTION
ParadigmSelection
QuantitativeV1 + V4 + one of V2/V3/V5
QualitativeV4 + V1 + one of V2/V3/V5
MixedV3 + two of V1/V2/V4/V5
No paradigm setV1 + V3 + V4 (maximum paradigmatic spread)

All VS Arena personas run on opus.

Stage 3: Team Creation

Create the team and spawn 3 persona teammates:

TeamCreate("vs-arena-{topic}")
Spawn teammate for each selected persona with prompt:
  - Research context (question, field, paradigm, variables, journal)
  - Persona definition reference (agents/v{N}.md)
  - Persona constraints reference (config/personas.json cannotRecommend)
  - Instruction: provide exactly ONE methodology recommendation

Stage 4: Independent Research

Each persona investigates the research question independently. No inter-agent communication during this stage.

Stage 5: Cross-Critique

Teammates message each other directly via team mailbox (SendMessage):

  • Each persona receives the other two recommendations
  • Each writes a 2-3 sentence critique from their epistemological perspective
  • Personas may refine their recommendations based on feedback

Stage 6: Synthesis + Checkpoint

Lead collects all positions and presents at CP_METHODOLOGY_APPROVAL:

## VS Arena: Methodology Recommendations

### Option A: [Persona] Recommendation
Methodology: [name]
T-Score: [score]
Rationale: [brief]
Cross-critique from other personas: [summaries]

### Option B: ...
### Option C: ...

Which methodology would you like to proceed with? (A/B/C)

WAIT for user selection. Record decision.

Stage 7: Cleanup

TeamDelete after user makes selection.

Scenario Templates

ScenarioTeam NameTeammatesWhen
VS Arenavs-arena-{topic}3 of V1-V5CP_METHODOLOGY_APPROVAL or explicit request
Systematic Reviewsr-pipeline-{topic}I1 x3 (per DB)I0 multi-DB fetch
Cross-Methodmethod-compare-{topic}C1+C2+C3Competing design recommendations
Parallel Reviewreview-{topic}2-5 agentsQuality review from multiple angles
Custom{user-specified}User-specifiedExplicit orchestrator call

Custom Teams

Users can request custom teams via natural language:

User: "/diverga:orchestrator create a team with C1 and C2 to compare
       quantitative designs for my RCT study"

Orchestrator parses:
  - agents: [C1, C2]
  - purpose: competing design recommendations
  - team_name: "design-compare-rct"
  - teammate_count: 2
User: "Have 3 agents review my methodology section from different angles"

Orchestrator infers:
  - agents: [X1 (ethics), B2 (quality), G2 (publication readiness)]
  - purpose: parallel review
  - team_name: "review-methodology"
  - teammate_count: 3

Maximum teammates: 5 (per Claude Code best practices). If user requests more, suggest splitting into phases.


Subagent Mode (Fallback)

When teams are disabled or unavailable, all workflows fall back to subagent dispatch.

Same VS Arena stages, but:

  • Stage 3: Task(subagent_type="diverga:v{N}", model="opus", run_in_background=true) instead of team creation
  • Stage 5: SKIP cross-critique -- subagents cannot message each other. Orchestrator synthesizes comparison instead: reads all 3 outputs, identifies overlaps and contradictions, presents unified comparison.
  • Stage 6: Lead presents orchestrator-synthesized comparison at checkpoint

Cross-critique is permanently unavailable in subagent mode. This is a known limitation, not a bug. The orchestrator compensates with its own synthesis.


Model Routing

All agents route to their designated model tier. Source of truth: config/agents.json.

CategoryAgentDisplay NameTierModel
A: FoundationA1Research Question RefinerHIGHopus
A2Theory & Critique ArchitectHIGHopus
A5Paradigm AdvisorHIGHopus
B: EvidenceB1Literature ScoutMEDIUMsonnet
B2Quality AppraiserMEDIUMsonnet
C: DesignC1Quantitative Design & SamplingHIGHopus
C2Qualitative Design (Ethnography/AR)HIGHopus
C3Mixed Methods DesignHIGHopus
C5Meta-Analysis MasterHIGHopus
D: CollectionD2Data Collection SpecialistMEDIUMsonnet
D4Instrument DeveloperHIGHopus
E: AnalysisE1Quantitative Analysis & Code GenHIGHopus
E2Qualitative CodingHIGHopus
E3Mixed Methods IntegrationHIGHopus
F: QualityF5Humanization VerifierMEDIUMsonnet
G: PublicationG1Journal MatcherMEDIUMsonnet
G2Publication SpecialistMEDIUMsonnet
G5Academic Style AuditorMEDIUMsonnet
G6Academic Style HumanizerHIGHopus
I: Systematic ReviewI0SR Pipeline OrchestratorHIGHopus
I1Paper RetrievalMEDIUMsonnet
I2Screening AssistantMEDIUMsonnet
I3RAG BuilderLOWhaiku
X: Cross-CuttingX1Research GuardianHIGHopus

V1-V5 personas are not listed here -- they are always invoked through VS Arena at opus tier.

Always pass model parameter explicitly when dispatching:

# HIGH tier
Task(subagent_type="general-purpose", model="opus", ...)
# MEDIUM tier
Task(subagent_type="general-purpose", model="sonnet", ...)
# LOW tier
Task(subagent_type="general-purpose", model="haiku", ...)

Token Cost Awareness

Before creating a team, prompt the user:

"Agent Teams will spawn N independent sessions. Proceed?
 [Y] Yes  /  [S] Subagents instead  /  [N] Cancel"

Skip this prompt if the user explicitly requested teams.


Removed Modes

Autonomous modes (Sisyphus, OMC, ralph, ultrawork, ecomode) removed in v6.0. See CHANGELOG.md.

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

a1

FreeSummary

Turn a broad research idea into clear, testable questions. Compare familiar and less conventional approaches, structure the selected question around its population and methods, and assess variables, feasibility, and potential theoretical contributions.

  • Turn a research topic into testable questions
  • Narrow an overly broad research question
  • Compare alternative research angles
brycewang-stanford/Auto-Empirical-Research-Skills4.7KUpdated Oct 20, 2026

a2

FreeSummary

Helps build a research framework by comparing theories, mapping relationships between variables, and deriving testable hypotheses. Explores less conventional options, examines weaknesses and competing explanations, and produces diagrams with anticipated reviewer objections.

  • Comparing theories for a research question
  • Deriving hypotheses from relationships between variables
  • Examining weaknesses and anticipated reviewer objections
brycewang-stanford/Auto-Empirical-Research-Skills4.7KUpdated Oct 20, 2026

a5

FreeSummary

Clarify the philosophical assumptions behind a research project and check whether they fit the proposed methods. Get a reasoned research stance, draft proposal wording, and guidance for explaining how the researcher's background and values shape the study.

  • Clarifying philosophical foundations for a research proposal
  • Explaining why the chosen research methods fit the study
  • Drafting a statement about the researcher's position
brycewang-stanford/Auto-Empirical-Research-Skills4.7KUpdated Oct 20, 2026

academic-paper

FreeSummary

Plan and draft academic papers through specialized agent roles covering literature, arguments, citations, and review. Get chapter guidance, revision roadmaps, Traditional Chinese and English abstracts, and documents formatted as LaTeX, DOCX, PDF, or Markdown.

  • Planning a paper chapter by chapter
  • Drafting a paper from research materials
  • Turning reviewer comments into a revision roadmap
brycewang-stanford/Auto-Empirical-Research-Skills4.7KUpdated Oct 20, 2026

Turn a detailed outline into a philosophy or interdisciplinary manuscript. Draft chapters with repeated quality reviews, check terminology and arguments across sections, and prepare the manuscript, evaluation reports, abstract, and metadata for submission.

  • Draft a full paper from a detailed outline
  • Review argument and citation quality chapter by chapter
  • Check terminology and argument flow across chapters
brycewang-stanford/Auto-Empirical-Research-Skills4.7KUpdated Oct 20, 2026

Simulate academic peer review through five reviewer roles tailored to the paper's field. Receive separate critiques, an editorial decision, and a prioritized revision roadmap, or check whether a revised manuscript addresses earlier comments.

  • Review a manuscript before journal submission
  • Examine research methods and statistical validity
  • Challenge core arguments and alternative explanations
brycewang-stanford/Auto-Empirical-Research-Skills4.7KUpdated Oct 20, 2026

See all skills by brycewang-stanford

Report a problem with this skill