deliberation

name	deliberation
description	Multi-agent architectural deliberation protocol for heterogeneous model debate on complex design problems

Multi-Agent Deliberation Protocol

This skill defines the protocol for heterogeneous model deliberation — using multiple LLMs (Claude, Gemini, GPT) to debate complex architectural and design problems via GitHub Discussions.

When to Use Deliberation

Deliberation is most valuable for:

• Architecturally complex problems with multiple viable approaches
• Trade-off decisions where different priorities lead to different conclusions
• Ambiguous requirements that benefit from multiple interpretations
• Novel patterns where training data divergence creates useful diversity

Deliberation is not valuable for:

• Clear-cut factual questions (use single-model lookup)
• Simple implementation decisions (use primary agent)
• Time-sensitive decisions (deliberation takes multiple rounds)

The Deliberation Medium: GitHub Discussions

GitHub Discussions serve as the persistent, auditable deliberation space. Benefits:

• Permanent record — "what were we thinking when we designed this?"
• Team visibility — human collaborators can observe or join
• Natural context — each agent re-reads the full history when engaging
• Semantic framing — "discussion" primes collaborative analysis, not solution-building

Use the Ideas category for architectural deliberations.

Agent Roles

Deliberator Agents (3)

• Claude Opus 4.6 — max adaptive thinking, strong at nuanced reasoning
• Gemini 3 Pro — deep think mode, highest agentic reliability
• GPT-5.2 — xhigh reasoning effort, strong general reasoning

Each deliberator:

• Has read-only codebase access (no write/edit tools)
• Can post discussion comments via GitHub API
• Can research externally (web search, doc lookup)
• Operates independently — no shared context between rounds

Orchestrator (Primary Agent)

• Posts the initial problem statement with relevant prior decisions
• Triggers deliberation rounds (spawning all 3 deliberators in parallel)
• Synthesizes final decision and stores it in mem0

Rules for Deliberating Agents

Identity

Always identify yourself in every comment using the format:

**[Model Name agent]** — Your comment begins here...

Examples: **[Claude Opus 4.6 agent]**, **[Gemini 3 Pro agent]**, **[GPT-5.2 agent]**

Investigation Protocol

1. Read the full discussion including all prior comments before responding
2. Investigate the codebase independently — read relevant source files, trace data flows, check existing implementations
3. Research externally when the problem involves libraries, APIs, or patterns you're uncertain about
4. Form your position based on evidence, not prior comments

Critical Engagement

• Challenge assumptions — if a prior comment makes claims without evidence, ask for it
• Disagree constructively — state what you disagree with and why, then propose an alternative
• Identify blind spots — look for what others missed (edge cases, performance implications, maintenance burden)
• Recognize good ideas — if a proposal is sound, say so (consensus is valuable when earned)

Evidence Requirements

• Cite specific files and line numbers when referencing code (use file:line format)
• Link to documentation when referencing library/API behavior
• Quote relevant code when discussing implementation details
• Quantify trade-offs when possible (performance, complexity, maintainability)

Boundaries

• DO NOT implement anything — no code changes, no PRs, no file creation
• DO NOT take the lead — you are one voice among equals
• DO NOT summarize — that's the orchestrator's job
• DO NOT repeat yourself — if you've made your point in a prior round, add new insight or signal agreement

Comment Structure Template

Structure your comments for clarity:

**[Model Name agent]**

### Investigation
[What I discovered in the codebase/research]

### Position
[My view on the problem and proposed solutions]

### Concerns
[Specific issues I see with current proposals, with evidence]

### Suggestions
[Concrete alternatives or refinements, with rationale]

This structure is a guideline, not a requirement. Adapt to the discussion flow.

Round Dynamics

Round 1: All agents investigate independently and post initial positions Round 2+: Agents respond to each other's proposals, refining or challenging

Convergence signals:

• Agents agree on key trade-offs even if they differ on final choice
• New rounds produce no new insights (agents repeat prior points)
• A clear decision emerges with documented rationale

Divergence is okay — if agents fundamentally disagree, that signals the decision needs human judgment or more information.

Memory Integration

Before deliberation starts:

• Orchestrator searches mem0 for related architectural decisions
• Relevant prior decisions are included in the discussion body
• Agents see this context and can build on or challenge prior decisions

After deliberation concludes:

• Orchestrator stores the decision in mem0 with discussion link
• Future deliberations can discover and reference this decision

Anti-Patterns

Avoid these common failure modes:

❌ Rubber-stamping — agreeing without independent investigation
❌ Scope creep — solving adjacent problems not in the discussion scope
❌ Premature implementation — proposing code before architecture is clear
❌ Credential arguments — "I'm better at X" (evidence speaks for itself)
❌ Repetition — restating your position without new evidence
❌ Bikeshedding — debating trivial details while avoiding hard trade-offs

Related Patterns

• parallel-agents (Simon Willison) — multiple agents on separate tasks (not deliberation)
• model-routing — task-appropriate model selection (not multi-model debate)
• Multi-Agent Debate (MAD) research — academic foundation for this pattern
• MALLM Framework — research framework for configurable multi-agent LLM debate

References

• Academic research validates heterogeneous (different model) debate for complex/ambiguous problems
• GitHub Discussions provide natural semantic framing vs Issues (which bias toward implementation)
• Fresh context each round (no persistence) reduces confirmation bias and enables new insights