Consilium: Multi-Agent Orchestration

Query external AI agents for independent, unbiased expert opinions. Each agent has a distinct thinking role and responds in a structured format for easy comparison.

Design Principles

Intellectual independence: Agents are instructed to think from first principles, challenge the framing of questions, and propose alternatives not mentioned in the query. They are free thinkers within the given context, not yes-men.

Role differentiation:

Codex = Rigorous Analyst — precision, code correctness, edge cases, implementation depth, security
Gemini = Lateral Thinker — cross-domain patterns, creative alternatives, questioning premises, big picture

Structured output: All agents respond using a common template (Assessment, Key Findings, Blind Spots, Alternatives, Recommendation with confidence level), making synthesis straightforward.

Anti-Bias Protocol

When formulating queries for consilium, follow these rules to maximize the value of independent opinions:

State the problem, not your solution. Instead of "Should we use X?", describe the constraints and goals.
Don't lead. Avoid "I think X is best, what do you think?" — this anchors the response.
Include raw context. Pipe code files or paste error logs directly rather than summarizing them (summaries carry your interpretation).
Omit your hypothesis when possible. Let agents form their own before revealing yours.

Read-Only Mode

External agents can read files and analyze code but CANNOT create, edit, or delete anything. Only the primary agent implements changes based on their recommendations.

Scripts

All scripts in scripts/ directory. The skill auto-detects its install location.

Single Agent Queries

# Codex — Rigorous Analyst (deep, precise, implementation-focused)
scripts/codex-query.sh "question" [context_file]
cat file.py | scripts/codex-query.sh "review this"

# Gemini — Lateral Thinker (broad, creative, premise-challenging)
scripts/gemini-query.sh "question" [context_file]
cat file.py | scripts/gemini-query.sh "review this"

Consensus Query (Both Agents in Parallel)

scripts/consensus-query.sh "architecture question"
cat file.py | scripts/consensus-query.sh "review this code"

When to Use Which

Situation	Script	Why
Code review, security audit	`codex-query.sh`	Codex excels at code-level precision and edge cases
Architecture decision, design choice	`consensus-query.sh`	Need both depth (Codex) and breadth (Gemini)
"Are we solving the right problem?"	`gemini-query.sh`	Gemini challenges premises and sees the bigger picture
Bug investigation, root cause analysis	`codex-query.sh`	Codex goes deep into implementation details
Exploring alternatives, brainstorming	`gemini-query.sh`	Gemini draws cross-domain analogies
High-stakes decision (irreversible)	`consensus-query.sh`	Two independent perspectives reduce blind spots

Synthesizing Responses

Agents respond with a shared structure. Compare section by section:

Assessment vs Assessment: Do they frame the problem differently? A framing difference often reveals the most insight.
Blind Spots: Union of both agents' blind spots is your risk map.
Alternatives: Check if either agent proposed something neither you nor the other agent considered.
Recommendations: Agreement = high confidence. Divergence = investigate the reasoning, not just the conclusion.

Response Patterns

When comparing the two responses, classify the pattern and act accordingly:

Agreement: Both recommend same approach — high confidence, proceed
Complementary: Different valid points that don't conflict — combine insights into a richer picture
Contradiction: Conflicting recommendations — present both with reasoning, let user decide
Unique insight: One agent caught something the other missed — highlight it, this is often the most valuable output

Prompt Patterns

Architecture Decision (unbiased framing)

scripts/consensus-query.sh "We need real-time updates for ~100 concurrent users.
Updates are server-initiated only. Current stack: [describe your stack].
Latency target: under 500ms from event to UI update.
What approach would you recommend and why?"

Code Review (pipe raw code, let agents form opinions)

cat src/services/auth.py | scripts/codex-query.sh \
  "Review this authentication service. Focus on whatever concerns you most."

Problem Investigation (provide facts, not hypotheses)

scripts/codex-query.sh "Database query returns empty result.
Direct query with same filter returns 5 documents.
[paste query here]
What's happening?"

Environment Variables

CODEX_MODEL: Override Codex model (default: gpt-5.4)
GEMINI_MODEL: Override Gemini model (default: gemini-3.1-pro-preview)
AGENT_TIMEOUT: Timeout seconds (default: 1200)

Prerequisites

Codex CLI installed and authenticated (codex --version)
Gemini CLI installed and authenticated (gemini --version)

agents-consilium