name	council-advice
description	Multi-model AI council for actionable project advice. Leverages Gemini 3 Flash and GPT-5.2 skills in parallel, then synthesizes through an Opus Judge for stage-appropriate, non-overkill recommendations. Use when seeking architectural guidance, code review synthesis, or implementation planning.

Council Advice

A multi-model advisory council that provides actionable, stage-appropriate recommendations by combining perspectives from multiple AI models and filtering through rigorous evaluation rubrics.

Architecture Overview

                    ┌─────────────────────────────────────────────────────────────┐
                    │                    COUNCIL ADVICE FLOW                       │
                    └─────────────────────────────────────────────────────────────┘

                                         User Request
                                              │
                                              ▼
                    ┌─────────────────────────────────────────────────────────────┐
                    │                 PHASE 1: COUNCIL CONSULTATION                │
                    │                      (Parallel Execution)                    │
                    └─────────────────────────────────────────────────────────────┘
                                              │
                         ┌────────────────────┼────────────────────┐
                         │                    │                    │
                         ▼                    ▼                    ▼
                ┌─────────────────┐  ┌─────────────────┐  ┌─────────────────┐
                │ GEMINI ADVISOR  │  │ GPT-5.2 ADVISOR │  │ CONTEXT LOADER  │
                │ (Gemini 3 Flash)│  │                 │  │                 │
                │ gemini_analyze  │  │ gpt52_analyze   │  │ Read project    │
                │ gemini_query    │  │ gpt52_query     │  │ files & stage   │
                └────────┬────────┘  └────────┬────────┘  └────────┬────────┘
                         │                    │                    │
                         └────────────────────┼────────────────────┘
                                              │
                                              ▼
                    ┌─────────────────────────────────────────────────────────────┐
                    │                 PHASE 2: OPUS JUDGE DELIBERATION             │
                    │                   (Claude Opus 4.5 via API)                  │
                    └─────────────────────────────────────────────────────────────┘
                                              │
                                              ▼
                         ┌─────────────────────────────────────────┐
                         │            EVALUATION RUBRICS           │
                         │                                         │
                         │  • Stage Relevancy (MVP/PoC/Prod)       │
                         │  • Overkill Detection                   │
                         │  • Over-complexity Assessment           │
                         │  • Over-engineering Detection           │
                         │  • Implementation Feasibility           │
                         │  • Project Purpose Alignment            │
                         └─────────────────────────────────────────┘
                                              │
                                              ▼
                    ┌─────────────────────────────────────────────────────────────┐
                    │                 PHASE 3: ACTIONABLE REPORT                   │
                    │                   (Implementation Plan)                      │
                    └─────────────────────────────────────────────────────────────┘

How to Use

When the user asks for council advice on any topic:

Clarify the request context if not provided:
- Project stage: MVP, PoC, Production, Maintenance
- Purpose: What problem is being solved?
- Constraints: Time, resources, technical limitations
Execute Phase 1: Call council members in parallel
Execute Phase 2: Run the Opus Judge script
Present Phase 3: Deliver the actionable report

Phase 1: Council Consultation

Execute these advisor consultations in parallel for maximum efficiency:

Gemini 3 Flash Advisor

Use the querying-gemini skill scripts for intelligent analysis:

For code/architecture analysis:

python .claude/skills/querying-gemini/scripts/gemini_analyze.py \
  --target "$TARGET_PATH" \
  --focus-areas "architecture,quality,security,performance" \
  --analysis-type comprehensive \
  --output-format json

For general guidance queries:

python .claude/skills/querying-gemini/scripts/gemini_query.py \
  --prompt "You are the Gemini Advisor on a multi-model council. Analyze the following request and provide your expert recommendations.

REQUEST: $USER_REQUEST

CONTEXT:
- Project Stage: $STAGE
- Project Purpose: $PURPOSE
- Technical Stack: $TECH_STACK

Provide:
1. Your assessment of the situation
2. Specific recommendations (prioritized)
3. Potential risks or concerns
4. Alternative approaches if applicable

Be thorough but practical. Focus on actionable insights." \
  --thinking-level high \
  --output-format json

Gemini 3 Flash Advantages:

1M token context window (analyze very large codebases)
Pro-level intelligence at Flash speed
Configurable thinking levels (minimal/low/medium/high)
Cost-effective: $0.50/1M input, $3/1M output

GPT-5.2 Advisor

Use the querying-gpt52 skill scripts for high-reasoning analysis:

For code analysis:

python .claude/skills/querying-gpt52/scripts/gpt52_analyze.py \
  --target "$TARGET_PATH" \
  --focus-areas "architecture,quality,security,performance" \
  --analysis-type comprehensive \
  --output-format json

For high-reasoning queries:

python .claude/skills/querying-gpt52/scripts/gpt52_query.py \
  --prompt "You are the GPT-5.2 Advisor on a multi-model council. Provide high-reasoning analysis for the following request.

REQUEST: $USER_REQUEST

CONTEXT:
- Project Stage: $STAGE
- Project Purpose: $PURPOSE
- Technical Stack: $TECH_STACK

Analyze with focus on:
1. Root-cause understanding of the problem
2. Architecture-level recommendations
3. Code quality implications
4. Security and performance considerations
5. Testing and maintainability impact

Provide depth and rigor in your analysis." \
  --reasoning-effort high \
  --output-format json

GPT-5.2 Advantages:

400K token context window
128K token max output (comprehensive responses)
Extended reasoning with xhigh level
Aug 2025 knowledge cutoff

Context Loader

Simultaneously read relevant project files to understand:

Current implementation state
Project structure
Existing patterns and conventions

Phase 2: Opus Judge Deliberation

After receiving council responses, execute the Opus Judge script:

python .claude/skills/council-advice/scripts/opus_judge.py \
  --gemini-response "$GEMINI_RESPONSE" \
  --codex-response "$GPT52_RESPONSE" \
  --project-stage "$PROJECT_STAGE" \
  --project-purpose "$PROJECT_PURPOSE" \
  --request "$ORIGINAL_REQUEST"

Note: Both advisor scripts must use --output-format json so the Opus Judge can properly parse the responses.

The Opus Judge:

Receives multi-model reviews
Weights recommendations against project context
Applies evaluation rubrics (see RUBRICS.md)
Decides what to embrace and what to disregard
Produces a synthesized, actionable report

Opus Judge Evaluation Criteria

For each recommendation, the judge evaluates:

Criterion	Accept If	Reject If
Stage Relevancy	Matches current stage needs	Premature optimization for stage
Overkill Detection	Proportional to problem	Solution exceeds problem scope
Complexity	Appropriate for team/project	Unnecessarily complex
Engineering	Solves actual need	Builds for hypothetical futures
Feasibility	Achievable with current resources	Requires unrealistic effort
Purpose Alignment	Advances project goals	Tangential to core mission

Phase 3: Actionable Report

The final output follows this structure for seamless conversion to implementation:

# Council Advice Report

## Executive Summary
[2-3 sentences on the key recommendation]

## Project Context
- **Stage**: {stage}
- **Purpose**: {purpose}
- **Request**: {original_request}

## Recommendations

### Embraced (Implement These)

#### 1. [Recommendation Title]
- **Source**: Gemini/GPT-5.2/Both
- **Priority**: P0/P1/P2
- **Rationale**: Why this was embraced
- **Implementation Steps**:
  1. Step one
  2. Step two
  3. Step three
- **Estimated Effort**: [time estimate]

### Deferred (Not Now, Maybe Later)

#### 1. [Recommendation Title]
- **Source**: Gemini/GPT-5.2/Both
- **Reason for Deferral**: [Stage mismatch/Overkill/etc.]
- **Revisit When**: [Condition for reconsidering]

### Rejected (Do Not Implement)

#### 1. [Recommendation Title]
- **Source**: Gemini/GPT-5.2/Both
- **Rejection Reason**: [Over-engineered/Out of scope/etc.]

## Implementation Plan

### Immediate Actions (This Session)
- [ ] Action 1
- [ ] Action 2

### Short-term Actions (This Sprint)
- [ ] Action 1
- [ ] Action 2

### Future Considerations
- [ ] Consideration 1
- [ ] Consideration 2

## Council Notes
[Any areas of agreement/disagreement between advisors]

Best Practices

Parallel Execution

Always execute both advisor scripts in parallel for maximum efficiency. Use Bash tool with run_in_background: true for both scripts, then collect results.

Example parallel execution:

# Run both advisors in background
python .claude/skills/querying-gemini/scripts/gemini_query.py --prompt "..." --output-format json &
python .claude/skills/querying-gpt52/scripts/gpt52_query.py --prompt "..." --output-format json &
wait

Stage-Appropriate Advice

Stage	Prioritize	Avoid
MVP	Speed, validation, core features	Optimization, scalability, edge cases
PoC	Proving concept, minimal viable	Production concerns, polish
Production	Reliability, security, performance	Technical debt shortcuts
Maintenance	Stability, documentation	Major rewrites

When to Use This Skill

Seeking architectural guidance
Planning feature implementation
Code review synthesis
Evaluating technical approaches
Making technology decisions
Refactoring strategy

When NOT to Use This Skill

Simple, straightforward tasks
Emergency bug fixes (use direct tools)
Documentation-only requests
One-line code changes

Model Comparison

Feature	Gemini 3 Flash	GPT-5.2
Context Window	1,000,000 tokens	400,000 tokens
Max Output	64,000 tokens	128,000 tokens
Speed	Faster	Slower
Thinking Levels	minimal/low/medium/high	none/low/medium/high/xhigh
Best For	Large codebases, speed-critical	Deep reasoning, comprehensive output
API Key	`GEMINI_API_KEY`	`OPENAI_API_KEY`

Troubleshooting

Gemini Scripts Not Found

Ensure the querying-gemini skill is installed:

ls -la .claude/skills/querying-gemini/scripts/

Expected files:

gemini_query.py
gemini_analyze.py
gemini_code.py
gemini_fix.py

GPT-5.2 Scripts Not Found

Ensure the querying-gpt52 skill is installed:

ls -la .claude/skills/querying-gpt52/scripts/