Claude-skill-registry council-advice
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.
git clone https://github.com/majiayu000/claude-skill-registry
T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/council-advice" ~/.claude/skills/majiayu000-claude-skill-registry-council-advice && rm -rf "$T"
skills/data/council-advice/SKILL.mdCouncil 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: $TECH_STACK Analyze with focus on: 1. Root-cause understanding of the problem 2. Architecture-level recommendations 3. Code quality implications 4. Security and performance considerations 5. Testing and maintainability impact Provide depth and rigor in your analysis." \ --reasoning-effort high \ --output-format json
GPT-5.2 Advantages:
- 400K token context window
- 128K token max output (comprehensive responses)
- Extended reasoning with
levelxhigh - Aug 2025 knowledge cutoff
Context Loader
Simultaneously read relevant project files to understand:
- Current implementation state
- Project structure
- Existing patterns and conventions
Phase 2: Opus Judge Deliberation
After receiving council responses, execute the Opus Judge script:
python .claude/skills/council-advice/scripts/opus_judge.py \ --gemini-response "$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 | | |
Troubleshooting
Gemini Scripts Not Found
Ensure the
querying-gemini skill is installed:
ls -la .claude/skills/querying-gemini/scripts/
Expected files:
gemini_query.pygemini_analyze.pygemini_code.pygemini_fix.py
GPT-5.2 Scripts Not Found
Ensure the
querying-gpt52 skill is installed:
ls -la .claude/skills/querying-gpt52/scripts/
Expected files:
gpt52_query.pygpt52_analyze.pygpt52_fix.py
Opus Judge Script Fails
Check:
is setANTHROPIC_API_KEY- Required packages installed (
)anthropic>=0.50.0 - Script has execute permissions
API Key Issues
Ensure the following environment variables are set:
# For Gemini 3 Flash export GEMINI_API_KEY=your-gemini-api-key # or export GOOGLE_API_KEY=your-google-api-key # For GPT-5.2 export OPENAI_API_KEY=your-openai-api-key # For Opus Judge export ANTHROPIC_API_KEY=your-anthropic-api-key
Slow Response
- Council consultation runs in parallel (~30-60s for GPT-5.2, ~10-30s for Gemini 3 Flash)
- Opus Judge adds ~30-60s
- Total expected time: 1-2 minutes for comprehensive advice
Related Documentation
- RUBRICS.md - Detailed evaluation rubrics for the Opus Judge
- Querying Gemini Skill - Gemini 3 Flash script documentation
- Querying GPT-5.2 Skill - GPT-5.2 script documentation