name: multi-llm-consult
version: 1.0.0
min_mindforge_version: 10.0.3
status: stable
triggers: second opinion, cross-model, multi-model, consult, gemini, codex, GPT, alternative model, consensus, multi-LLM, external model, validation
Skill — Multi-LLM Consult
When this skill activates
When seeking a second opinion from external models, validating a decision across multiple AI providers, or when the user explicitly requests cross-model consultation.
Mandatory actions when this skill is active
Before consulting external models
- Sanitize the prompt. NEVER send raw project context to external models.
- Remove: file paths, internal variable names, proprietary business logic
- Remove: API keys, secrets, credentials, internal URLs
- Remove: user PII, customer data, anything covered by data-privacy skill
- Keep: the abstract question, general patterns, public knowledge references
- Estimate cost. Each external call costs tokens. Check budget via cost-tracking module.
- Define the question clearly. Vague questions produce vague answers. Frame as:
- "Given [sanitized context], which approach is better: A or B? Why?"
| Provider | Model | Best For | Cost Tier |
|---|
| Anthropic | claude-opus-4-7 | Deep reasoning, architecture | complex |
| Google | gemini-2.5-pro | Research, long context, web grounding | research |
| OpenAI | gpt-4o | Alternative perspective, validation | consult |
Consultation Protocol
Single Consult (one external model):
- Sanitize prompt
- Send to selected model
- Present response with source attribution
- Note areas of agreement/disagreement with primary analysis
Consensus Consult (all 3 models):
- Sanitize prompt (same prompt to all)
- Send to all configured models in parallel
- Analyze responses for:
- Agreement (2+ models recommend same approach): high confidence signal
- Divergence (models disagree): flag for user decision, present all perspectives
- Novel insight (one model raises a point others missed): highlight specifically
- Produce synthesis:
Consensus: [Yes/No/Partial]
Recommended: [approach]
Agreement: [which models agree]
Dissent: [which models disagree and why]
Novel: [unique insights from individual models]
During consultation
- Log every external call in token-ledger.jsonl (model, tokens, cost)
- Never send more than 2000 tokens to external models per consultation
- If a model is unavailable: skip it, note in output, continue with available models
- Respect rate limits — max 3 consultations per session
After consultation
- Present results to user with clear attribution
- Never auto-execute based on external model recommendations
- External opinions are ADVISORY — user sovereignty applies
- Log consultation summary in AUDIT
Self-check before task completion
- [ ] Did I sanitize the prompt before sending to external models?
- [ ] Did I log every external call in token-ledger.jsonl?
- [ ] Did I attribute responses to their source model (no unattributed blending)?
- [ ] Did I remind the user that external opinions are advisory?