Multi llm consult

Skill sairam0424/MindForge/.mindforge/skills/multi-llm-consult

MindForge: The Enterprise Agentic Framework for Claude Code & Antigravity. High-performance autonomous execution, wave-parallelism, and multi-tier governance for production-grade AI engineering.From the repository description

Install

npx -y skills add sairam0424/MindForge --skill multi-llm-consult

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

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SKILL.md

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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?"

Configured Models

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?

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.