Multi model oracle

Multi-Model Oracle

Query a configured frontier-model pool in parallel, with automatic prompt engineering and intelligent response merging. The pipeline: optimize the prompt, query available models, merge into one best-effort answer.

Models

Model	Provider	Strengths
Reasoning model	OpenRouter or configured provider	Nuanced reasoning, creative depth, multi-perspective analysis
Coding model	OpenRouter or configured provider	Technical precision, breadth, systematic coverage, code
Fast synthesis model	OpenAI-compatible provider	Efficient prompt engineering, normalization, and merge synthesis

Quick Start

python3 /home/ubuntu/skills/multi-model-oracle/scripts/oracle.py "Your question here"

Workflow

Step 1: Determine the Query

Accept the query from the user. It can be any type: code, creative writing, analysis, research, reasoning, practical advice, or general questions.

For long or complex prompts, save to a file first:

python3 /home/ubuntu/skills/multi-model-oracle/scripts/oracle.py --file /path/to/prompt.txt

Step 2: Run the Oracle

Standard mode (prompt engineering + parallel query + merge):

python3 /home/ubuntu/skills/multi-model-oracle/scripts/oracle.py "query"

With individual model responses visible:

python3 /home/ubuntu/skills/multi-model-oracle/scripts/oracle.py "query" --show-individual

With prompt engineering details visible:

python3 /home/ubuntu/skills/multi-model-oracle/scripts/oracle.py "query" --show-prompts

Raw mode (skip prompt engineering, send query as-is):

python3 /home/ubuntu/skills/multi-model-oracle/scripts/oracle.py "query" --raw

Save output to file:

python3 /home/ubuntu/skills/multi-model-oracle/scripts/oracle.py "query" -o result.md

Read query from stdin (pipe input):

echo "Your question" | python3 /home/ubuntu/skills/multi-model-oracle/scripts/oracle.py --stdin

Step 3: Deliver the Result

The merged answer is printed to stdout and optionally saved to a file. Present the merged answer to the user. If --show-individual was used, also share the individual responses for comparison.

Pipeline Stages

Stage 1 - Prompt Engineering: Detects query intent (code, creative, analysis, research, reasoning, practical, general), then uses the configured fast synthesis model to create model-specific prompt variants optimized for each model role.

Stage 1.5 - Parallel Prompt Engineering: The model-specific prompt variants are generated in parallel via ThreadPoolExecutor for faster startup.

Stage 2 - Parallel Query: Sends prompts simultaneously via ThreadPoolExecutor. Each model has retry logic (2 retries with exponential backoff) and 180s timeout. If a model fails, the pipeline continues with remaining models.

Stage 3 - Intelligent Merge: Feeds all successful responses to the configured fast synthesis model with a merge prompt that extracts the strongest elements, resolves contradictions, eliminates redundancy, and produces a unified answer that reads as if from a single expert. Has its own retry logic (2 retries) with concatenation fallback if merge fails.

Stage 4 - Output: Formats the final result with metadata (models used, timing, token counts).

Requirements

• OPENROUTER_API_KEY environment variable for OpenRouter-backed model roles
• OPENAI_API_KEY environment variable for OpenAI-compatible synthesis models
• Python packages: requests, openai

Prompt Engineering Reference

For details on the intent detection patterns, model-specific tailoring, and merge strategy, read references/prompt_engineering.md.