onfire7777 
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_KEYenvironment variable for OpenRouter-backed model rolesOPENAI_API_KEYenvironment 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.