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Multi-LLM collaborative brainstorming and planning.
Passed 13 of 17 test cases with the skill, and 7 of 17 without it.
and 6 more in the full report
9 fixed8 passed either way4 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 9 test cases the skill fixed. Same model, same prompt, with and without the skill.
We are setting up our council environment variables for OpenAI and want the baseline default entry. We need a budget-friendly option that costs around five cents per million input tokens rather than gpt-4o or gpt-5.2. Which model identifier belongs in the configuration?
| Checked | Without skill | With skill |
|---|---|---|
| Identifies gpt-5-nano as the default OpenAI model | Missed | Passed |
The instructions your model reads when the skill loads.
Consult multiple AI models (ChatGPT and Gemini) for their perspectives before presenting implementation plans to users.
When user requests consultation with other AI models, use phrases like:
Process:
scripts/query_llms.py with the user's prompt to get perspectives from both ChatGPT and GeminiThe skill requires API keys and optional model configuration stored in a .env file in the working directory:
OPENAI_API_KEY=sk-...
GEMINI_API_KEY=...
# Optional: Specify which models to use (defaults shown below)
OPENAI_MODEL=gpt-5-nano
GEMINI_MODEL=gemini-3-flash-preview
Default Models:
gpt-5-nano (fastest, most cost-efficient - $0.05/1M input, $0.40/1M output)gemini-3-flash-preview (balanced speed and intelligence)Upgrade Options for Better Collaboration:
OpenAI models (ordered by capability and cost):
gpt-5-nano - Fastest, most cost-efficient ($0.05/1M in, $0.40/1M out) - DEFAULTtest cases passed, vs 7 / 17 without
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