Review Roulette

When this skill is active, your ONLY job is orchestration — you do NOT perform any review analysis yourself. You randomly select 3 reasoning models, dispatch the review to all of them in parallel, and merge the results.

Step 1: Discover Available Reasoning Models

Run ${PI_CMD:-pi} --list-models via bash to get the current list of models with valid API keys. Eligible models are those that show thinking: yes in the output — these are the reasoning-capable / premium models.

Excluded models: Never select openai/o3-pro — it is prohibitively expensive. If it appears in the model list, skip it entirely.

Example reasoning models you might see (provider/model format):

• anthropic/claude-opus-4-6
• anthropic/claude-sonnet-4-5 (with thinking)
• openai/o3
• openai/o4-mini
• openai/gpt-5-pro
• openai/gpt-5.2-pro
• google/gemini-2.5-pro (with thinking)
• google/gemini-2.5-flash (with thinking)
• google/gemini-3.1-pro-preview
• xai/grok-4

The exact list depends on which API keys are configured. Always check — do not assume models are available.

Step 2: Randomly Select 3 Models

From the eligible reasoning models, pick exactly 3 at random.

CRITICAL — true randomness and diversity:

• Do NOT always pick the same 3 models. The entire point of review roulette is variety of perspectives across runs.
• Prefer different providers when possible. If you have reasoning models from Anthropic, OpenAI, Google, and xAI, pick from 3 different providers. Only double up on a provider if fewer than 3 providers have eligible models.
• Shuffle or randomize your selection each time. Do not default to alphabetical order or any fixed preference.

Step 3: Dispatch the Review in Parallel

Use the task tool with the tasks array to dispatch all 3 reviews simultaneously. Each task object must include:

1. model: The selected model in provider/model format.
2. task: The FULL original review prompt/instructions. Each subtask starts fresh with NO conversation history and NO context from the parent. You must forward EVERYTHING you were asked to do — the complete prompt, all instructions, the diff, file contents, any constraints or formatting requirements, the expected JSON output schema, etc. Do not summarize or abbreviate. Pass it all through verbatim.

Example structure:

{
  "tasks": [
    {
      "task": "<the ENTIRE original review prompt and instructions>",
      "model": "anthropic/claude-opus-4-6"
    },
    {
      "task": "<the ENTIRE original review prompt and instructions>",
      "model": "openai/o3"
    },
    {
      "task": "<the ENTIRE original review prompt and instructions>",
      "model": "google/gemini-2.5-pro"
    }
  ]
}

Step 4: Merge Results

Each subtask will return a review result containing a summary string and a suggestions array (the standard review JSON format).

Collect the results from all 3 subtask responses and merge them:

• Suggestions: Concatenate all suggestions arrays into a single array.

• Summary: Concatenate all summaries with model attribution. Format the merged summary as:

  <provider/model1>:
  <summary1>
  
  <provider/model2>:
  <summary2>
  
  <provider/model3>:
  <summary3>
  

  This attributed format also serves as a record of which models were used in the review.

Return the merged result as the final JSON response.

Do NOT:

• Deduplicate suggestions — let the consumer decide what overlaps
• Synthesize, summarize, or editorialize on the combined results
• Perform any review analysis yourself

Do:

• Concatenate all suggestion arrays: [...model1, ...model2, ...model3]
• Concatenate all summaries with provider/model: attribution as shown above
• Return the merged result as the final JSON response in the same schema the subtasks used

Summary

You (parent)                    Subtask 1 (model A)
    │                               │
    ├── pick 3 random models        ├── receive full prompt
    ├── forward full prompt ──────► ├── perform review
    │                               └── return suggestions JSON
    │
    ├── forward full prompt ──────► Subtask 2 (model B) ──► suggestions JSON
    │
    ├── forward full prompt ──────► Subtask 3 (model C) ──► suggestions JSON
    │
    └── merge all summaries (with model attribution) + suggestions[] ──► final JSON response

The parent does zero analysis. It is purely a dispatcher and merger. Each model's summary is attributed so the final output records which models contributed.