Multi Model Validation

Multi-Model Validation

Version: 3.3.0 Purpose: Patterns for running multiple AI models in parallel via Claudish proxy with context-aware preferences, dynamic model discovery, session-based workspaces, and performance statistics Status: Production Ready

Overview

Multi-model validation is the practice of running multiple AI models (Grok, Gemini, GPT-5, DeepSeek, etc.) in parallel to validate code, designs, or implementations from different perspectives. This achieves:

• 3-5x speedup via parallel execution (15 minutes → 5 minutes)
• Consensus-based prioritization (issues flagged by all models are CRITICAL)
• Diverse perspectives (different models catch different issues)
• Cost transparency (know before you spend)
• Free model discovery (NEW v3.0) - find high-quality free models from trusted providers
• Performance tracking - identify slow/failing models for future exclusion
• Data-driven recommendations - optimize model shortlist based on historical performance

Key Innovations:

1. Context-Aware Preferences (NEW v3.3.0) - Automatically use saved model preferences per task type (debug/research/coding/review) from .claude/multimodel-team.json
2. Dynamic Model Discovery (v3.0) - Use claudish --top-models and claudish --free to get current available models with pricing
3. Session-Based Workspaces (v3.0) - Each validation session gets a unique directory to prevent conflicts
4. 4-Message Pattern - Ensures true parallel execution by using only Task tool calls in a single message
5. Pattern 7-8 - Statistics collection and data-driven model recommendations

This skill is extracted from the /review command and generalized for use in any multi-model workflow.

⚠️ MANDATORY: Learn and Reuse User Preferences

Model preferences are learned per context and reused automatically.

• First time a context is used → ASK user → SAVE to that context
• Next time same context → USE saved models automatically (no asking)
• User explicitly says "change models" or "different models" → ASK and UPDATE

# FIRST STEP - Read preferences file
cat .claude/multimodel-team.json 2>/dev/null

Flow:

1. Detect context from task keywords
   - "debug", "error", "bug", "fix" → debug
   - "research", "analyze", "investigate" → research
   - "implement", "build", "create", "code" → coding
   - "review", "audit", "check" → review

2. Check if contextPreferences[context] exists and is non-empty

   IF EXISTS (has models saved):
   → Use those models directly
   → DO NOT ask user
   → Proceed with validation

   IF EMPTY/MISSING (first time for this context):
   → Run: claudish --top-models
   → Ask user to select models (AskUserQuestion)
   → Save to contextPreferences[context]
   → Proceed with validation

3. User override triggers (explicit request to change):
   - "use different models"
   - "change models"
   - "update model preferences"
   → Ask user to select new models
   → Update contextPreferences[context]

Example - Learning Flow:

# First debug task ever:
Task: "Debug this authentication error"
→ Context: debug
→ contextPreferences.debug is empty
→ ASK: "Which models for debug tasks?"
→ User selects: grok, glm, minimax
→ SAVE to contextPreferences.debug
→ Run with those models

# Second debug task:
Task: "Debug the API timeout"
→ Context: debug
→ contextPreferences.debug = ["grok", "glm", "minimax"]
→ USE directly (no asking)
→ Run with saved models

# User wants to change:
Task: "Debug this error, use different models"
→ Detected: "different models" override trigger
→ ASK: "Which models for debug tasks?"
→ User selects: gemini, gpt-5-codex
→ UPDATE contextPreferences.debug
→ Run with new models

Related Skills

CRITICAL: Tracking Protocol Required

Before using any patterns in this skill, ensure you have completed the pre-launch setup from orchestration:model-tracking-protocol.

Launching models without tracking setup = INCOMPLETE validation.

Cross-References:

• orchestration:model-tracking-protocol - MANDATORY tracking templates and protocols (NEW in v0.6.0)
  • Pre-launch checklist (8 required items)
  • Tracking table templates
  • Failure documentation format
  • Results presentation template
• orchestration:quality-gates - Approval gates and severity classification
• orchestration:task-orchestration - Progress tracking during execution
• orchestration:error-recovery - Handling failures and retries

Skill Integration:

This skill (multi-model-validation) defines execution patterns (how to run models in parallel). The model-tracking-protocol skill defines tracking infrastructure (how to collect and present results).

Use both together:

skills: orchestration:multi-model-validation, orchestration:model-tracking-protocol

Core Patterns

Pattern 0: Session Setup and Model Discovery (NEW v3.0)

Purpose: Create isolated session workspace and discover available models dynamically.

Why Session-Based Workspaces:

Using a fixed directory like ai-docs/reviews/ causes problems:

• ❌ Multiple sessions overwrite each other's files
• ❌ Stale data from previous sessions pollutes results
• ❌ Hard to track which files belong to which session

Instead, create a unique session directory for each validation:

# Generate unique session ID
TARGET_SLUG=$(echo "${TASK_NAME:-review}" | tr '[:upper:] ' '[:lower:]-' | sed 's/[^a-z0-9-]//g' | head -c20)
SESSION_ID="review-${TARGET_SLUG}-$(date +%Y%m%d-%H%M%S)-$(head -c 4 /dev/urandom | xxd -p)"
SESSION_DIR="ai-docs/sessions/${SESSION_ID}"

# Create session workspace
mkdir -p "$SESSION_DIR"

echo "Session: $SESSION_ID"
echo "Directory: $SESSION_DIR"

# Example output:
# Session: review-auth-impl-20251212-143052-a3f2
# Directory: ai-docs/sessions/review-auth-impl-20251212-143052-a3f2

Benefits:

• ✅ Each session is isolated (no cross-contamination)
• ✅ Traceable - can associate files with a specific session
• ✅ Session ID can be used for tracking in statistics
• ✅ Parallel sessions don't conflict
• ✅ Aligned with dev:feature session pattern
• ✅ Committed to git for audit trail (unlike /tmp/)

⚠️ Do NOT use /tmp/ for session directories. Files in /tmp/ are not traceable, not committable, and parallel runs will overwrite each other.

Dynamic Model Discovery:

NEVER hardcode model lists. Models change frequently - new ones appear, old ones deprecate, pricing updates. Instead, use claudish to get current available models:

# Get top paid models (best value for money)
claudish --top-models

# Example output:
#   google/gemini-3-pro-preview    Google     $7.00/1M   1048K   🔧 🧠 👁️
#   openai/gpt-5.2-codex           Openai     $5.63/1M   400K    🔧 🧠 👁️
#   x-ai/grok-code-fast-1          X-ai       $0.85/1M   256K    🔧 🧠
#   minimax/minimax-m2.5           Minimax    $0.64/1M   262K    🔧 🧠
#   z-ai/glm-4.7                   Z-ai       $1.07/1M   202K    🔧 🧠
#   qwen/qwen3-vl-235b-a22b-ins... Qwen       $0.70/1M   262K    🔧    👁️

# Get free models from trusted providers
claudish --free

# Example output:
#   google/gemini-2.0-flash-exp:free  Google     FREE      1049K   ✓ · ✓
#   mistralai/devstral-2512:free      Mistralai  FREE      262K    ✓ · ·
#   qwen/qwen3-coder:free             Qwen       FREE      262K    ✓ · ·
#   qwen/qwen3-235b-a22b:free         Qwen       FREE      131K    ✓ ✓ ·
#   openai/gpt-oss-120b:free          Openai     FREE      131K    ✓ ✓ ·

Recommended Free Models for Code Review:

Model Selection Flow (Learn and Reuse):

1. Read Preferences File
   → cat .claude/multimodel-team.json
   → If file NOT exists → create empty one

2. Detect Task Context
   → Parse task for keywords (case-insensitive):
     - "debug", "error", "bug", "fix", "trace", "issue" → debug
     - "research", "investigate", "analyze", "explore", "find" → research
     - "implement", "build", "create", "code", "develop", "feature" → coding
     - "review", "audit", "check", "validate", "verify" → review
   → If no keywords match → context = "default"

3. Check for Override Triggers in User Message
   → "use different models", "change models", "update preferences"
   → If found → force_ask = true

4. Load or Learn Models
   → models = contextPreferences[context]

   IF models exist AND NOT force_ask:
     → USE models directly (no asking)
     → Go to step 6

   IF models empty OR force_ask:
     → Run: claudish --top-models
     → AskUserQuestion with multiSelect
     → Save user selection to contextPreferences[context]
     → Go to step 6

5. Save Updated Preferences
   → Write .claude/multimodel-team.json
   → Update lastUpdated timestamp

6. Execute with Models
   → Launch parallel validation
   → No further confirmation needed

Context Keywords:

Override Triggers (force re-selection):

• "use different models"
• "change models"
• "update model preferences"
• "select new models"

⚠️ Prefix Collision Awareness

CRITICAL: When using claudish, be aware of model ID prefix routing.

Claudish routes to different backends based on model ID prefix:

Collision-Free Models (safe for OpenRouter):

• x-ai/grok-code-fast-1 ✅
• google/gemini-* ✅ (use g/ for Gemini Direct)
• deepseek/deepseek-chat ✅
• minimax/* ✅ (use mmax/ for MiniMax Direct)
• qwen/qwen3-coder:free ✅
• mistralai/devstral-2512:free ✅
• moonshotai/* ✅ (use kimi/ for Kimi Direct)
• z-ai/glm-* ✅ (use glm/ for GLM Direct)
• openai/* ✅ (use oai/ for OpenAI Direct)
• anthropic/claude-* ✅

Direct API prefixes for cost savings:

Rule: OpenRouter models work without prefix. Use direct API prefixes for cost savings when you have the corresponding API key.

Interactive Model Selection (AskUserQuestion with multiSelect):

CRITICAL: Use AskUserQuestion tool with multiSelect: true to let users choose models interactively. This provides a better UX than just showing recommendations.

// Use AskUserQuestion to let user select models
AskUserQuestion({
  questions: [{
    question: "Which external models should validate your code? (Internal Claude reviewer always included)",
    header: "Models",
    multiSelect: true,
    options: [
      // Top paid (from claudish --top-models + historical data)
      {
        label: "x-ai/grok-code-fast-1 ⚡",
        description: "$0.85/1M | Quality: 87% | Avg: 42s | Fast + accurate"
      },
      {
        label: "google/gemini-3-pro-preview",
        description: "$7.00/1M | Quality: 91% | Avg: 55s | High accuracy"
      },
      // Free models (from claudish --free)
      {
        label: "qwen/qwen3-coder:free 🆓",
        description: "FREE | Quality: 82% | 262K context | Coding-specialized"
      },
      {
        label: "mistralai/devstral-2512:free 🆓",
        description: "FREE | 262K context | Dev-focused, new model"
      }
    ]
  }]
})

Remember Selection for Session:

Store the user's model selection in the session directory so it persists throughout the validation:

# After user selects models, save to session
save_session_models() {
  local session_dir="$1"
  shift
  local models=("$@")

  # Always include internal reviewer
  echo "claude-embedded" > "$session_dir/selected-models.txt"

  # Add user-selected models
  for model in "${models[@]}"; do
    echo "$model" >> "$session_dir/selected-models.txt"
  done

  echo "Session models saved to $session_dir/selected-models.txt"
}

# Load session models for subsequent operations
load_session_models() {
  local session_dir="$1"
  cat "$session_dir/selected-models.txt"
}

# Usage:
# After AskUserQuestion returns selected models
save_session_models "$SESSION_DIR" "x-ai/grok-code-fast-1" "qwen/qwen3-coder:free"

# Later in the session, retrieve the selection
MODELS=$(load_session_models "$SESSION_DIR")

Session Model Memory Structure:

$SESSION_DIR/
├── selected-models.txt    # User's model selection (persists for session)
├── code-context.md        # Code being reviewed
├── claude-review.md       # Internal review
├── grok-review.md         # External review (if selected)
├── qwen-coder-review.md   # External review (if selected)
└── consolidated-review.md # Final consolidated review

Why Remember the Selection:

1. Re-runs: If validation needs to be re-run, use same models
2. Consistency: All phases of validation use identical model set
3. Audit trail: Know which models produced which results
4. Cost tracking: Accurate cost attribution per session

Always Include Internal Reviewer:

BEST PRACTICE: Always run internal Claude reviewer alongside external models.

Why?
✓ FREE (embedded Claude, no API costs)
✓ Fast baseline (usually fastest)
✓ Provides comparison point
✓ Works even if ALL external models fail
✓ Consistent behavior (same model every time)

The internal reviewer should NEVER be optional - it's your safety net.

Pattern 1: The 4-Message Pattern (MANDATORY)

This pattern is CRITICAL for achieving true parallel execution with multiple AI models.

Why This Pattern Exists:

Claude Code executes tools sequentially by default when different tool types are mixed in the same message. To achieve true parallelism, you MUST:

1. Use ONLY one tool type per message
2. Ensure all Task calls are in a single message
3. Separate preparation (Bash) from execution (Task) from presentation

The Pattern:

Message 1: Preparation (Bash Only)
  - Create workspace directories
  - Validate inputs (check if claudish installed)
  - Write context files (code to review, design reference, etc.)
  - NO Task calls
  - NO Tasks calls

Message 2: Parallel Execution (Task Only)
  - Launch ALL AI models in SINGLE message
  - ONLY Task tool calls
  - Separate each Task with --- delimiter
  - Each Task is independent (no dependencies)
  - All execute simultaneously

Message 3: Auto-Consolidation (Task Only)
  - Automatically triggered when N ≥ 2 models complete
  - Launch consolidation agent
  - Pass all review file paths
  - Apply consensus analysis

Message 4: Present Results
  - Show user prioritized issues
  - Include consensus levels (unanimous, strong, majority)
  - Link to detailed reports
  - Cost summary (if applicable)

Example: 5-Model Parallel Code Review

Message 1: Preparation (Session Setup + Model Discovery)
  # Create unique session workspace
  Bash: SESSION_ID="review-$(date +%Y%m%d-%H%M%S)-$(head -c 4 /dev/urandom | xxd -p)"
  Bash: SESSION_DIR="ai-docs/sessions/${SESSION_ID}" && mkdir -p "$SESSION_DIR"
  Bash: git diff > "$SESSION_DIR/code-context.md"

  # Discover available models
  Bash: claudish --top-models  # See paid options
  Bash: claudish --free        # See free options

  # User selects models via AskUserQuestion (see Pattern 0)

Message 2: Parallel Execution (ONLY Task calls - single message)
  Task: senior-code-reviewer
    Prompt: "Review $SESSION_DIR/code-context.md for security issues.
             Write detailed review to $SESSION_DIR/claude-review.md
             Return only brief summary."
  ---
  Bash: claudish --model x-ai/grok-code-fast-1 --stdin --quiet
    < $SESSION_DIR/review-prompt.md > $SESSION_DIR/grok-review.md 2>$SESSION_DIR/grok-stderr.log
  ---
  Bash: claudish --model qwen/qwen3-coder:free --stdin --quiet
    < $SESSION_DIR/review-prompt.md > $SESSION_DIR/qwen-coder-review.md 2>$SESSION_DIR/qwen-stderr.log
  ---
  Bash: claudish --model openai/gpt-5.1-codex --stdin --quiet
    < $SESSION_DIR/review-prompt.md > $SESSION_DIR/gpt5-review.md 2>$SESSION_DIR/gpt5-stderr.log
  ---
  Bash: claudish --model mistralai/devstral-2512:free --stdin --quiet
    < $SESSION_DIR/review-prompt.md > $SESSION_DIR/devstral-review.md 2>$SESSION_DIR/devstral-stderr.log

  All 5 models execute simultaneously (5x parallelism!)

Message 3: Auto-Consolidation
  (Automatically triggered - don't wait for user to request)

  Task: senior-code-reviewer
    Prompt: "Consolidate 5 code reviews from:
             - $SESSION_DIR/claude-review.md
             - $SESSION_DIR/grok-review.md
             - $SESSION_DIR/qwen-coder-review.md
             - $SESSION_DIR/gpt5-review.md
             - $SESSION_DIR/devstral-review.md

             Apply consensus analysis:
             - Issues flagged by ALL 5 → UNANIMOUS (VERY HIGH confidence)
             - Issues flagged by 4 → STRONG (HIGH confidence)
             - Issues flagged by 3 → MAJORITY (MEDIUM confidence)
             - Issues flagged by 1-2 → DIVERGENT (LOW confidence)

             Prioritize by consensus level and severity.
             Write to $SESSION_DIR/consolidated-review.md"

Message 4: Present Results + Update Statistics
  # Track performance for each model (see Pattern 7)
  track_model_performance "claude-embedded" "success" 32 8 95
  track_model_performance "x-ai/grok-code-fast-1" "success" 45 6 87
  track_model_performance "qwen/qwen3-coder:free" "success" 52 5 82
  track_model_performance "openai/gpt-5.1-codex" "success" 68 7 89
  track_model_performance "mistralai/devstral-2512:free" "success" 48 5 84

  # Record session summary
  record_session_stats 5 5 0 68 245 3.6

  "Multi-model code review complete! 5 AI models analyzed your code.
   Session: $SESSION_ID

   Top 5 Issues (Prioritized by Consensus):
   1. [UNANIMOUS] Missing input validation on POST /api/users
   2. [UNANIMOUS] SQL injection risk in search endpoint
   3. [STRONG] Weak password hashing (bcrypt rounds too low)
   4. [MAJORITY] Missing rate limiting on authentication endpoints
   5. [MAJORITY] Insufficient error handling in payment flow

   Model Performance (this session):
   | Model                          | Time | Issues | Quality | Cost   |
   |--------------------------------|------|--------|---------|--------|
   | claude-embedded                | 32s  | 8      | 95%     | FREE   |
   | x-ai/grok-code-fast-1          | 45s  | 6      | 87%     | $0.002 |
   | qwen/qwen3-coder:free          | 52s  | 5      | 82%     | FREE   |
   | openai/gpt-5.1-codex        | 68s  | 7      | 89%     | $0.015 |
   | mistralai/devstral-2512:free   | 48s  | 5      | 84%     | FREE   |

   Parallel Speedup: 3.6x (245s sequential → 68s parallel)

   See $SESSION_DIR/consolidated-review.md for complete analysis.
   Performance logged to ai-docs/llm-performance.json"

Performance Impact:

• Sequential execution: 5 models × 3 min = 15 minutes
• Parallel execution: max(model times) ≈ 5 minutes
• Speedup: 3x with perfect parallelism

Pattern 2: Parallel Execution Architecture

Single Message, Multiple Tasks:

The key to parallel execution is putting ALL Task calls in a single message with the --- delimiter:

✅ CORRECT - Parallel Execution:

Task: agent1
  Prompt: "Task 1 instructions"
---
Task: agent2
  Prompt: "Task 2 instructions"
---
Task: agent3
  Prompt: "Task 3 instructions"

All 3 execute simultaneously.

Anti-Pattern: Sequential Execution

❌ WRONG - Sequential Execution:

Message 1:
  Task: agent1

Message 2:
  Task: agent2

Message 3:
  Task: agent3

Each task waits for previous to complete (3x slower).

Independent Tasks Requirement:

Each Task must be independent (no dependencies):

✅ CORRECT - Independent:
  Task: review code for security
  Task: review code for performance
  Task: review code for style

  All can run simultaneously (same input, different perspectives).

❌ WRONG - Dependent:
  Task: implement feature
  Task: write tests for feature (depends on implementation)
  Task: review implementation (depends on tests)

  Must run sequentially (each needs previous output).

Unique Output Files:

Each Task MUST write to a unique output file within the session directory:

✅ CORRECT - Unique Files in Session Directory:
  Task: reviewer1 → $SESSION_DIR/claude-review.md
  Task: reviewer2 → $SESSION_DIR/grok-review.md
  Task: reviewer3 → $SESSION_DIR/qwen-coder-review.md

❌ WRONG - Shared File:
  Task: reviewer1 → $SESSION_DIR/review.md
  Task: reviewer2 → $SESSION_DIR/review.md (overwrites reviewer1!)
  Task: reviewer3 → $SESSION_DIR/review.md (overwrites reviewer2!)

❌ WRONG - Fixed Directory (not session-based):
  Task: reviewer1 → ai-docs/reviews/claude-review.md  # May conflict with other sessions!

Wait for All Before Consolidation:

Do NOT consolidate until ALL tasks complete:

✅ CORRECT - Wait for All:
  Launch: Task1, Task2, Task3, Task4 (parallel)
  Wait: All 4 complete
  Check: results.filter(r => r.status === 'fulfilled').length
  If >= 2: Proceed with consolidation
  If < 2: Offer retry or abort

❌ WRONG - Premature Consolidation:
  Launch: Task1, Task2, Task3, Task4
  After 30s: Task1, Task2 done
  Consolidate: Only Task1 + Task2 (Task3, Task4 still running!)

Pattern 3: External Model Invocation via Bash+claudish

How External Models Are Invoked:

External AI models are invoked deterministically via Bash+claudish CLI. The orchestrator calls claudish directly — no LLM delegation needed. This is 100% reliable.

# Pattern: Bash tool with run_in_background
claudish --model x-ai/grok-code-fast-1 --stdin --quiet \
  < $SESSION_DIR/prompt.md > $SESSION_DIR/grok-result.md 2>$SESSION_DIR/grok-stderr.log; \
  echo $? > $SESSION_DIR/grok.exit

Required Flags:

• --model — The external model ID
• --stdin — Read prompt from stdin
• --quiet — Clean output for file capture

Output Strategy:

Claudish writes full output to the redirect file. The orchestrator reads results after completion:

Full Output ($SESSION_DIR/grok-result.md):
  "# Code Review by Grok
   ## Security Issues
   ### CRITICAL: SQL Injection in User Search
   [full detailed analysis]"

Verification:
  Exit code ($SESSION_DIR/grok.exit): 0
  Stderr ($SESSION_DIR/grok-stderr.log): (empty = success)

Auto-Approve Behavior:

Claudish auto-approves by default (non-interactive mode for scripting):

✅ CORRECT - Auto-approve is default, no flag needed:
  claudish --model grok --stdin --quiet

⚠️ Interactive mode (requires user input, avoid in automation):
  claudish --model grok --stdin --quiet --no-auto-approve

Correct Pattern Example

# ✅ CORRECT: External model via Bash+claudish (deterministic)
Bash({
  command: "claudish --model x-ai/grok-code-fast-1 --stdin --quiet < session/prompt.md > session/grok-result.md 2>session/grok-stderr.log; echo $? > session/grok.exit",
  description: "Run Grok review via claudish",
  run_in_background: true
})

# ✅ CORRECT: Internal model via Task
Task({
  subagent_type: "dev:researcher",
  description: "Internal Claude review",
  run_in_background: true,
  prompt: "Review the design plan...\n\nWrite to: session/internal-result.md"
})

Pattern 4: Cost Estimation and Transparency

Input/Output Token Separation:

Provide separate estimates for input and output tokens:

Cost Estimation for Multi-Model Review:

Input Tokens (per model):
  - Code context: 500 lines × 1.5 = 750 tokens
  - Review instructions: 200 tokens
  - Total input per model: ~1000 tokens
  - Total input (5 models): 5,000 tokens

Output Tokens (per model):
  - Expected output: 2,000 - 4,000 tokens
  - Total output (5 models): 10,000 - 20,000 tokens

Cost Calculation (example rates):
  - Input: 5,000 tokens × $0.0001/1k = $0.0005
  - Output: 15,000 tokens × $0.0005/1k = $0.0075 (3-5x more expensive)
  - Total: $0.0080 (range: $0.0055 - $0.0105)

User Approval Gate:
  "Multi-model review will cost approximately $0.008 ($0.005 - $0.010).
   Proceed? (Yes/No)"

Input Token Estimation Formula:

Input Tokens = (Code Lines × 1.5) + Instruction Tokens

Why 1.5x multiplier?
  - Code lines: ~1 token per line (average)
  - Context overhead: +50% (imports, comments, whitespace)

Example:
  500 lines of code → 500 × 1.5 = 750 tokens
  + 200 instruction tokens = 950 tokens total input

Output Token Estimation Formula:

Output Tokens = Base Estimate + Complexity Factor

Base Estimates by Task Type:
  - Code review: 2,000 - 4,000 tokens
  - Design validation: 1,000 - 2,000 tokens
  - Architecture planning: 3,000 - 6,000 tokens
  - Bug investigation: 2,000 - 5,000 tokens

Complexity Factors:
  - Simple (< 100 lines code): Use low end of range
  - Medium (100-500 lines): Use mid-range
  - Complex (> 500 lines): Use high end of range

Example:
  400 lines of complex code → 4,000 tokens (high complexity)
  50 lines of simple code → 2,000 tokens (low complexity)

Range-Based Estimates:

Always provide a range (min-max), not a single number:

✅ CORRECT - Range:
  "Estimated cost: $0.005 - $0.010 (depends on review depth)"

❌ WRONG - Single Number:
  "Estimated cost: $0.0075"
  (User surprised when actual is $0.0095)

Why Output Costs More:

Output tokens are typically 3-5x more expensive than input tokens:

Example Pricing (OpenRouter):
  - Grok: $0.50 / 1M input, $1.50 / 1M output (3x difference)
  - Gemini Flash: $0.10 / 1M input, $0.40 / 1M output (4x difference)
  - GPT-5 Codex: $1.00 / 1M input, $5.00 / 1M output (5x difference)

Impact:
  If input = 5,000 tokens, output = 15,000 tokens:
    Input cost: $0.0005
    Output cost: $0.0075 (15x higher despite only 3x more tokens)
    Total: $0.0080 (94% is output!)

User Approval Before Execution:

ALWAYS ask for user approval before expensive operations:

Present to user:
  "You selected 5 AI models for code review:
   - Claude Sonnet (embedded, free)
   - Grok Code Fast (external, $0.002)
   - Gemini 2.5 Flash (external, $0.001)
   - GPT-5 Codex (external, $0.004)
   - DeepSeek Coder (external, $0.001)

   Estimated total cost: $0.008 ($0.005 - $0.010)

   Proceed with multi-model review? (Yes/No)"

If user says NO:
  Offer alternatives:
    1. Use only free embedded Claude
    2. Select fewer models
    3. Cancel review

If user says YES:
  Proceed with Message 2 (parallel execution)

Pattern 5: Auto-Consolidation Logic

Automatic Trigger:

Consolidation should happen automatically when N ≥ 2 reviews complete:

✅ CORRECT - Auto-Trigger:

const results = await Promise.allSettled([task1, task2, task3, task4, task5]);
const successful = results.filter(r => r.status === 'fulfilled');

if (successful.length >= 2) {
  // Auto-trigger consolidation (DON'T wait for user to ask)
  const consolidated = await Task({
    subagent_type: "senior-code-reviewer",
    description: "Consolidate reviews",
    prompt: `Consolidate ${successful.length} reviews and apply consensus analysis`
  });

  return formatResults(consolidated);
} else {
  // Too few successful reviews
  notifyUser("Only 1 model succeeded. Retry failures or abort?");
}

❌ WRONG - Wait for User:

const results = await Promise.allSettled([...]);
const successful = results.filter(r => r.status === 'fulfilled');

// Present results to user
notifyUser("3 reviews complete. Would you like me to consolidate them?");
// Waits for user to request consolidation...

Why Auto-Trigger:

• Better UX (no extra user prompt needed)
• Faster workflow (no wait for user response)
• Expected behavior (user assumes consolidation is part of workflow)

Minimum Threshold:

Require at least 2 successful reviews for meaningful consensus:

if (successful.length >= 2) {
  // Proceed with consolidation
} else if (successful.length === 1) {
  // Only 1 review succeeded
  notifyUser("Only 1 model succeeded. No consensus available. See single review or retry?");
} else {
  // All failed
  notifyUser("All models failed. Check logs and retry?");
}

Pass All Review File Paths:

Consolidation agent needs paths to ALL review files within the session directory:

Task: senior-code-reviewer
  Prompt: "Consolidate reviews from these files:
           - $SESSION_DIR/claude-review.md
           - $SESSION_DIR/grok-review.md
           - $SESSION_DIR/qwen-coder-review.md

           Apply consensus analysis and prioritize issues."

Don't Inline Full Reviews:

❌ WRONG - Inline Reviews (context pollution):
  Prompt: "Consolidate these reviews:

           Claude Review:
           [500 lines of review content]

           Grok Review:
           [500 lines of review content]

           Qwen Review:
           [500 lines of review content]"

✅ CORRECT - File Paths in Session Directory:
  Prompt: "Read and consolidate reviews from:
           - $SESSION_DIR/claude-review.md
           - $SESSION_DIR/grok-review.md
           - $SESSION_DIR/qwen-coder-review.md"

Pattern 6: Consensus Analysis

Consensus Levels:

Classify issues by how many models flagged them:

Consensus Levels (for N models):

UNANIMOUS (100% agreement):
  - All N models flagged this issue
  - VERY HIGH confidence
  - MUST FIX priority

STRONG CONSENSUS (67-99% agreement):
  - Most models flagged this issue (⌈2N/3⌉ to N-1)
  - HIGH confidence
  - RECOMMENDED priority

MAJORITY (50-66% agreement):
  - Half or more models flagged this issue (⌈N/2⌉ to ⌈2N/3⌉-1)
  - MEDIUM confidence
  - CONSIDER priority

DIVERGENT (< 50% agreement):
  - Only 1-2 models flagged this issue
  - LOW confidence
  - OPTIONAL priority (may be model-specific perspective)

Example: 5 Models

Issue Flagged By:              Consensus Level:    Priority:
─────────────────────────────────────────────────────────────
All 5 models                   UNANIMOUS (100%)    MUST FIX
4 models                       STRONG (80%)        RECOMMENDED
3 models                       MAJORITY (60%)      CONSIDER
2 models                       DIVERGENT (40%)     OPTIONAL
1 model                        DIVERGENT (20%)     OPTIONAL

Keyword-Based Matching (v1.0):

Simple consensus analysis using keyword matching:

Algorithm:

1. Extract issues from each review
2. For each unique issue:
   a. Identify keywords (e.g., "SQL injection", "input validation")
   b. Check which other reviews mention same keywords
   c. Count models that flagged this issue
   d. Assign consensus level

Example:

Claude Review: "Missing input validation on POST /api/users"
Grok Review: "Input validation absent in user creation endpoint"
Gemini Review: "No validation for user POST endpoint"

Keywords: ["input validation", "POST", "/api/users", "user"]
Match: All 3 reviews mention these keywords
Consensus: UNANIMOUS (3/3 = 100%)

Model Agreement Matrix:

Show which models agree on which issues:

Issue Matrix:

Issue                             Claude  Grok  Gemini  GPT-5  DeepSeek  Consensus
──────────────────────────────────────────────────────────────────────────────────
SQL injection in search              ✓      ✓     ✓       ✓       ✓      UNANIMOUS
Missing input validation             ✓      ✓     ✓       ✓       ✗      STRONG
Weak password hashing                ✓      ✓     ✓       ✗       ✗      MAJORITY
Missing rate limiting                ✓      ✓     ✗       ✗       ✗      DIVERGENT
Insufficient error handling          ✓      ✗     ✗       ✗       ✗      DIVERGENT

Prioritized Issue List:

Sort issues by consensus level, then by severity:

Top 10 Issues (Prioritized):

1. [UNANIMOUS - CRITICAL] SQL injection in search endpoint
   Flagged by: Claude, Grok, Gemini, GPT-5, DeepSeek (5/5)

2. [UNANIMOUS - HIGH] Missing input validation on POST /api/users
   Flagged by: Claude, Grok, Gemini, GPT-5, DeepSeek (5/5)

3. [STRONG - HIGH] Weak password hashing (bcrypt rounds too low)
   Flagged by: Claude, Grok, Gemini, GPT-5 (4/5)

4. [STRONG - MEDIUM] Missing rate limiting on auth endpoints
   Flagged by: Claude, Grok, Gemini, GPT-5 (4/5)

5. [MAJORITY - MEDIUM] Insufficient error handling in payment flow
   Flagged by: Claude, Grok, Gemini (3/5)

... (remaining issues)

Future Enhancement (v1.1+): Semantic Similarity

Instead of keyword matching, use semantic similarity:
  - Embed issue descriptions with sentence-transformers
  - Calculate cosine similarity between embeddings
  - Issues with >0.8 similarity are "same issue"
  - More accurate consensus detection

Pattern 7: Statistics Collection and Analysis

Purpose: Track model performance to help users identify slow or poorly-performing models for future exclusion.

Storage Location: ai-docs/llm-performance.json (persistent across all sessions)

When to Collect Statistics:

• After each model completes (success, failure, or timeout)
• During consolidation phase (quality scores)
• At session end (session summary)

File Structure (ai-docs/llm-performance.json):

{
  "schemaVersion": "2.0.0",
  "lastUpdated": "2025-12-12T10:45:00Z",
  "models": {
    "claude-embedded": {
      "modelId": "claude-embedded",
      "provider": "Anthropic",
      "isFree": true,
      "pricing": "FREE (embedded)",
      "totalRuns": 12,
      "successfulRuns": 12,
      "failedRuns": 0,
      "totalExecutionTime": 420,
      "avgExecutionTime": 35,
      "minExecutionTime": 28,
      "maxExecutionTime": 52,
      "totalIssuesFound": 96,
      "avgQualityScore": 92,
      "totalCost": 0,
      "qualityScores": [95, 90, 88, 94, 91],
      "lastUsed": "2025-12-12T10:35:22Z",
      "trend": "stable",
      "history": [
        {
          "timestamp": "2025-12-12T10:35:22Z",
          "session": "review-20251212-103522-a3f2",
          "status": "success",
          "executionTime": 32,
          "issuesFound": 8,
          "qualityScore": 95,
          "cost": 0
        }
      ]
    },
    "x-ai-grok-code-fast-1": {
      "modelId": "x-ai/grok-code-fast-1",
      "provider": "X-ai",
      "isFree": false,
      "pricing": "$0.85/1M",
      "totalRuns": 10,
      "successfulRuns": 9,
      "failedRuns": 1,
      "totalCost": 0.12,
      "trend": "improving"
    },
    "qwen-qwen3-coder-free": {
      "modelId": "qwen/qwen3-coder:free",
      "provider": "Qwen",
      "isFree": true,
      "pricing": "FREE",
      "totalRuns": 5,
      "successfulRuns": 5,
      "failedRuns": 0,
      "totalCost": 0,
      "trend": "stable"
    }
  },
  "sessions": [
    {
      "sessionId": "review-20251212-103522-a3f2",
      "timestamp": "2025-12-12T10:35:22Z",
      "totalModels": 4,
      "successfulModels": 3,
      "failedModels": 1,
      "parallelTime": 120,
      "sequentialTime": 335,
      "speedup": 2.8,
      "totalCost": 0.018,
      "freeModelsUsed": 2
    }
  ],
  "recommendations": {
    "topPaid": ["x-ai/grok-code-fast-1", "google/gemini-3-pro-preview"],
    "topFree": ["qwen/qwen3-coder:free", "mistralai/devstral-2512:free"],
    "bestValue": ["x-ai/grok-code-fast-1"],
    "avoid": [],
    "lastGenerated": "2025-12-12T10:45:00Z"
  }
}

Key Benefits of Persistent Storage:

• Track model reliability over time (not just one session)
• Identify consistently slow models
• Calculate historical success rates
• Generate data-driven shortlist recommendations

How to Calculate Quality Score:

Quality = % of model's issues that appear in unanimous or strong consensus

quality_score = (issues_in_unanimous + issues_in_strong) / total_issues * 100

Example:
- Model found 10 issues
- 4 appear in unanimous consensus
- 3 appear in strong consensus
- Quality = (4 + 3) / 10 * 100 = 70%

Higher quality means the model finds issues other models agree with.

How to Calculate Parallel Speedup:

speedup = sum(all_execution_times) / max(execution_time)

Example:
- Claude: 32s
- Grok: 45s
- Gemini: 38s
- GPT-5: 120s

Sequential would take: 32 + 45 + 38 + 120 = 235s
Parallel took: max(32, 45, 38, 120) = 120s
Speedup: 235 / 120 = 1.96x

Performance Statistics Display Format:

## Model Performance Statistics

| Model                     | Time   | Issues | Quality | Status    |
|---------------------------|--------|--------|---------|-----------|
| claude-embedded           | 32s    | 8      | 95%     | ✓         |
| x-ai/grok-code-fast-1     | 45s    | 6      | 85%     | ✓         |
| google/gemini-2.5-flash   | 38s    | 5      | 90%     | ✓         |
| openai/gpt-5.1-codex   | 120s   | 9      | 88%     | ✓ (slow)  |
| deepseek/deepseek-chat    | TIMEOUT| 0      | -       | ✗         |

**Session Summary:**
- Parallel Speedup: 1.96x (235s sequential → 120s parallel)
- Average Time: 59s
- Slowest: gpt-5.1-codex (2.0x avg)

**Recommendations:**
⚠️ gpt-5.1-codex runs 2x slower than average - consider removing
⚠️ deepseek-chat timed out - check API status or remove from shortlist
✓ Top performers: claude-embedded, gemini-2.5-flash (fast + high quality)

Recommendation Logic:

1. Flag SLOW models:
   if (model.executionTime > 2 * avgExecutionTime) {
     flag: "⚠️ Runs 2x+ slower than average"
     suggestion: "Consider removing from shortlist"
   }

2. Flag FAILED/TIMEOUT models:
   if (model.status !== "success") {
     flag: "⚠️ Failed or timed out"
     suggestion: "Check API status or increase timeout"
   }

3. Identify TOP PERFORMERS:
   if (model.qualityScore > 85 && model.executionTime < avgExecutionTime) {
     highlight: "✓ Top performer (fast + high quality)"
   }

4. Suggest SHORTLIST:
   sortedModels = models.sort((a, b) => {
     // Quality/speed ratio: higher quality + lower time = better
     scoreA = a.qualityScore / (a.executionTime / avgExecutionTime)
     scoreB = b.qualityScore / (b.executionTime / avgExecutionTime)
     return scoreB - scoreA
   })
   shortlist = sortedModels.slice(0, 3)

Implementation (writes to ai-docs/llm-performance.json):

# Track model performance after each model completes
# Updates historical aggregates and adds to run history
# Parameters: model_id, status, duration, issues, quality_score, cost, is_free
track_model_performance() {
  local model_id="$1"
  local status="$2"
  local duration="$3"
  local issues="${4:-0}"
  local quality_score="${5:-}"
  local cost="${6:-0}"
  local is_free="${7:-false}"

  local perf_file="ai-docs/llm-performance.json"
  local model_key=$(echo "$model_id" | tr '/:' '-')  # Handle colons in free model names

  # Initialize file if doesn't exist
  [[ -f "$perf_file" ]] || echo '{"schemaVersion":"2.0.0","models":{},"sessions":[],"recommendations":{}}' > "$perf_file"

  jq --arg model "$model_key" \
     --arg model_full "$model_id" \
     --arg status "$status" \
  

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