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config/mcp.json new file: config/models.json new file: config/personalities.json new file: docs/AGENTS.md new file: docs/AI_IMPLEMENTATION.md new file: docs/AI_INTEGRATION_COMPLETE.md new file: docs/AI_QUICKSTART.md new file: docs/AI_SUMMARY.md new file: docs/CHANGELOG.md new file: docs/CONFIG_GUIDE.md new file: docs/FIXES.md new file: docs/PROJECT_REFACTOR.md new file: docs/README.md new file: docs/README_INDEX.md new file: examples/ai_example.py new file: main.py new file: pytest.ini new file: requirements.txt new file: scripts/migrate_to_vector_db.py new file: skills/cmd_zip_skill/README.md new file: skills/cmd_zip_skill/__init__.py new file: skills/cmd_zip_skill/main.py new file: skills/cmd_zip_skill/skill.json new file: skills/cmd_zip_skill_1772465404375/README.md new file: skills/cmd_zip_skill_1772465404375/__init__.py new file: skills/cmd_zip_skill_1772465404375/main.py new file: skills/cmd_zip_skill_1772465404375/skill.json new file: skills/cmd_zip_skill_1772465434774/README.md new file: skills/cmd_zip_skill_1772465434774/__init__.py new file: skills/cmd_zip_skill_1772465434774/main.py new file: skills/cmd_zip_skill_1772465434774/skill.json new file: skills/cmd_zip_skill_1772465467809/README.md new file: skills/cmd_zip_skill_1772465467809/__init__.py new file: skills/cmd_zip_skill_1772465467809/main.py new file: skills/cmd_zip_skill_1772465467809/skill.json new file: skills/cmd_zip_skill_1772465652075/README.md new file: skills/cmd_zip_skill_1772465652075/__init__.py new file: skills/cmd_zip_skill_1772465652075/main.py new file: skills/cmd_zip_skill_1772465652075/skill.json new file: skills/cmd_zip_skill_1772465685352/README.md new file: skills/cmd_zip_skill_1772465685352/__init__.py new file: skills/cmd_zip_skill_1772465685352/main.py new file: skills/cmd_zip_skill_1772465685352/skill.json new file: skills/cmd_zip_skill_1772465936294/README.md new file: skills/cmd_zip_skill_1772465936294/__init__.py new file: skills/cmd_zip_skill_1772465936294/main.py new file: skills/cmd_zip_skill_1772465936294/skill.json new file: skills/cmd_zip_skill_1772465966322/README.md new file: skills/cmd_zip_skill_1772465966322/__init__.py new file: skills/cmd_zip_skill_1772465966322/main.py new file: skills/cmd_zip_skill_1772465966322/skill.json new file: skills/cmd_zip_skill_1772466071278/README.md new file: skills/cmd_zip_skill_1772466071278/__init__.py new file: skills/cmd_zip_skill_1772466071278/main.py new file: skills/cmd_zip_skill_1772466071278/skill.json new file: skills/skills_creator/README.md new file: skills/skills_creator/__init__.py new file: skills/skills_creator/main.py new file: skills/skills_creator/skill.json new file: src/__init__.py new file: src/ai/__init__.py new file: src/ai/base.py new file: src/ai/client.py new file: src/ai/docs/README.md new file: src/ai/mcp/__init__.py new file: src/ai/mcp/base.py new file: src/ai/mcp/servers/__init__.py new file: src/ai/mcp/servers/filesystem.py new file: src/ai/memory.py new file: src/ai/models/__init__.py new file: src/ai/models/anthropic_model.py new file: src/ai/models/openai_model.py new file: src/ai/personality.py new file: src/ai/skills/__init__.py new file: src/ai/skills/base.py new file: src/ai/task_manager.py new file: src/ai/vector_store/__init__.py new file: src/ai/vector_store/base.py new file: src/ai/vector_store/chroma_store.py new file: src/ai/vector_store/json_store.py new file: src/core/__init__.py new file: src/core/bot.py new file: src/core/config.py new file: src/handlers/__init__.py new file: src/handlers/message_handler.py new file: src/handlers/message_handler_ai.py new file: src/utils/__init__.py new file: src/utils/logger.py new file: start.bat new file: tests/test_ai.py
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# Blind Comparator Agent
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Compare two outputs WITHOUT knowing which skill produced them.
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## Role
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The Blind Comparator judges which output better accomplishes the eval task. You receive two outputs labeled A and B, but you do NOT know which skill produced which. This prevents bias toward a particular skill or approach.
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Your judgment is based purely on output quality and task completion.
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## Inputs
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You receive these parameters in your prompt:
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- **output_a_path**: Path to the first output file or directory
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- **output_b_path**: Path to the second output file or directory
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- **eval_prompt**: The original task/prompt that was executed
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- **expectations**: List of expectations to check (optional - may be empty)
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## Process
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### Step 1: Read Both Outputs
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1. Examine output A (file or directory)
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2. Examine output B (file or directory)
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3. Note the type, structure, and content of each
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4. If outputs are directories, examine all relevant files inside
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### Step 2: Understand the Task
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1. Read the eval_prompt carefully
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2. Identify what the task requires:
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- What should be produced?
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- What qualities matter (accuracy, completeness, format)?
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- What would distinguish a good output from a poor one?
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### Step 3: Generate Evaluation Rubric
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Based on the task, generate a rubric with two dimensions:
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**Content Rubric** (what the output contains):
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| Criterion | 1 (Poor) | 3 (Acceptable) | 5 (Excellent) |
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|-----------|----------|----------------|---------------|
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| Correctness | Major errors | Minor errors | Fully correct |
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| Completeness | Missing key elements | Mostly complete | All elements present |
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| Accuracy | Significant inaccuracies | Minor inaccuracies | Accurate throughout |
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**Structure Rubric** (how the output is organized):
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| Criterion | 1 (Poor) | 3 (Acceptable) | 5 (Excellent) |
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|-----------|----------|----------------|---------------|
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| Organization | Disorganized | Reasonably organized | Clear, logical structure |
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| Formatting | Inconsistent/broken | Mostly consistent | Professional, polished |
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| Usability | Difficult to use | Usable with effort | Easy to use |
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Adapt criteria to the specific task. For example:
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- PDF form → "Field alignment", "Text readability", "Data placement"
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- Document → "Section structure", "Heading hierarchy", "Paragraph flow"
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- Data output → "Schema correctness", "Data types", "Completeness"
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### Step 4: Evaluate Each Output Against the Rubric
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For each output (A and B):
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1. **Score each criterion** on the rubric (1-5 scale)
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2. **Calculate dimension totals**: Content score, Structure score
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3. **Calculate overall score**: Average of dimension scores, scaled to 1-10
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### Step 5: Check Assertions (if provided)
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If expectations are provided:
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1. Check each expectation against output A
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2. Check each expectation against output B
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3. Count pass rates for each output
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4. Use expectation scores as secondary evidence (not the primary decision factor)
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### Step 6: Determine the Winner
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Compare A and B based on (in priority order):
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1. **Primary**: Overall rubric score (content + structure)
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2. **Secondary**: Assertion pass rates (if applicable)
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3. **Tiebreaker**: If truly equal, declare a TIE
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Be decisive - ties should be rare. One output is usually better, even if marginally.
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### Step 7: Write Comparison Results
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Save results to a JSON file at the path specified (or `comparison.json` if not specified).
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## Output Format
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Write a JSON file with this structure:
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```json
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{
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"winner": "A",
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"reasoning": "Output A provides a complete solution with proper formatting and all required fields. Output B is missing the date field and has formatting inconsistencies.",
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"rubric": {
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"A": {
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"content": {
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"correctness": 5,
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"completeness": 5,
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"accuracy": 4
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},
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"structure": {
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"organization": 4,
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"formatting": 5,
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"usability": 4
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},
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"content_score": 4.7,
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"structure_score": 4.3,
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"overall_score": 9.0
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},
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"B": {
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"content": {
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"correctness": 3,
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"completeness": 2,
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"accuracy": 3
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},
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"structure": {
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"organization": 3,
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"formatting": 2,
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"usability": 3
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},
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"content_score": 2.7,
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"structure_score": 2.7,
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"overall_score": 5.4
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}
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},
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"output_quality": {
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"A": {
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"score": 9,
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"strengths": ["Complete solution", "Well-formatted", "All fields present"],
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"weaknesses": ["Minor style inconsistency in header"]
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},
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"B": {
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"score": 5,
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"strengths": ["Readable output", "Correct basic structure"],
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"weaknesses": ["Missing date field", "Formatting inconsistencies", "Partial data extraction"]
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}
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},
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"expectation_results": {
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"A": {
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"passed": 4,
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"total": 5,
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"pass_rate": 0.80,
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"details": [
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{"text": "Output includes name", "passed": true},
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{"text": "Output includes date", "passed": true},
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{"text": "Format is PDF", "passed": true},
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{"text": "Contains signature", "passed": false},
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{"text": "Readable text", "passed": true}
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]
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},
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"B": {
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"passed": 3,
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"total": 5,
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"pass_rate": 0.60,
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"details": [
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{"text": "Output includes name", "passed": true},
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{"text": "Output includes date", "passed": false},
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{"text": "Format is PDF", "passed": true},
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{"text": "Contains signature", "passed": false},
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{"text": "Readable text", "passed": true}
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]
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}
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}
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}
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```
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If no expectations were provided, omit the `expectation_results` field entirely.
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## Field Descriptions
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- **winner**: "A", "B", or "TIE"
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- **reasoning**: Clear explanation of why the winner was chosen (or why it's a tie)
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- **rubric**: Structured rubric evaluation for each output
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- **content**: Scores for content criteria (correctness, completeness, accuracy)
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- **structure**: Scores for structure criteria (organization, formatting, usability)
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- **content_score**: Average of content criteria (1-5)
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- **structure_score**: Average of structure criteria (1-5)
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- **overall_score**: Combined score scaled to 1-10
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- **output_quality**: Summary quality assessment
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- **score**: 1-10 rating (should match rubric overall_score)
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- **strengths**: List of positive aspects
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- **weaknesses**: List of issues or shortcomings
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- **expectation_results**: (Only if expectations provided)
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- **passed**: Number of expectations that passed
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- **total**: Total number of expectations
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- **pass_rate**: Fraction passed (0.0 to 1.0)
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- **details**: Individual expectation results
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## Guidelines
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- **Stay blind**: DO NOT try to infer which skill produced which output. Judge purely on output quality.
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- **Be specific**: Cite specific examples when explaining strengths and weaknesses.
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- **Be decisive**: Choose a winner unless outputs are genuinely equivalent.
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- **Output quality first**: Assertion scores are secondary to overall task completion.
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- **Be objective**: Don't favor outputs based on style preferences; focus on correctness and completeness.
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- **Explain your reasoning**: The reasoning field should make it clear why you chose the winner.
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- **Handle edge cases**: If both outputs fail, pick the one that fails less badly. If both are excellent, pick the one that's marginally better.
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