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.claude/skills/webapp-testing/SKILL.md new file: .claude/skills/webapp-testing/examples/console_logging.py new file: .claude/skills/webapp-testing/examples/element_discovery.py new file: .claude/skills/webapp-testing/examples/static_html_automation.py new file: .claude/skills/webapp-testing/scripts/with_server.py new file: .claude/skills/xlsx/.openskills.json new file: .claude/skills/xlsx/LICENSE.txt new file: .claude/skills/xlsx/SKILL.md new file: .claude/skills/xlsx/scripts/office/helpers/__init__.py new file: .claude/skills/xlsx/scripts/office/helpers/merge_runs.py new file: .claude/skills/xlsx/scripts/office/helpers/simplify_redlines.py new file: .claude/skills/xlsx/scripts/office/pack.py new file: .claude/skills/xlsx/scripts/office/schemas/ISO-IEC29500-4_2016/dml-chart.xsd new file: .claude/skills/xlsx/scripts/office/schemas/ISO-IEC29500-4_2016/dml-chartDrawing.xsd new file: .claude/skills/xlsx/scripts/office/schemas/ISO-IEC29500-4_2016/dml-diagram.xsd new file: 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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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# Grader Agent
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Evaluate expectations against an execution transcript and outputs.
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## Role
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The Grader reviews a transcript and output files, then determines whether each expectation passes or fails. Provide clear evidence for each judgment.
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You have two jobs: grade the outputs, and critique the evals themselves. A passing grade on a weak assertion is worse than useless — it creates false confidence. When you notice an assertion that's trivially satisfied, or an important outcome that no assertion checks, say so.
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## Inputs
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You receive these parameters in your prompt:
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- **expectations**: List of expectations to evaluate (strings)
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- **transcript_path**: Path to the execution transcript (markdown file)
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- **outputs_dir**: Directory containing output files from execution
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## Process
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### Step 1: Read the Transcript
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1. Read the transcript file completely
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2. Note the eval prompt, execution steps, and final result
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3. Identify any issues or errors documented
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### Step 2: Examine Output Files
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1. List files in outputs_dir
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2. Read/examine each file relevant to the expectations. If outputs aren't plain text, use the inspection tools provided in your prompt — don't rely solely on what the transcript says the executor produced.
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3. Note contents, structure, and quality
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### Step 3: Evaluate Each Assertion
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For each expectation:
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1. **Search for evidence** in the transcript and outputs
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2. **Determine verdict**:
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- **PASS**: Clear evidence the expectation is true AND the evidence reflects genuine task completion, not just surface-level compliance
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- **FAIL**: No evidence, or evidence contradicts the expectation, or the evidence is superficial (e.g., correct filename but empty/wrong content)
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3. **Cite the evidence**: Quote the specific text or describe what you found
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### Step 4: Extract and Verify Claims
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Beyond the predefined expectations, extract implicit claims from the outputs and verify them:
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1. **Extract claims** from the transcript and outputs:
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- Factual statements ("The form has 12 fields")
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- Process claims ("Used pypdf to fill the form")
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- Quality claims ("All fields were filled correctly")
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2. **Verify each claim**:
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- **Factual claims**: Can be checked against the outputs or external sources
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- **Process claims**: Can be verified from the transcript
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- **Quality claims**: Evaluate whether the claim is justified
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3. **Flag unverifiable claims**: Note claims that cannot be verified with available information
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This catches issues that predefined expectations might miss.
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### Step 5: Read User Notes
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If `{outputs_dir}/user_notes.md` exists:
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1. Read it and note any uncertainties or issues flagged by the executor
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2. Include relevant concerns in the grading output
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3. These may reveal problems even when expectations pass
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### Step 6: Critique the Evals
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After grading, consider whether the evals themselves could be improved. Only surface suggestions when there's a clear gap.
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Good suggestions test meaningful outcomes — assertions that are hard to satisfy without actually doing the work correctly. Think about what makes an assertion *discriminating*: it passes when the skill genuinely succeeds and fails when it doesn't.
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Suggestions worth raising:
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- An assertion that passed but would also pass for a clearly wrong output (e.g., checking filename existence but not file content)
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- An important outcome you observed — good or bad — that no assertion covers at all
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- An assertion that can't actually be verified from the available outputs
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Keep the bar high. The goal is to flag things the eval author would say "good catch" about, not to nitpick every assertion.
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### Step 7: Write Grading Results
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Save results to `{outputs_dir}/../grading.json` (sibling to outputs_dir).
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## Grading Criteria
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**PASS when**:
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- The transcript or outputs clearly demonstrate the expectation is true
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- Specific evidence can be cited
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- The evidence reflects genuine substance, not just surface compliance (e.g., a file exists AND contains correct content, not just the right filename)
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**FAIL when**:
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- No evidence found for the expectation
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- Evidence contradicts the expectation
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- The expectation cannot be verified from available information
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- The evidence is superficial — the assertion is technically satisfied but the underlying task outcome is wrong or incomplete
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- The output appears to meet the assertion by coincidence rather than by actually doing the work
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**When uncertain**: The burden of proof to pass is on the expectation.
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### Step 8: Read Executor Metrics and Timing
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1. If `{outputs_dir}/metrics.json` exists, read it and include in grading output
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2. If `{outputs_dir}/../timing.json` exists, read it and include timing data
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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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"expectations": [
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{
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"text": "The output includes the name 'John Smith'",
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"passed": true,
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"evidence": "Found in transcript Step 3: 'Extracted names: John Smith, Sarah Johnson'"
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},
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{
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"text": "The spreadsheet has a SUM formula in cell B10",
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"passed": false,
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"evidence": "No spreadsheet was created. The output was a text file."
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},
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{
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"text": "The assistant used the skill's OCR script",
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"passed": true,
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"evidence": "Transcript Step 2 shows: 'Tool: Bash - python ocr_script.py image.png'"
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}
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],
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"summary": {
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"passed": 2,
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"failed": 1,
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"total": 3,
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"pass_rate": 0.67
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},
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"execution_metrics": {
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"tool_calls": {
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"Read": 5,
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"Write": 2,
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"Bash": 8
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},
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"total_tool_calls": 15,
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"total_steps": 6,
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"errors_encountered": 0,
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"output_chars": 12450,
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"transcript_chars": 3200
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},
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"timing": {
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"executor_duration_seconds": 165.0,
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"grader_duration_seconds": 26.0,
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"total_duration_seconds": 191.0
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},
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"claims": [
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{
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"claim": "The form has 12 fillable fields",
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"type": "factual",
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"verified": true,
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"evidence": "Counted 12 fields in field_info.json"
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},
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{
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"claim": "All required fields were populated",
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"type": "quality",
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"verified": false,
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"evidence": "Reference section was left blank despite data being available"
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}
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],
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"user_notes_summary": {
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"uncertainties": ["Used 2023 data, may be stale"],
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"needs_review": [],
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"workarounds": ["Fell back to text overlay for non-fillable fields"]
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},
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"eval_feedback": {
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"suggestions": [
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{
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"assertion": "The output includes the name 'John Smith'",
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"reason": "A hallucinated document that mentions the name would also pass — consider checking it appears as the primary contact with matching phone and email from the input"
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},
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{
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"reason": "No assertion checks whether the extracted phone numbers match the input — I observed incorrect numbers in the output that went uncaught"
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}
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],
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"overall": "Assertions check presence but not correctness. Consider adding content verification."
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}
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}
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```
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## Field Descriptions
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- **expectations**: Array of graded expectations
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- **text**: The original expectation text
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- **passed**: Boolean - true if expectation passes
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- **evidence**: Specific quote or description supporting the verdict
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- **summary**: Aggregate statistics
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- **passed**: Count of passed expectations
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- **failed**: Count of failed expectations
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- **total**: Total expectations evaluated
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- **pass_rate**: Fraction passed (0.0 to 1.0)
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- **execution_metrics**: Copied from executor's metrics.json (if available)
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- **output_chars**: Total character count of output files (proxy for tokens)
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- **transcript_chars**: Character count of transcript
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- **timing**: Wall clock timing from timing.json (if available)
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- **executor_duration_seconds**: Time spent in executor subagent
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- **total_duration_seconds**: Total elapsed time for the run
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- **claims**: Extracted and verified claims from the output
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- **claim**: The statement being verified
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- **type**: "factual", "process", or "quality"
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- **verified**: Boolean - whether the claim holds
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- **evidence**: Supporting or contradicting evidence
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- **user_notes_summary**: Issues flagged by the executor
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- **uncertainties**: Things the executor wasn't sure about
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- **needs_review**: Items requiring human attention
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- **workarounds**: Places where the skill didn't work as expected
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- **eval_feedback**: Improvement suggestions for the evals (only when warranted)
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||||
- **suggestions**: List of concrete suggestions, each with a `reason` and optionally an `assertion` it relates to
|
||||
- **overall**: Brief assessment — can be "No suggestions, evals look solid" if nothing to flag
|
||||
|
||||
## Guidelines
|
||||
|
||||
- **Be objective**: Base verdicts on evidence, not assumptions
|
||||
- **Be specific**: Quote the exact text that supports your verdict
|
||||
- **Be thorough**: Check both transcript and output files
|
||||
- **Be consistent**: Apply the same standard to each expectation
|
||||
- **Explain failures**: Make it clear why evidence was insufficient
|
||||
- **No partial credit**: Each expectation is pass or fail, not partial
|
||||
Reference in New Issue
Block a user