PDF → Markdown Validator Skill

Validate PDF to Markdown conversion quality using a comprehensive, multi-dimensional evaluation framework. This skill provides standardized metrics, evaluation harnesses, and reporting tools to assess conversion fidelity across table accuracy, style preservation, robustness, and performance.

When to use

Use this skill when you need to:

• Validate PDF extraction quality: Measure how accurately PDFs convert to Markdown
• Compare conversion approaches: Benchmark different PDF processing implementations
• Track quality improvements: Quantify gains from processing enhancements
• Automate quality gates: Enforce minimum quality standards in CI/CD pipelines
• Generate evaluation reports: Create detailed analysis of conversion successes and failures
• Identify failure patterns: Discover systematic issues in specific PDF features
• Measure performance regressions: Track processing speed alongside quality metrics

Core concepts

Validation Framework

The validation framework computes a composite quality score (0–100) combining four independent metric dimensions:

FinalScore = (0.40 × TableAccuracy) + (0.40 × StyleAccuracy)
           + (0.10 × Robustness) + (0.10 × Performance)

Each dimension is independent and can be evaluated separately or together.

1. Table Accuracy (40% weight)

Measures how accurately tables are detected and their cell content extracted.

Components:

• Table Detection F1: IoU-based matching of predicted vs. gold tables (IoU ≥ 0.5 threshold)

  • Precision: correctly identified tables / all detected tables
  • Recall: correctly identified tables / all gold tables
  • F1: harmonic mean of precision and recall

• Cell Content Accuracy: Token-level F1 averaging across matched table cells

  • Matches cells by position within detected tables
  • Unmatched cells score 0.0
  • Aggregates to mean F1 across all cells

Formula:

TableAccuracy = (0.5 × TableDetectionF1) + (0.5 × CellContentAccuracy)

Interpretation:

• 90–100: Excellent table handling; tables detected and content extracted accurately
• 70–89: Good; minor detection misses or cell content variations
• 50–69: Fair; tables detected but cells have significant content errors
• Below 50: Poor; systematic table detection failures or content corruption

2. Style Accuracy (40% weight)

Measures how accurately text formatting (bold, italic, heading levels) is preserved.

Components:

• Bold F1: Token-level F1 for bold detection
• Italic F1: Token-level F1 for italic detection
• Heading F1: Heading-level accuracy (prediction must match gold level exactly; e.g., H2 ≠ H3)

Formula:

StyleAccuracy = macro_average(BoldF1, ItalicF1, HeadingF1)
              = (BoldF1 + ItalicF1 + HeadingF1) / 3

Interpretation:

• 90–100: Styles consistently preserved; formatting markup matches gold
• 70–89: Good; minor style detection misses
• 50–69: Fair; mixed style accuracy; some styles missed consistently
• Below 50: Poor; styles largely lost or misdocumented

3. Robustness (10% weight)

Measures system stability and validity across a test corpus, particularly edge cases.

Components:

• Crash-free rate: Percent of documents processed without panics or non-zero exit codes
• Markdown validity: Percent of generated Markdown files passing pandoc syntax validation
• Completeness: Percent of documents producing non-empty output

Formula:

Robustness = (CrashFreeRate + MarkdownValidityRate + CompletenessRate) / 3

Interpretation:

• 95–100: Production-ready; system handles edge cases gracefully
• 85–94: Stable; occasional edge-case issues but recovers
• 70–84: Moderate stability; known failure patterns on specific input types
• Below 70: Fragile; systematic crashes or invalid output on subset of inputs

4. Performance (10% weight)

Measures processing speed relative to a baseline; targets 1-page PDF ≈ 200–500ms.

Components:

• Median latency: P50 processing time across corpus
• P95 latency: 95th percentile processing time (captures tail behavior)

Formula:

Performance = 0.5 × min(1.0, baseline_median / run_median)
            + 0.5 × min(1.0, baseline_p95 / run_p95)

Interpretation:

• 95–100: Fast processing; meets or exceeds baseline
• 80–94: Good; acceptable slowdown (≤20%)
• 60–79: Acceptable; noticeable overhead (20–60%)
• Below 60: Slow; significant performance regression

Quick start

1. Prepare ground-truth annotations

Create .gold.md files for each test PDF:

# Copy reference Markdown next to PDF with .gold.md extension
cp reference_output.md test.gold.md

# Format: Each section annotated with metadata
# Gold format example:
# # Heading 1
# **bold text** and *italic text*
#
# | Column A | Column B |
# |----------|---------|
# | cell 1   | cell 2  |

2. Run validation

# Evaluate against ground truth
cargo run -p edgequake-pdf --example real_dataset_eval -- \
  --input crates/edgequake-pdf/test-data/real_dataset \
  --gold \
  --metrics

# Generate detailed report
python3 .github/skills/pdf-markdown-validator/scripts/validate.py \
  --pdf-dir crates/edgequake-pdf/test-data/real_dataset \
  --gold-dir . \
  --output-report metrics_report.json

3. Interpret results

# View summary scores
cat metrics_report.json | jq '.summary'

# Analyze failures by category
python3 .github/skills/pdf-markdown-validator/scripts/analyze_failures.py \
  metrics_report.json

Capabilities

Core Capabilities

1. Metric Computation

• Token-level F1 calculation: Boolean/classification metrics at granular text unit level
• Spatial matching: IoU-based table detection using bounding box coordinates
• Aggregate averaging: Macro-averaging (label-balanced) and weighted averaging strategies
• Normalization: Robust handling of missing annotations and edge cases

2. Evaluation Harness

• Batch evaluation: Process entire corpora in single invocation
• Incremental scoring: Support partial annotations and progressive refinement
• Performance profiling: Automatically capture timing with statistical summaries (median, P95)
• Error categorization: Classify failures into systematic buckets (missed tables, style loss, etc.)

3. Reporting & Visualization

• JSON reports: Machine-readable output for CI/CD integration
• Markdown summaries: Human-friendly reports with trend analysis
• Per-document breakdown: Detailed scores for each file with failure reasons
• Comparison mode: Side-by-side analysis of baseline vs. current runs

4. Validation Gates

• Hard gates: Fail evaluation if Markdown is invalid or crashes occur
• Soft gates: Warn if quality drops below threshold but allow pass
• Trend analysis: Flag regressions even if absolute score passes
• Baseline anchoring: Detect performance slowdowns vs. previous runs

Integration Points

With Cargo Testing

# Embed metrics in standard cargo test output
cargo test -p edgequake-pdf -- --nocapture

# Fail CI if composite score below threshold
cargo test -p edgequake-pdf --features ci-strict

With Python Evaluation Scripts

from pdf_validator import PDFValidator

validator = PDFValidator(
    pdf_dir="test-data/real_dataset",
    gold_dir="test-data/gold",
    metrics=["table", "style", "robustness", "performance"]
)
score = validator.evaluate()
print(f"Composite Score: {score.composite}/100")

With CI/CD Pipelines

# GitHub Actions example
- name: Validate PDF → Markdown
  run: |
    cargo run -p edgequake-pdf --example real_dataset_eval -- --metrics
    python .github/skills/pdf-markdown-validator/scripts/validate.py \
      --ci-mode --fail-below 75

Metric Definitions & Examples

Table Accuracy Example

Input PDF: 2×3 table with headers "Name, Age" and row "John, 25"

Gold Markdown:

| Name | Age |
| ---- | --- |
| John | 25  |

Generated Markdown (Perfect):

| Name | Age |
| ---- | --- |
| John | 25  |

Scores:

• TableDetectionF1: 1.0 (table detected correctly)
• CellContentAccuracy: 1.0 (all cells match token-for-token)
• TableAccuracy: 1.0

Generated Markdown (Partial Match):

| Name | Age  |
| ---- | ---- |
| John | 25.0 |

Scores:

• TableDetectionF1: 1.0 (table structure preserved)
• CellContentAccuracy: 0.8 (minor token variations: "25" vs "25.0")
• TableAccuracy: 0.9

Style Accuracy Example

Gold Markdown:

# Main Heading

This is **bold** and _italic_ text.

## Sub Heading

More content here.

Generated Markdown (Perfect):

# Main Heading

This is **bold** and _italic_ text.

## Sub Heading

More content here.

Scores:

• BoldF1: 1.0
• ItalicF1: 1.0
• HeadingF1: 1.0
• StyleAccuracy: 1.0

Generated Markdown (Partial):

# Main Heading

This is bold and italic text.

## Sub Heading

More content here.

Scores:

• BoldF1: 0.5 (missed "bold" marker)
• ItalicF1: 0.5 (missed "italic" marker)
• HeadingF1: 1.0 (headings correct)
• StyleAccuracy: 0.67

Robustness Example

Test corpus: 30 PDFs (including 5 edge cases: corrupted, multilingual, scanned, etc.)

Results:

• Crash-free rate: 29/30 = 96.7% (1 crash on scanned PDF)
• Markdown validity: 30/30 = 100% (all outputs valid)
• Completeness: 28/30 = 93.3% (2 near-empty outputs from obfuscated fonts)

Robustness Score: (96.7 + 100 + 93.3) / 3 = 96.7%

Performance Example

Baseline (previous release):

• Median: 250ms per 1-page PDF
• P95: 800ms per 1-page PDF

Current run:

• Median: 280ms per 1-page PDF
• P95: 750ms per 1-page PDF

Scores:

• Median: min(1.0, 250/280) = 0.89
• P95: min(1.0, 800/750) = 1.0
• Performance: 0.5 × 0.89 + 0.5 × 1.0 = 0.95

Workflow: Running a Full Validation

Step 1: Prepare Test Data

# Navigate to PDF crate
cd edgequake/crates/edgequake-pdf

# Ensure ground-truth annotations exist
# Files should be named: <pdf_name>.gold.md
ls -1 test-data/real_dataset/*.gold.md

Step 2: Generate Markdown Output

# Convert all PDFs to Markdown
cargo run -p edgequake-pdf --example real_dataset_eval -- --write

# Outputs written to: test-data/real_dataset/*.md

Step 3: Run Validation

# Compute all metrics
python3 ../../.github/skills/pdf-markdown-validator/scripts/validate.py \
  --pdf-dir test-data/real_dataset \
  --gold-dir test-data/real_dataset \
  --output-report validation_report.json \
  --verbose

Step 4: Analyze Results

# View summary
cat validation_report.json | jq '.summary'

# Detailed per-document breakdown
cat validation_report.json | jq '.documents | .[] | {name, scores}'

# Identify failure patterns
python3 ../../.github/skills/pdf-markdown-validator/scripts/analyze_failures.py \
  validation_report.json --group-by failure_type

Step 5: Iterative Improvement

1. Identify low-scoring dimensions (table, style, etc.)
2. Focus on highest-impact directory (processor, renderer, layout)
3. Implement targeted fix with tests
4. Re-run validation to measure improvement
5. Record baseline for next comparison

Ground-Truth Annotation Format

The .gold.md files serve as reference implementations. Use this structure:

# Document Title (H1)

## Section Heading (H2)

This paragraph contains **bold text** and _italic text_ and **_bold-italic text_**.

### Subsection (H3)

#### Sub-subsection (H4)

**Note:** Use standard Markdown syntax. Be precise with:

- Bold: **text**
- Italic: _text_
- Bold-Italic: **_text_**
- Headings: # through #### for H1–H4

### Tables

| Column 1 | Column 2 | Column 3 |
| -------- | -------- | -------- |
| Cell 1   | Cell 2   | Cell 3   |
| Cell 4   | Cell 5   | Cell 6   |

Ensure:

- Pipes align properly
- Headers separated by `---|---` row
- No trailing spaces (can affect parsing)

### Code Blocks

\`\`\`python
def hello():
print("world")
\`\`\`

Use triple backticks with language identifier.

### Lists

Bullet list:

- Item 1
- Item 2
  - Nested item
- Item 3

Numbered list:

1. First
2. Second
3. Third

### Edge Cases

- **Multi-line table cells**: Not standard Markdown; flatten to single line
- **Merged cells**: Not representable in Markdown tables; split into separate rows
- **Vertical headers**: Use first row convention (all cells with **bold**)

CLI Commands & Scripts

Validation Runner

# Full validation pipeline
python3 scripts/validate.py \
  --pdf-dir <path/to/pdfs> \
  --gold-dir <path/to/gold> \
  [--output-report <report.json>] \
  [--metrics table,style,robustness,performance] \
  [--ci-mode] \
  [--fail-below 75]

Options:

• --pdf-dir: Directory containing PDFs and generated .md files
• --gold-dir: Directory containing .gold.md reference files
• --output-report: JSON file for machine-readable results (default: validation_report.json)
• --metrics: Comma-separated metrics to compute (default: all)
• --ci-mode: Fail with non-zero exit code if score below threshold
• --fail-below: Minimum acceptable score (default: 75)

Failure Analysis

# Identify and categorize failures
python3 scripts/analyze_failures.py \
  <report.json> \
  [--group-by failure_type|document|metric] \
  [--export <output.csv>]

Comparison Tool

# Compare two validation runs
python3 scripts/compare_runs.py \
  <baseline_report.json> \
  <current_report.json> \
  [--show-improvements] \
  [--show-regressions]

Integration with CI/CD

GitHub Actions Example

name: PDF Validation

on: [push, pull_request]

jobs:
  validate:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3

      - name: Install dependencies
        run: |
          sudo apt-get install -y pandoc
          pip install -r .github/skills/pdf-markdown-validator/requirements.txt

      - name: Generate Markdown
        run: |
          cd edgequake/crates/edgequake-pdf
          cargo run --example real_dataset_eval -- --write

      - name: Validate conversion
        run: |
          python3 .github/skills/pdf-markdown-validator/scripts/validate.py \
            --pdf-dir edgequake/crates/edgequake-pdf/test-data/real_dataset \
            --gold-dir edgequake/crates/edgequake-pdf/test-data/real_dataset \
            --ci-mode \
            --fail-below 75

      - name: Upload report
        if: always()
        uses: actions/upload-artifact@v3
        with:
          name: validation_report
          path: validation_report.json

Extending the Validator

Adding Custom Metrics

# In your evaluation extension
from pdf_validator import BaseMetric

class CustomMetric(BaseMetric):
    def compute(self, gold_md: str, generated_md: str) -> float:
        """Implement your metric logic."""
        # Return float in [0, 1]
        pass

# Register with validator
validator.register_metric("custom_metric", CustomMetric(), weight=0.1)

Implementing Custom Scoring Functions

# Use different aggregation strategy
def weighted_macro_f1(f1_scores: dict) -> float:
    weights = {"bold": 0.4, "italic": 0.3, "heading": 0.3}
    return sum(f1_scores[k] * weights[k] for k in f1_scores)

validator.set_style_aggregator(weighted_macro_f1)

Troubleshooting

Issue: "Markdown validation failed"

Symptom: Many documents fail pandoc validation.

Diagnosis:

# Run pandoc on output directly
pandoc -f markdown -t html -o /dev/null generated.md

Common causes:

• Unmatched backticks in code blocks
• Invalid table syntax (missing pipes, misaligned columns)
• Improper escaping of special characters

Solution: Check the .md file for syntax errors and regenerate if needed.

Issue: "Cell content accuracy very low"

Symptom: TableAccuracy < 50% despite detecting tables.

Diagnosis: Table cells extracted but content is corrupted or misaligned.

Solution:

1. Check cell boundary detection in edgequake-pdf/src/processors/
2. Verify token normalization (whitespace, special chars)
3. Compare gold vs. generated side-by-side in a diff tool

Issue: "Performance regressed significantly"

Symptom: Performance score < 60%.

Diagnosis: Processing time increased 2-3x over baseline.

Steps:

1. Profile with cargo flamegraph -p edgequake-pdf
2. Check for newly added allocations or loops
3. Review recent changes to layout or renderer logic

Issue: "Robustness failures on specific PDFs"

Symptom: Crashes only on PDFs in edge_cases/ subdirectory.

Diagnosis: Parser/processor doesn't handle malformed or unusual PDFs.

Solution:

1. Isolate failing PDF
2. Add regression test for that case
3. Fix parser to handle edge case gracefully (or document limitation)

Best Practices

For Agents Using This Skill

1. Run validation after every significant change: Don't let regressions accumulate
2. Use version-pinned baseline: Document which version is the baseline for comparisons
3. Combine metrics holistically: Don't optimize one dimension at expense of others
4. Test edge cases explicitly: Include corrupted, multilingual, scanned PDFs in test set
5. Monitor trend, not just score: Small regressions compound over time

For Refining Annotations

1. Be consistent: Use identical formatting across all .gold.md files
2. Document special cases: Note why a PDF is difficult (e.g., "scanned, requires OCR")
3. Update baselines carefully: When improving gold annotations, remeasure all documents
4. Version control artifacts: Keep both .pdf and .gold.md in git

For CI/CD Integration

1. Set realistic thresholds: Don't enforce 100%; account for inherent PDF variation
2. Alert on regressions, not absolutes: Flag when score drops, even if still passing
3. Separate blocking vs. warning gates: Report comprehensive metrics but gate on critical ones
4. Archive reports: Keep historical results for trend analysis

References & Further Reading

• EdgeQuake PDF Crate: edgequake/crates/edgequake-pdf/README.md
• Improvement Guide: specs/27-improve-pdf.md (OODA loop methodology)
• Evaluation Harness: edgequake/crates/edgequake-pdf/examples/real_dataset_eval.rs
• Test Data: edgequake/crates/edgequake-pdf/test-data/real_dataset/

Summary

This skill enables systematic, quantified validation of PDF → Markdown conversions. Use it to:

• Measure quality with standardized metrics
• Track improvements objectively
• Automate gates in CI/CD
• Compare approaches fairly
• Guide optimization efforts

Start with the Quick Start section, prepare ground-truth annotations, and run the validation pipeline. Iterate using the Workflow guide, focusing on lowest-scoring dimensions first.