Batch Report Skill

Generate comprehensive analysis reports for completed batch processing jobs.

Features

• Manifest analysis - Count successes, failures, partial completions
• Timing breakdown - Per-step latency analysis, identify bottlenecks
• Failure patterns - Categorize and summarize failure modes
• Quality metrics - Sample outputs for quality assessment
• Markdown report - Human-readable summary
• Agent-inbox integration - Auto-send to project inbox

Quick Start

cd .pi/skills/batch-report

# Generate report for extractor batch (auto-detects format)
uv run python report.py analyze /path/to/batch/output

# Generate and send to agent-inbox
uv run python report.py analyze /path/to/batch/output --send-to extractor

# Just show summary stats
uv run python report.py summary /path/to/batch/output

# Analyze a standalone state file
uv run python report.py state /path/to/.batch_state.json

# JSON output for piping
uv run python report.py summary /path/to/output --json | jq .success_rate

Commands

analyze

- Full analysis report

uv run python report.py analyze /path/to/output \
    --output report.md \
    --send-to extractor \
    --priority high

Options:

--output

-o

--send-to

-s

--priority

-p

--sample

-n

--format

-f

extractor

youtube

generic

auto

--json

-j

summary

- Quick stats only

uv run python report.py summary /path/to/output
uv run python report.py summary /path/to/output --json

Output:

Batch: run-2025-12-18_144426-2eb428c
Total: 230 | Success: 180 | Failed: 35 | Partial: 15
Success rate: 78.3%
Avg time: 4.2 min | Slowest: 09_section_summarizer (45%)

JSON Output:

{
  "batch": "run-2025-12-18_144426-2eb428c",
  "format": "extractor",
  "total": 230,
  "successful": 180,
  "partial": 15,
  "failed": 35,
  "success_rate": 78.3,
  "avg_time_min": 4.2
}

state

- Analyze standalone state files

uv run python report.py state /path/to/.batch_state.json
uv run python report.py state /path/to/.batch_state.json --json

Works with any

.batch_state.json

failures

- List failures with reasons

uv run python report.py failures /path/to/output
uv run python report.py failures /path/to/output --json

Report Format

# Batch Report: run-2025-12-18_144426-2eb428c

## Summary
- **Total items:** 230
- **Successful:** 180 (78.3%)
- **Failed:** 35 (15.2%)
- **Partial:** 15 (6.5%)

## Timing Analysis
| Step | Avg (s) | Max (s) | % of Total |
|------|---------|---------|------------|
| 09_section_summarizer | 120.5 | 341.0 | 45.2% |
| 05_table_extractor | 65.3 | 105.0 | 24.5% |
...

## Failure Patterns
| Pattern | Count | Example |
|---------|-------|---------|
| Empty text_content | 12 | 047ca6ef... |
| CUDA OOM | 5 | 9497a4e5... |
...

## Recommendations
1. Consider --text-only mode for knowledge extraction
2. Add table confidence threshold before VLM
...

Supported Batch Formats

The

--format

extractor

youtube

generic

auto

Auto-detect logic:

1. If

   */manifest.json

   and

   */timings_summary.json

   exist →

   extractor

2. If

   .batch_state.json

   has "transcript" in description →

   youtube

3. If

   .batch_state.json

   exists →

   generic

Extractor batches

Expects:

• */manifest.json

  - Per-item manifests

• */timings_summary.json

  - Timing data

• */14_report_generator/json_output/final_report.json

  - Quality metrics

• failed_urls.txt

  - Failed items list

YouTube transcript batches

Expects:

• .batch_state.json

  - State file with transcript-related description

Generic batches

Expects:

• .batch_state.json

  - State file with completed/failed counts

• *.log

  files for failure analysis (optional)

Integration with agent-inbox

# Send report as bug
uv run python report.py analyze /path/to/output \
    --send-to extractor \
    --priority high

# Message sent: extractor_abc123

Dependencies

dependencies = [
    "typer",
    "rich",
]