batch-report
Generate post-run analysis reports for batch processing jobs. Analyzes manifests, timings, and failures to produce comprehensive markdown reports. Optionally sends to agent-inbox for cross-project communication.
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- 2026년 9월 24일 15:51
- 감지된 SKILL.md 언어
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소스 지침 · 읽기 전용 미리보기- name
- batch-report
- description
- Generate post-run analysis reports for batch processing jobs. Analyzes manifests, timings, and failures to produce comprehensive markdown reports. Optionally sends to agent-inbox for cross-project communication.
- allowed-tools
- Bash, Read, Write
- triggers
- ["batch report","generate report","analyze batch","summarize extraction"]
- metadata
- {"short-description":"Post-run batch analysis and reporting"}
- provides
- ["batch-report"]
- composes
- ["create-figure","task-monitor","agentic-evals"]
- disciplines
- ["observability-operations","data-engineering"]
# 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
```bash
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
```bash
uv run python report.py analyze /path/to/output \
--output report.md \
--send-to extractor \
--priority high
```
**Options:**
| Option | Short | Description |
|--------|-------|-------------|
| `--output` | `-o` | Output file path (default: stdout) |
| `--send-to` | `-s` | Send report to agent-inbox project |
| `--priority` | `-p` | Priority for agent-inbox (low/normal/high/critical) |
| `--sample` | `-n` | Number of samples to include (default: 5) |
| `--format` | `-f` | Batch format: `extractor`, `youtube`, `generic`, `auto` (default: auto) |
| `--json` | `-j` | Output as JSON for piping to other tools |
### `summary` - Quick stats only
```bash
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:
```json
{
"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
```bash
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` file from any batch job.
### `failures` - List failures with reasons
```bash
uv run python report.py failures /path/to/output
uv run python report.py failures /path/to/output --json
```
## Report Format
```markdown
# 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
...
```
## Visualization
After generating reports (especially with `--json`), offer to visualize via `/create-figure`:
```bash
# Timing waterfall by pipeline step
create-figure metrics --input batch.json --output timing.png --type hbar --title "Step Timing"
# Success/failure distribution
create-figure metrics --input batch.json --output results.png --type pie --title "Batch Results"
# Failure pattern breakdown
create-figure metrics --input batch.json --output failures.png --type bar --title "Failure Patterns"
```
**When to offer:** After presenting batch analysis, ask: "Want me to visualize the timing breakdown?"
## Supported Batch Formats
The `--format` flag accepts: `extractor`, `youtube`, `generic`, or `auto` (default).
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
```bash
# Send report as bug
uv run python report.py analyze /path/to/output \
--send-to extractor \
--priority high
# Message sent: extractor_abc123
```
## Dependencies
```toml
dependencies = [
"typer",
"rich",
]
```
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