infrastructure-reporting
Skill for the reporting infrastructure module providing pipeline reporting, error aggregation, executive summaries, dashboard generation, test reporting, and multi-project reports. Use when generating build reports, aggregating errors, creating visual dashboards, or producing executive summaries across projects.
Best use case
infrastructure-reporting is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Skill for the reporting infrastructure module providing pipeline reporting, error aggregation, executive summaries, dashboard generation, test reporting, and multi-project reports. Use when generating build reports, aggregating errors, creating visual dashboards, or producing executive summaries across projects.
Teams using infrastructure-reporting should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/reporting/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How infrastructure-reporting Compares
| Feature / Agent | infrastructure-reporting | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Skill for the reporting infrastructure module providing pipeline reporting, error aggregation, executive summaries, dashboard generation, test reporting, and multi-project reports. Use when generating build reports, aggregating errors, creating visual dashboards, or producing executive summaries across projects.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Reporting Module
Pipeline reporting, error aggregation, and executive dashboard generation.
## Pipeline Reports (`pipeline_report_model.py`, `pipeline_io.py`)
```python
from infrastructure.reporting import (
generate_pipeline_report, save_pipeline_report,
save_validation_report, save_test_results,
save_performance_report, save_error_summary,
)
from infrastructure.reporting.report_generator import generate_test_report
# Generate comprehensive pipeline report
report = generate_pipeline_report(
test_results=test_data,
validation_results=validation_data,
performance_metrics=perf_data,
errors=error_list,
)
# Save report to file
save_pipeline_report(report, output_dir)
# Individual report sections
test_report = generate_test_report(test_data)
saved_files = save_validation_report(validation_data, output_dir)
```
## Error Aggregation (`error_aggregator.py`)
```python
from infrastructure.reporting import (
ErrorAggregator, ErrorEntry,
get_error_aggregator, reset_error_aggregator,
)
# Singleton aggregator
aggregator = get_error_aggregator()
aggregator.add(ErrorEntry(stage="rendering", message="Missing figure"))
aggregator.add(ErrorEntry(stage="validation", message="Broken link"))
# Get summary
summary = aggregator.summarize()
reset_error_aggregator()
```
## Executive Summaries (`executive_reporter.py`)
```python
from infrastructure.reporting import (
generate_executive_summary, save_executive_summary,
collect_project_metrics, ProjectMetrics, ExecutiveSummary,
)
# Generate cross-project executive summary
summary = generate_executive_summary(repo_root, project_names)
files = save_executive_summary(summary, output_dir)
# Collect metrics for a single project
metrics = collect_project_metrics(project_path)
```
## Dashboard Generation (`_dashboard_matplotlib.py`)
```python
from infrastructure.reporting import (
generate_all_dashboards,
generate_matplotlib_dashboard,
generate_plotly_dashboard,
)
# Generate all dashboard formats (PNG, PDF, HTML)
files = generate_all_dashboards(executive_summary, output_dir)
# Individual dashboard types
generate_matplotlib_dashboard(summary, output_dir)
generate_plotly_dashboard(summary, output_dir)
```
## Test Reporting (`pytest_output_parser.py`, `report_generator.py`)
```python
from infrastructure.reporting.pytest_output_parser import parse_pytest_output
from infrastructure.reporting.report_generator import save_test_report_to_files
# Parse pytest output into structured data
results = parse_pytest_output(pytest_output_text, stderr="", exit_code=0)
save_test_report_to_files(results, output_path)
```
## Output Reporting (`output_statistics.py`)
```python
from infrastructure.reporting import (
collect_output_statistics,
log_output_summary,
write_output_statistics_reports,
)
stats = collect_output_statistics(output_dir)
log_output_summary(stats)
write_output_statistics_reports(output_dir, stats)
```
## Multi-Project Reports
```python
from infrastructure.reporting import generate_multi_project_report
# Full workflow: executive summary + dashboards + CSV exports
files = generate_multi_project_report(
repo_root=Path("."),
project_names=["project_a", "project_b"],
output_dir=Path("output/executive_summary"),
)
```
## Test Summary Generation (`markdown_formatter.py`)
```python
from infrastructure.reporting.markdown_formatter import run_test_summary_generation
run_test_summary_generation()
```
## Manuscript Overview (`manuscript_overview.py`)
```python
from infrastructure.reporting.manuscript_overview import generate_manuscript_overview
overview = generate_manuscript_overview(project_path)
```
## Coverage Parsing (`coverage_parser.py`)
```python
from infrastructure.reporting.coverage_parser import extract_coverage_percentage
passed, pct = extract_coverage_percentage(stdout_text, coverage_json_paths)
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