Practical examples of how I investigate production issues, communicate during incidents, and turn recurring support problems into reusable tooling and documentation.
I am an Enterprise Support Engineer at GitHub, where I work with enterprise customers and collaborate daily with engineering and product teams on complex technical issues involving CI/CD, infrastructure, security, and developer workflows.
This is a personal portfolio. It is not an official GitHub project and does not contain GitHub source code, customer information, internal documentation, ticket data, or confidential operational details. All examples and data are synthetic.
| If you are evaluating... | Review |
|---|---|
| Python and support automation | Incident Triage CLI and Engineering Escalation Checker |
| Incident ownership and communication | Incident response playbook and sample postmortem |
| Applied support-engineering judgment | Synthetic case-study patterns |
| Support-system design | Support operations architecture |
| Escalation effectiveness | Escalation effectiveness scorecard |
| GitHub platform expertise | Actions, GHES, and Advanced Security guidance |
| Standalone public projects | Workflow Auditor and GHES Bundle Triage |
| Project breadth | Capabilities demonstrated |
Requires Python 3.10 or later and has no runtime dependencies.
git clone https://github.com/jjoseph456/support-engineering-portfolio.git
cd support-engineering-portfolio
python -m pip install -e .
support-triage examples/incidents.json
escalation-check examples/escalation.jsonsupport-triage is a dependency-free Python command-line tool that turns a
small batch of synthetic service signals into a prioritized investigation
queue.
It demonstrates a support-engineering workflow:
- Validate the incoming diagnostic data.
- Classify severity consistently.
- Identify a likely failure category without presenting it as a confirmed root cause.
- Recommend the next diagnostic action.
- Produce human-readable or machine-readable output.
- Return a meaningful exit code for CI and automation.
python -m support_triage examples/incidents.jsonMachine-readable output:
python -m support_triage examples/incidents.json --jsonFail an automation step when a high- or critical-severity incident is present:
python -m support_triage examples/incidents.json --fail-on highSEVERITY INCIDENT SERVICE LIKELY CATEGORY NEXT ACTION
CRITICAL INC-1042 agent-runtime resource exhaustion Inspect container termination reason and memory limits.
HIGH INC-1041 evaluation-api upstream dependency Compare upstream provider errors and request correlation IDs.
MEDIUM INC-1040 trace-ingest rate limiting Inspect rate-limit headers and client backoff behavior.
LOW INC-1039 control-plane no clear fault Continue monitoring and compare against the service baseline.
escalation-check reviews a synthetic escalation package before it reaches an
engineering team. It verifies that the package includes a clear impact
statement, reproduction steps, timestamped evidence, explicitly labeled
hypotheses, a focused engineering question, workaround status, and a complete
customer update.
python -m escalation_check examples/escalation.jsonMachine-readable output and an automation gate:
python -m escalation_check examples/escalation.json --json
python -m escalation_check examples/escalation.json --minimum-score 90The tool treats observations, hypotheses, and confirmed root cause as different types of information. That distinction reduces premature conclusions and gives engineering a cleaner starting point.
docs/incident-response-playbook.md- a concise process for triage, escalation, communication, and closure.docs/sample-postmortem.md- a blameless postmortem built from a fictional container-memory incident.docs/engineering-ready-escalations.md- a technical article on converting an ambiguous report into an actionable engineering escalation.docs/closure-is-not-customer-recovery.md- why an engineering close, an inactivity close, and a verified customer outcome must be recorded separately, with synthetic examples.docs/synthetic-case-study-patterns.md- fictional, privacy-safe examples of evidence-led diagnosis, escalation ownership, and durable operational improvements.docs/support-operations-architecture.md- a visual map of how the public tools support intake, investigation, escalation, customer continuity, and knowledge maintenance.docs/escalation-scorecard.md- a privacy-safe framework for measuring escalation quality, update cadence, and reusable learning.
- Secure and Reliable GitHub Actions Workflows
- GitHub Enterprise Server Operational Readiness
- GitHub Advanced Security Rollout and Operations
- Python data modeling, validation, CLI design, and JSON serialization
- Unit tests covering severity, diagnostic categorization, validation, and command exit behavior
- GitHub Actions continuous integration
- Clear separation between observed evidence, working hypotheses, and confirmed root cause
- Actionable incident communication and escalation criteria
- Reusable documentation designed to reduce repeat support effort
These projects show how I approach common support-engineering problems:
| Area | Example output | Intended outcome |
|---|---|---|
| Incident and escalation process review | Triage rules, escalation-quality checks, and runbooks | Faster handoffs and fewer incomplete engineering escalations |
| GitHub Actions workflow health check | Security and reliability findings with prioritized remediation | Reduced workflow risk and more predictable CI/CD |
| GHES operational-readiness review | Readiness checklist and operational recommendations | Clearer upgrade, backup, monitoring, and incident preparation |
| Support automation prototype | Tested Python CLI with machine-readable output and exit codes | Repeatable triage and less manual support work |
| Technical documentation sprint | Troubleshooting guides, postmortems, and knowledge articles | Better case deflection and reusable operational knowledge |
The repository demonstrates the method and output quality without using employer, customer, or support-case material.
github-actions-workflow-auditor
is a Python CLI for repeatable workflow security and reliability reviews.
It provides:
- Ten focused checks covering permissions, action pinning, shell interpolation, privileged pull-request patterns, timeouts, concurrency, OIDC boundaries, and reusable-workflow secret handling
- Human-readable and JSON reports
- Configurable CI failure thresholds
- Secure and intentionally insecure synthetic workflow examples
- Eleven unit tests, pinned CI dependencies, and an MIT license
gh-bundle is a read-only gh CLI
extension for an initial review of GitHub Enterprise Server support bundles.
It demonstrates:
- Selective extraction of diagnostic files instead of expanding an entire multi-gigabyte archive
- Topology-aware handling of single-node and multi-node bundles
- Disk, memory, out-of-memory, service-health, and connectivity signals
- Defensive handling of missing or malformed diagnostic files
- Human-readable and JSON output with automation-friendly exit codes
The tool is designed as an initial screen that points to source evidence, not as a substitute for complete diagnosis.
Install the package in editable mode and run the test suite:
python -m pip install -e .
python -m unittest discover -s tests -v.
|-- .github/workflows/test.yml
|-- pyproject.toml
|-- SECURITY.md
|-- best-practices/
| |-- README.md
| |-- ghes-operational-readiness.md
| |-- github-actions-secure-reliable-workflows.md
| `-- github-advanced-security-rollout.md
|-- docs/
| |-- closure-is-not-customer-recovery.md
| |-- engineering-ready-escalations.md
| |-- incident-response-playbook.md
| |-- sample-postmortem.md
| |-- support-operations-architecture.md
| `-- synthetic-case-study-patterns.md
|-- escalation_check/
| |-- __init__.py
| |-- __main__.py
| `-- checker.py
|-- examples/
| |-- escalation.json
| `-- incidents.json
|-- support_triage/
| |-- __init__.py
| |-- __main__.py
| `-- triage.py
`-- tests/
|-- test_escalation_check.py
`-- test_triage.py
- LinkedIn: Joseph P. Joseph