foryourhealth111-pixel/generating-test-reports
bundled/skills/generating-test-reports/SKILL.md
Generate structured test reports with pass/fail rollups, coverage summaries, and test artifacts. Use when the user is asking for test-result packaging or delivery, not for root-cause debugging or feature implementation.
$ install --global
skillsauthnpx skillsauth add foryourhealth111-pixel/vco-skills-codex generating-test-reportsInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
Security Scan Results
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
3
Scanners Passed
9
Scanners in report
Clean
TrivyContainer and dependency vulnerability scanner
95%
Clean
SemgrepStatic code analysis for vulnerabilities
95%
Clean
mcp-scan (Snyk)Model Context Protocol security validation
95%
Skipped
Snyk (dep)Open source security scanning
50%
Skipped
Socket.devSupply chain security analysis
50%
Skipped
VirusTotalMulti-engine malware detection
50%
Skipped
CrowdStrikeAdvanced threat intelligence
50%
Skipped
OSV-ScannerOpen Source Vulnerability database check
50%
Skipped
OWASP Dep-Check
50%
Last scanned: Apr 26, 2026, 3:45 AM40.4s9 files scanned
SKILL.md
- name:
- generating-test-reports
- description:
- |
- allowed-tools:
- Read, Write, Edit, Grep, Glob, Bash(test:report-*)
- version:
- 1.0.0
- author:
- Jeremy Longshore <[email protected]>
- license:
- MIT
Test Report Generator
Purpose
Use this skill when the next deliverable is a readable test report rather than another round of debugging.
When to Use
Use this skill when:
- Summarizing pass/fail status, flaky tests, or coverage output for others to read
- Turning raw
pytest, JUnit, or coverage artifacts into a concise report - Producing release, QA, or review-facing test documentation
Not For / Boundaries
- Root-cause debugging of a failure: use
systematic-debugging - Code review of the underlying implementation: use
code-reviewer - Implementing a new test suite from scratch: use
tdd-guide
Typical Outputs
- Test execution summary with totals and failure clusters
- Coverage snapshot and quality-gate status
- Follow-up action list for the most important failures
Related Skills
systematic-debuggingbefore report packaging if failures are still unexplainedverification-before-completionwhen the report is part of a completion gate
Related Skills
foryourhealth111-pixel/shap
development
VerifiedTrustedCommunity
Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model.
1,760SKILL.mdUpdated Apr 27, 2026
foryourhealth111-pixel/sentry
development
VerifiedTrustedCommunity
Use when the user asks to inspect Sentry issues or events, summarize recent production errors, or pull basic Sentry health data via the Sentry API; perform read-only queries with the bundled script and require `SENTRY_AUTH_TOKEN`.
1,760SKILL.mdUpdated Apr 27, 2026
foryourhealth111-pixel/senior-prompt-engineer
development
VerifiedTrustedCommunity
World-class prompt engineering skill for LLM optimization, prompt patterns, structured outputs, and AI product development. Expertise in Claude, GPT-4, prompt design patterns, few-shot learning, chain-of-thought, and AI evaluation. Includes RAG optimization, agent design, and LLM system architecture. Use when building AI products, optimizing LLM performance, designing agentic systems, or implementing advanced prompting techniques.
1,760SKILL.mdUpdated Apr 27, 2026
foryourhealth111-pixel/senior-ml-engineer
development
VerifiedTrustedCommunity
World-class ML engineering skill for productionizing ML models, MLOps, and building scalable ML systems. Expertise in PyTorch, TensorFlow, model deployment, feature stores, model monitoring, and ML infrastructure. Includes LLM integration, fine-tuning, RAG systems, and agentic AI. Use when deploying ML models, building ML platforms, implementing MLOps, or integrating LLMs into production systems.
1,760SKILL.mdUpdated Apr 27, 2026