foryourhealth111-pixel/metric-calculator
bundled/skills/metric-calculator/SKILL.md
Compute well-defined metrics from existing formulas, datasets, or test outputs. Use as an explicit/manual helper when the metric definition is already known, not for choosing the overall analysis owner or dashboard strategy.
$ install --global
skillsauthnpx skillsauth add foryourhealth111-pixel/vco-skills-codex metric-calculatorInstall 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, 3:48 AM251.7s1 file scanned
SKILL.md
- name:
- metric-calculator
- description:
- |
- allowed-tools:
- Read, Write, Edit, Bash, Grep
- version:
- 1.0.0
- license:
- MIT
- author:
- Jeremy Longshore <[email protected]>
Metric Calculator
Positioning
Treat this skill as an explicit/manual helper for narrow metric-computation work.
When to Use
Use this skill when:
- Calculating a named business, statistical, or QA metric from available data
- Converting raw counts into rates, ratios, deltas, or scorecards
- Verifying that a metric formula is implemented consistently across outputs
Not For / Boundaries
- Model evaluation strategy selection: use
evaluating-machine-learning-models - Full regression or causal analysis ownership: use
performing-regression-analysis - Chart design or presentation decisions: use
creating-data-visualizations
Typical Outputs
- Metric definitions and formulas
- Reproducible calculation steps
- Sanity checks for units, denominators, and aggregation scope
Related Skills
evaluating-machine-learning-modelsfor ML benchmark metricscreating-data-visualizationsafter the numbers are finalized
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