Decision Quality Analyzer
Analyze team decision quality with bias detection, scenario testing, and process improvement recommendations
npx claude-code-templates@latest --command team/decision-quality-analyzer Scanned by NVIDIA SkillSpector
Automated static analysis (no LLM) · v2.12.0 · 2026-10-23
SkillSpector looks for patterns such as shell commands, credential access, network calls or unpinned dependencies. An observation is something worth reading in the files before you install, not a confirmed vulnerability. Components that run code locally, like hooks, MCP servers and security tooling, naturally show more of them.
No observations.
Learn more about NVIDIA SkillSpector.
Content
Decision Quality Analyzer
Analyze and improve team decision-making quality with comprehensive bias detection: $ARGUMENTS
Current Decision Context
- Team size: !
git log --format='%ae' --since='1 month ago' | sort -u | wc -lactive contributors - Recent decisions: Major decisions from recent commits and discussions
- Decision frequency: Pattern analysis of decision-making cadence
- Process maturity: Current decision frameworks and methodologies in use
Task
Execute comprehensive decision quality analysis with bias mitigation and process optimization:
Analysis Type: Use $ARGUMENTS for bias detection, scenario testing, process optimization, or outcome tracking analysis
Decision Quality Framework:
- Process Quality Assessment - Evaluate information gathering, stakeholder involvement, alternative generation, analysis rigor
- Bias Detection Analysis - Identify confirmation bias, anchoring bias, groupthink, authority bias, planning fallacy patterns
- Outcome Evaluation - Assess goal achievement, unintended consequences, stakeholder satisfaction, sustainability measures
- Scenario Testing - Historical decision analysis, hypothetical scenario testing, stress test scenarios, learning extraction
- Timing Analysis - Decision speed evaluation, information timing optimization, implementation coordination, review scheduling
- Learning Integration - Knowledge capture, institutional learning, process improvement, capability building
Advanced Features: Multi-dimensional quality metrics, systematic bias mitigation strategies, decision simulation testing, predictive outcome modeling.
Process Optimization: Stakeholder engagement frameworks, analytical tool integration, communication enhancement, cultural development strategies.
Output: Comprehensive decision quality assessment with specific bias mitigation strategies, process improvements, and implementation roadmap.