decision-quality-analyzer — Claude Code command: how to install
Evaluates team decision making, flags cognitive biases, and stress-tests project scenarios directly from Git history.
npx claude-code-templates@latest --command team/decision-quality-analyzerWhat decision-quality-analyzer does
This command reviews recent commits and discussions to evaluate how a team arrives at technical choices. It inspects active contributors over the past month via git log and inspects decision frequency. You can target specific evaluations with flags like --bias-detection, --scenario-testing, --process-optimization, or --outcome-tracking.
The workflow spots common decision traps including confirmation bias, anchoring, groupthink, and planning fallacies. It relies on standard environment capabilities: Read, Write, Edit, and Bash.
Git history is required. Without a local repository, contributor counts and recent context cannot be collected. The run produces a structured assessment covering process improvements and actionable next steps.
What it helps with
- detect groupthink and anchoring bias in team discussions
- audit decision cadence from the past month of Git commits
- stress-test hypothetical scenarios before major releases
- outline structured steps for team alignment
Example prompts for the agent
- Run bias detection on recent choices: /decision-quality-analyzer --bias-detection
- Stress-test alternative scenarios: /decision-quality-analyzer --scenario-testing
- Track outcomes of prior architectural decisions: /decision-quality-analyzer --outcome-tracking
How to install decision-quality-analyzer
1claude-code-templates
npx claude-code-templates@latest --command team/decision-quality-analyzer2Manually: put the files in place
.claude/commands/decision-quality-analyzer.mdCall it with /decision-quality-analyzer. For all projects use ~/.claude/commands/
Manually: put the files in place
~/.codex/prompts/decision-quality-analyzer.mdCall it with /prompts:decision-quality-analyzer. Codex marks this format deprecated in favour of skills
Manually: put the files in place
.github/prompts/decision-quality-analyzer.prompt.mdManually: put the files in place
.gemini/commands/decision-quality-analyzer.tomlGemini stores commands as TOML: the prompt text goes into the prompt field
Questions about decision-quality-analyzer
How to install decision-quality-analyzer in Claude Code?
Run npx claude-code-templates@latest --command team/decision-quality-analyzer. This saves the configuration to .claude/commands/decision-quality-analyzer.md.
What does decision-quality-analyzer do?
It inspects team decision habits, flags cognitive patterns like confirmation bias, and suggests process improvements.
Which tools does decision-quality-analyzer need?
The command requires access to Read, Write, Edit, and Bash, alongside a working Git repository.
Source text
decision-quality-analyzer.md · MIT
--- allowed-tools: Read, Write, Edit, Bash argument-hint: "[analysis-type] | --bias-detection | --scenario-testing | --process-optimization | --outcome-tracking" description: Analyze team decision quality with bias detection, scenario testing, and process improvement recommendations --- # 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 -l` active 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**: 1. **Process Quality Assessment** - Evaluate information gathering, stakeholder involvement, alternative generation, analysis rigor 2. **Bias Detection Analysis** - Identify confirmation bias, anchoring bias, groupthink, authority bias, planning fallacy patterns 3. **Outcome Evaluation** - Assess goal achievement, unintended consequences, stakeholder satisfaction, sustainability measures 4. **Scenario Testing** - Historical decision analysis, hypothetical scenario testing, stress test scenarios, learning extraction 5. **Timing Analysis** - Decision speed evaluation, information timing optimization, implementation coordination, review scheduling 6. **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.
© davila7 · MIT · davila7/claude-code-templates