managing-dei-metrics
Structures diversity, equity, and inclusion data collection with benchmarking and disclosure requirements. Use when analyzing DEI metrics, benchmarking diversity, or preparing DEI disclosures.
Best use case
managing-dei-metrics is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Structures diversity, equity, and inclusion data collection with benchmarking and disclosure requirements. Use when analyzing DEI metrics, benchmarking diversity, or preparing DEI disclosures.
Teams using managing-dei-metrics should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/managing-dei-metrics/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How managing-dei-metrics Compares
| Feature / Agent | managing-dei-metrics | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Structures diversity, equity, and inclusion data collection with benchmarking and disclosure requirements. Use when analyzing DEI metrics, benchmarking diversity, or preparing DEI disclosures.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Managing DEI Metrics Structures diversity, equity, and inclusion data collection, benchmarking against peer and industry standards, and preparation of disclosure-ready DEI reports for ESG frameworks and investor communications. ## When To Use - Building or auditing a portfolio company's DEI data collection infrastructure - Benchmarking workforce composition against industry peers or index constituents - Preparing DEI disclosures for annual reports, sustainability reports, or LP questionnaires - Responding to ESG rating agency questionnaires (MSCI, Sustainalytics, ISS) that include diversity dimensions - Evaluating fund-level or GP-level diversity commitments (e.g., ILPA diversity metrics template) - Supporting regulatory disclosure under Nasdaq board diversity rules, EU CSRD, or UK FCA diversity requirements [VERIFY] ## Inputs To Gather - **Entity scope**: Fund-level, GP-level, or portfolio company-level; single entity or aggregated - **Reporting framework(s)**: SASB, GRI 405/406, TCFD-adjacent social metrics, ILPA template, UNPRI, proprietary LP templates - **Metric categories**: Board composition, senior leadership, overall workforce, new hires, promotions, attrition, pay equity - **Demographic dimensions**: Gender, race/ethnicity, age, disability status, veteran status; confirm which are legally collectible in relevant jurisdictions [VERIFY] - **Benchmark sources**: Industry peer set, index composition data, national labor force statistics (e.g., BLS EEO-1 categories for US) - **Reporting period**: Fiscal year, calendar year, or point-in-time snapshot date - **Prior period data**: At least one prior period for trend analysis; ideally two or more for trajectory assessment - **Data collection method**: Self-identification surveys, HRIS exports, board questionnaires, third-party data providers ## Workflow 1. **Define metric taxonomy** - Map requested metrics to reporting framework definitions (e.g., GRI 405-1 distinguishes governance bodies vs. employees by category) - Standardize demographic category labels across entities if aggregating multiple portfolio companies - Confirm legal permissibility of collecting each demographic dimension per jurisdiction [VERIFY] 2. **Collect and validate raw data** - Ingest HRIS or survey data; flag response rates below 70% as potentially non-representative - Cross-check headcount totals against payroll or financial records - Identify missing data points and mark with [VERIFY] rather than imputing values - Note self-identification opt-out rates separately — do not merge "declined to state" with any demographic category 3. **Calculate core metrics** - Representation percentages by level (board, C-suite, VP+, manager, individual contributor) - Year-over-year change in representation at each level - Hiring and promotion rates by demographic group relative to applicant/eligible pool - Attrition rates by demographic group (voluntary vs. involuntary where available) - Pay equity ratios (median and mean) by gender and race/ethnicity, controlling for role level and geography where data permits 4. **Benchmark against peers** - Source industry benchmarks from relevant datasets (e.g., McKinsey Diversity Wins, Equileap, Bloomberg Gender-Equality Index) - Present entity metrics alongside 25th, 50th, and 75th percentile benchmarks - Flag metrics where entity falls below 25th percentile as areas of concern - Note benchmark vintage — stale benchmarks (>2 years old) should be flagged 5. **Assess disclosure readiness** - Map completed metrics to each target framework's required and recommended fields - Identify gaps: missing metrics, insufficient granularity, or data quality issues - For regulated disclosures (Nasdaq, CSRD, FCA), confirm all mandatory fields are populated [VERIFY] - Draft narrative context for quantitative metrics — explain material changes, initiatives underway, and targets 6. **Compile output report** - Structure by audience: investor-facing summary, internal management detail, regulatory submission - Include methodology notes covering data sources, response rates, category definitions, and benchmark sources - Attach data tables in appendix format suitable for LP due diligence or rating agency submission ## Output A DEI metrics management report containing: - **Executive summary**: Key representation figures, notable trends, and peer positioning - **Detailed metrics tables**: Broken out by level, demographic dimension, and reporting period - **Benchmark comparison**: Entity vs. peer/industry percentiles with visual indicators (above/below median) - **Gap analysis**: Missing data points, framework compliance gaps, and recommended remediation steps - **Methodology appendix**: Data sources, collection dates, response rates, category definitions, benchmark vintage - **Disclosure crosswalk**: Matrix mapping available metrics to each target framework's line items ## Quality Checks - Confirm all percentages within a category sum to 100% (accounting for rounding) - Verify headcount figures reconcile to a known source of truth (payroll, board roster) - Ensure no personally identifiable information appears in output — all data must be aggregated - Check that small-group thresholds are applied (suppress demographic breakdowns where group size < 5 to prevent re-identification) - Validate that benchmark comparisons use matching scope (e.g., same industry classification, comparable entity size) - Confirm disclosure crosswalk covers all mandatory fields for each specified framework [VERIFY] - Flag any metric where data quality or coverage is insufficient with [VERIFY] and a brief explanation
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