conducting-peer-benchmarking-analysis
Evaluates fund performance against peer universes with vintage year comparison, quartile ranking, and strategy-specific benchmarking. Use when benchmarking fund performance, analyzing vintage comparisons, or assessing relative positioning.
Best use case
conducting-peer-benchmarking-analysis is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Evaluates fund performance against peer universes with vintage year comparison, quartile ranking, and strategy-specific benchmarking. Use when benchmarking fund performance, analyzing vintage comparisons, or assessing relative positioning.
Teams using conducting-peer-benchmarking-analysis 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/conducting-peer-benchmarking-analysis/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How conducting-peer-benchmarking-analysis Compares
| Feature / Agent | conducting-peer-benchmarking-analysis | 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?
Evaluates fund performance against peer universes with vintage year comparison, quartile ranking, and strategy-specific benchmarking. Use when benchmarking fund performance, analyzing vintage comparisons, or assessing relative positioning.
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
# Conducting Peer Benchmarking Analysis ## When To Use - Preparing quarterly or annual LP reports that require relative performance context - Responding to LP due diligence requests for peer comparison data - Evaluating fund positioning ahead of fundraising or investor meetings - Assessing whether a fund's return profile warrants strategy adjustments - Building track record presentations for new fund marketing materials ## Inputs To Gather - **Fund performance data**: Net IRR, gross IRR, TVPI, DPI, RVPI, and PME ratios for each fund vehicle - **Vintage year**: The year of first capital call (not final close) for accurate cohort matching - **Strategy classification**: Buyout, growth equity, venture (early/late), real estate (value-add/opportunistic/core), credit, infrastructure, secondaries, or fund-of-funds - **Geography focus**: North America, Europe, Asia-Pacific, global, or emerging markets - **Fund size band**: Confirm AUM range to select the correct peer slice (e.g., small-cap buyout vs. mega-cap) - **Benchmark source(s)**: Cambridge Associates, Preqin, Burgiss, PitchBook, Hamilton Lane, or proprietary LP datasets — note each source's methodology and universe construction [VERIFY] - **Reporting date**: As-of date for all metrics; confirm alignment between fund data and benchmark data timestamps - **Currency**: Base currency for the fund and whether benchmark data is hedged or unhedged ## Workflow 1. **Define the peer universe** - Match vintage year exactly; avoid blending adjacent vintages unless the universe is too small (< 15 funds) - Filter by strategy, geography, and fund size band - Document universe size (number of funds) and any exclusions applied - If using multiple benchmark providers, note universe overlap and methodology differences (e.g., Cambridge uses pooled IRR; Burgiss uses fund-level median) 2. **Normalize performance metrics** - Align as-of dates — if the fund reports on 12/31 but the benchmark updates on 9/30, flag the gap - Confirm net-to-LP vs. gross-of-fees basis; never compare net IRR to gross benchmarks - Convert to common currency if the fund and benchmark use different bases - For younger funds (vintage < 3 years), emphasize DPI and called capital % over IRR, which is volatile early in fund life 3. **Calculate quartile rankings** - Rank the fund within the peer universe for each metric: net IRR, TVPI, DPI - Assign quartile (Q1 = top 25%, Q2 = 25-50%, Q3 = 50-75%, Q4 = bottom 25%) - Report the exact percentile where available, not just the quartile band - Note the spread between quartile boundaries — a narrow Q1/Q2 boundary signals a compressed peer set 4. **Run PME analysis** (if applicable) - Select the appropriate public market index (e.g., S&P 500, MSCI World, Russell 2000) based on strategy and geography [VERIFY index selection against LP preferences] - Calculate Kaplan-Schoar PME, Long-Nickels PME, or Direct Alpha — document which method and why - A PME > 1.0 indicates outperformance vs. the public index on a cash-flow-weighted basis 5. **Contextualize the results** - Compare current quartile ranking to prior reporting periods — is the fund trending up or down? - Identify J-curve effects for younger funds that suppress early IRR - Note any survivorship bias in the benchmark dataset (liquidated underperformers may be excluded) - For funds nearing end of life, weight DPI and realized multiples more heavily than IRR 6. **Compile the benchmarking output** - Summary table: Fund metric | Peer median | Peer upper quartile | Fund quartile rank - Vintage year scatter plot or bar chart positioning the fund within the distribution - Narrative commentary explaining relative positioning, trends, and any caveats - Source attribution for all benchmark data with as-of dates ## Output - **Peer benchmarking summary table** with fund metrics alongside peer median, upper quartile, and lower quartile for each KPI - **Quartile ranking card** showing net IRR, TVPI, and DPI rankings with percentile positions - **PME comparison** (where applicable) with index selection rationale - **Narrative commentary** (2-4 paragraphs) contextualizing performance relative to peers, noting trends, J-curve effects, and data limitations - **Data source disclosures** listing benchmark provider, universe size, vintage year, as-of date, and any filters applied ## Quality Checks - Confirm vintage year assignment uses first capital call date, not final close date - Verify net vs. gross alignment — never mix fee bases in a single comparison - Check that peer universe size is disclosed; flag universes with fewer than 15 funds as potentially unreliable - Ensure as-of dates match within one quarter between fund data and benchmark data - Validate that quartile boundaries are calculated from the correct universe (not a broader or narrower slice) - Confirm PME index selection is appropriate for the fund's strategy and geography - Mark any stale benchmark data (> 6 months old) with [VERIFY] for updated figures - Review for survivorship bias disclosure — note whether the benchmark includes liquidated or written-off funds