managing-impact-fund-reporting
Structures impact fund reporting with IRIS+ metrics, theory of change alignment, and additionality assessment. Use when reporting impact metrics, using IRIS+ indicators, or measuring fund impact.
Best use case
managing-impact-fund-reporting is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Structures impact fund reporting with IRIS+ metrics, theory of change alignment, and additionality assessment. Use when reporting impact metrics, using IRIS+ indicators, or measuring fund impact.
Teams using managing-impact-fund-reporting 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-impact-fund-reporting/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How managing-impact-fund-reporting Compares
| Feature / Agent | managing-impact-fund-reporting | 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 impact fund reporting with IRIS+ metrics, theory of change alignment, and additionality assessment. Use when reporting impact metrics, using IRIS+ indicators, or measuring fund impact.
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 Impact Fund Reporting Structures impact fund reporting with IRIS+ metrics, theory of change alignment, and additionality assessment. ## When To Use - Preparing quarterly or annual impact reports for LP distribution - Selecting and mapping IRIS+ metrics to fund-level and portfolio-company-level outcomes - Aligning reported results with the fund's stated theory of change - Assessing additionality — demonstrating that impact would not have occurred absent the fund's investment - Responding to GIIN, IMP, or SFDR reporting requirements [VERIFY framework applicability by fund domicile and LP base] - Benchmarking fund impact performance against sector or peer cohorts ## Inputs To Gather - **Fund documents**: LPA impact mandate, theory of change narrative, side letter impact commitments - **IRIS+ catalog selections**: Confirmed strategic goals, core metric sets, and any custom indicators already adopted - **Portfolio data**: Company-level KPIs, baseline measurements, and reporting-period actuals for each IRIS+ metric - **Prior reports**: Previous impact reports, LP feedback, and any data quality flags from prior cycles - **Framework obligations**: Applicable disclosure standards (SFDR PAI indicators, IMP dimensions, OPIM conventions, SDG mapping) [VERIFY which frameworks apply] - **Attribution methodology**: Approach used for counterfactual/additionality (e.g., contribution analysis, quasi-experimental, qualitative narrative) ## Workflow 1. **Confirm reporting scope and period** - Identify which portfolio companies are in scope (active, exited within period, write-offs) - Confirm reporting date, currency, and consolidation method (pro-rata vs. full attribution) 2. **Map theory of change to IRIS+ metrics** - Link each theory-of-change outcome to one or more IRIS+ indicators (e.g., PI2607 for client individuals, OI1638 for units sold) - Flag gaps where theory-of-change outcomes lack a measurable IRIS+ proxy — propose alternative indicators or qualitative evidence - Note any custom metrics not in the IRIS+ catalog and document their definitions 3. **Collect and validate portfolio-company data** - Distribute data collection templates aligned to selected IRIS+ indicators - Cross-check reported figures against financial data, third-party sources, or prior baselines - Mark unverified or estimated data points with [VERIFY] and note estimation methodology 4. **Aggregate to fund level** - Roll up company-level metrics using the agreed attribution method - Calculate weighted and unweighted portfolio averages where relevant - Present both absolute outcomes (e.g., total beneficiaries reached) and normalized metrics (e.g., impact per $M deployed) 5. **Assess additionality** - For each material outcome, articulate the counterfactual: what would have occurred without the fund's capital and engagement - Document investor contribution along IMP dimensions (financial additionality, engagement/TA additionality, signaling) - Rate additionality confidence (high / moderate / low) and disclose basis for the rating 6. **Draft the impact report** - Structure sections: executive summary, theory of change recap, metric-by-metric results, additionality narrative, portfolio spotlights, data quality notes, forward-looking targets - Include visual dashboards — progress-toward-target charts, SDG alignment heat maps, year-over-year trend lines - Append a data methodology annex covering collection process, estimation conventions, and assurance status 7. **Review and finalize** - Circulate draft to investment team and impact leads for factual review - Reconcile any LP-specific reporting obligations from side letters - Obtain sign-off from fund manager or impact committee before distribution ## Output - **Impact report** structured with theory-of-change alignment, IRIS+ metric tables (indicator ID, definition, baseline, target, actual, variance), additionality assessment, and data quality disclosures - **Metric appendix** listing each IRIS+ indicator used, its catalog definition, and any fund-specific adaptations - **Data quality summary** flagging estimated values, missing data, and verification status per company - **LP-ready executive summary** (1–2 pages) with headline outcomes, portfolio highlights, and forward targets ## Quality Checks - Every IRIS+ metric cited includes the correct indicator ID and standard definition — no ad hoc renaming - Theory of change linkage is explicit: each reported metric traces back to a stated outcome in the fund's impact thesis - Additionality narrative goes beyond "we invested" — articulates specific counterfactual reasoning per material outcome - Attribution methodology is disclosed and applied consistently across portfolio companies - Data quality flags are transparent — no estimated figures presented as actuals - SDG or framework mappings (SFDR, IMP) match the indicator evidence, not just thematic association [VERIFY alignment with current framework versions] - Report period, scope, and consolidation basis are stated upfront — reader can understand what is and is not included - Forward-looking targets are time-bound and reference the same IRIS+ indicators used for actuals
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