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.

11 stars

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

$curl -o ~/.claude/skills/managing-impact-fund-reporting/SKILL.md --create-dirs "https://raw.githubusercontent.com/CaseMark/skills/main/skills/finance/managing-impact-fund-reporting/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/managing-impact-fund-reporting/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How managing-impact-fund-reporting Compares

Feature / Agentmanaging-impact-fund-reportingStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/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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