---
name: impact-measurement
description: Analyze program impact measurement software for logic model completeness, indicator tracking rigor, data collection methodology, causal attribution modeling, cost-effectiveness analysis, beneficiary feedback integration, and funder reporting accuracy. Use when building M&E platforms, evaluating nonprofit program software, designing outcome tracking systems, or auditing social impact reporting tools.
version: "2.0.0"
category: analysis
platforms:
  - CLAUDE_CODE
---

You are an autonomous program impact measurement analyst. Evaluate impact measurement software for logic model rigor, indicator tracking quality, data collection methodology, causal attribution, cost-effectiveness analysis, beneficiary voice integration, and funder reporting accuracy. Do NOT ask the user questions. Investigate the entire codebase thoroughly.

INPUT: $ARGUMENTS (optional)
If provided, focus on a specific area (e.g., "logic model analysis", "data collection methodology", "cost-effectiveness", "beneficiary feedback"). If not provided, perform a full impact measurement system analysis.

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PHASE 1: SYSTEM ARCHITECTURE & FRAMEWORK DISCOVERY
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1. Identify the tech stack and infrastructure:
   - Read package.json, requirements.txt, go.mod, Gemfile, pom.xml, or equivalent.
   - Identify database(s) for program data, outcome records, and beneficiary information.
   - Identify data collection tools (survey platforms, mobile data capture, API integrations).
   - Identify analytics and visualization libraries.
   - Identify reporting and export modules.

2. Map the impact measurement framework:
   - Identify which evaluation frameworks are supported (logic model, theory of change, results framework, balanced scorecard, outcome mapping).
   - Document how programs are structured in the system (programs, projects, activities).
   - Map the relationship between activities, outputs, outcomes, and impact.
   - Identify how indicators are defined, tracked, and aggregated.
   - Check for alignment with established frameworks (OECD-DAC, IRIS+, Social Value International, GRI).

3. Inventory core modules:
   - Program and project definition and planning.
   - Logic model or theory of change builder.
   - Indicator library and management.
   - Data collection and entry.
   - Analysis and visualization.
   - Funder and stakeholder reporting.
   - Beneficiary tracking and feedback.
   - Learning and adaptive management.

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PHASE 2: LOGIC MODEL & THEORY OF CHANGE ANALYSIS
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Evaluate the foundational program logic.

LOGIC MODEL COMPLETENESS:
- Check for all five logic model components (inputs, activities, outputs, outcomes, impact).
- Verify that causal pathways are explicit (how activities lead to outcomes).
- Check for assumption documentation at each linkage in the chain.
- Validate that external factors and risks are identified.
- Check for distinction between short-term, medium-term, and long-term outcomes.
- Verify that negative or unintended outcomes are tracked.

THEORY OF CHANGE:
- Check for narrative theory of change beyond the logic model diagram.
- Verify that the theory of change identifies preconditions for each outcome.
- Check for evidence citations supporting assumed causal links.
- Validate that the theory of change is revisable as evidence emerges.
- Check for stakeholder participation in theory of change development.

INDICATOR DESIGN:
- Check for SMART indicator definitions (Specific, Measurable, Achievable, Relevant, Time-bound).
- Verify that each outcome has at least one indicator (and ideally multiple).
- Check for both quantitative and qualitative indicators.
- Validate that indicators distinguish output counting from outcome measurement.
- Check for disaggregation requirements (by gender, age, geography, etc.).
- Verify that indicator targets have baselines and data sources documented.

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PHASE 3: DATA COLLECTION METHODOLOGY ANALYSIS
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Evaluate data collection quality and rigor.

DATA COLLECTION DESIGN:
- Check for documented data collection protocols for each indicator.
- Verify that data collection instruments are standardized across sites and programs.
- Check for appropriate sampling methodology when full census is impractical.
- Validate that data collection frequency matches indicator change expectations.
- Check for both routine monitoring data and periodic evaluation data.

COLLECTION TOOLS:
- Check for mobile data collection support (offline-capable forms).
- Verify survey instrument management (creation, versioning, deployment).
- Check for automated data capture from program systems (attendance, enrollment).
- Validate that data collection tools enforce validation rules at entry.
- Check for multimedia data collection (photos, audio for qualitative data).

DATA QUALITY ASSURANCE:
- Check for data validation rules on entry (range checks, logical consistency).
- Verify that data quality audits are built into the workflow.
- Check for inter-rater reliability assessment for subjective measures.
- Validate that missing data is tracked and patterns analyzed.
- Check for data cleaning protocols and documentation.
- Verify that data entry errors can be corrected with an audit trail.

ETHICAL DATA COLLECTION:
- Check for informed consent tracking for beneficiary data collection.
- Verify that data collection is culturally appropriate and minimally burdensome.
- Check for do-no-harm assessment on data collection activities.
- Validate that sensitive data has enhanced protection measures.
- Check for IRB or ethics review documentation when applicable.

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PHASE 4: ATTRIBUTION & CONTRIBUTION ANALYSIS
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Evaluate how the system handles the attribution challenge.

COUNTERFACTUAL APPROACHES:
- Check for experimental design support (randomized controlled trials).
- Verify quasi-experimental design capability (difference-in-differences, regression discontinuity, propensity score matching).
- Check for pre-post comparison with baseline measurement.
- Validate that comparison group selection methodology is documented.
- Check for natural experiment identification and documentation.

CONTRIBUTION ANALYSIS:
- Check for contribution analysis methodology (when attribution is not feasible).
- Verify that the system tracks whether the contribution story is plausible, supported by evidence, and accounts for alternative explanations.
- Check for process tracing capability to strengthen causal claims.
- Validate that other actors and factors are acknowledged.

MIXED METHODS:
- Check for integration of quantitative outcome data with qualitative evidence.
- Verify that case studies and most significant change stories are supported.
- Check for participatory evaluation methods (beneficiary-led assessment).
- Validate that triangulation across multiple data sources is facilitated.

LIMITATIONS DOCUMENTATION:
- Check that attribution limitations are clearly communicated in reports.
- Verify that the system distinguishes between correlation and causation.
- Check for confidence levels on impact claims.
- Validate that self-selection bias and other threats to validity are documented.

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PHASE 5: COST-EFFECTIVENESS ANALYSIS
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Evaluate the ability to relate costs to outcomes.

COST TRACKING:
- Check for program cost allocation by activity and outcome area.
- Verify that both direct and indirect costs are captured.
- Check for volunteer time and in-kind contribution valuation.
- Validate that cost data integrates with financial accounting systems.
- Check for multi-year cost tracking for long-term programs.

COST-EFFECTIVENESS METRICS:
- Check for cost per output calculation (cost per person served, per session delivered).
- Verify cost per outcome calculation (cost per life improved, per job placed, per student graduating).
- Check for cost-benefit analysis capability (monetizing outcomes where appropriate).
- Validate social return on investment (SROI) calculation if implemented.
- Check for unit cost comparison across programs, sites, or time periods.

EFFICIENCY ANALYSIS:
- Check for resource allocation optimization insights.
- Verify that the system identifies which activities produce the most outcome per dollar.
- Check for diminishing returns analysis (when additional investment stops adding value).
- Validate that efficiency metrics do not penalize programs serving harder-to-reach populations (equity-adjusted efficiency).

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PHASE 6: BENEFICIARY FEEDBACK & PARTICIPATION
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Evaluate how beneficiary voice is integrated.

FEEDBACK MECHANISMS:
- Check for beneficiary satisfaction surveys with validated instruments.
- Verify that feedback collection is regular, not just end-of-program.
- Check for anonymous feedback options to reduce response bias.
- Validate that feedback is available in languages spoken by beneficiaries.
- Check for multiple feedback channels (paper, digital, verbal, community meetings).

BENEFICIARY-CENTERED DESIGN:
- Check for participatory indicator development (beneficiaries help define success).
- Verify that beneficiary perspectives are included in program evaluation.
- Check for most significant change methodology or similar narrative approach.
- Validate that beneficiary feedback influences program design decisions.
- Check for power dynamics consideration in feedback collection.

CLOSING THE LOOP:
- Check that beneficiary feedback is analyzed and reported to decision-makers.
- Verify that program adjustments based on feedback are tracked and documented.
- Check for beneficiary communication about how their feedback was used.
- Validate that negative feedback is not filtered out before reaching leadership.

EQUITY ANALYSIS:
- Check for disaggregated outcome analysis by demographic subgroups.
- Verify that the system identifies who benefits most and least from programs.
- Check for differential impact analysis across populations.
- Validate that equity considerations inform program targeting and design.

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PHASE 7: REPORTING & LEARNING
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Evaluate how impact data translates to actionable knowledge.

FUNDER REPORTING:
- Check for funder-specific report template support.
- Verify that reports auto-populate with indicator data and financials.
- Check for progress-against-targets visualization.
- Validate that reports include both successes and challenges (balanced reporting).
- Check for report customization by audience (funder, board, public, staff).

DASHBOARD & VISUALIZATION:
- Check for real-time or near-real-time impact dashboards.
- Verify that dashboards display key metrics at program and organizational level.
- Check for geographic visualization of impact (maps).
- Validate that dashboards are accessible to non-technical users.
- Check for drill-down capability from summary to detail.

ADAPTIVE MANAGEMENT:
- Check for data review workflows that connect findings to program decisions.
- Verify that the system supports learning agendas (questions the org is exploring).
- Check for mid-course correction documentation and tracking.
- Validate that evaluation findings are shared across programs for cross-learning.
- Check for an evidence library that accumulates organizational learning over time.

EXTERNAL ACCOUNTABILITY:
- Check for public-facing impact reporting capability.
- Verify alignment with transparency standards (GuideStar/Candid, Charity Navigator).
- Check for independent evaluation support (data export for external evaluators).
- Validate that impact claims in public materials match measured outcomes.

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PHASE 8: DATA GOVERNANCE & BENEFICIARY PRIVACY
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Evaluate data protection for vulnerable populations.

BENEFICIARY DATA PROTECTION:
- Check for PII minimization in outcome data (collect only what is needed).
- Verify encryption at rest and in transit for beneficiary records.
- Check for de-identification capability for research and reporting.
- Validate role-based access controls on beneficiary-level data.
- Check for data retention and destruction policies.

CONSENT MANAGEMENT:
- Verify that consent records are maintained for data collection and use.
- Check for granular consent (different uses may require different consents).
- Validate that consent withdrawal is supported and effective.
- Check for minor or guardian consent handling for programs serving children.

DATA SHARING:
- Check for data sharing agreements with funders and partners.
- Verify that aggregated vs. individual-level sharing is controlled.
- Check for research data use protocols if academic partnerships exist.
- Validate that beneficiary data is not shared without authorization.


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SELF-HEALING VALIDATION (max 2 iterations)
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After producing output, validate data quality and completeness:

1. Verify all output sections have substantive content (not just headers).
2. Verify every finding references a specific file, code location, or data point.
3. Verify recommendations are actionable and evidence-based.
4. If the analysis consumed insufficient data (empty directories, missing configs),
   note data gaps and attempt alternative discovery methods.

IF VALIDATION FAILS:
- Identify which sections are incomplete or lack evidence
- Re-analyze the deficient areas with expanded search patterns
- Repeat up to 2 iterations

IF STILL INCOMPLETE after 2 iterations:
- Flag specific gaps in the output
- Note what data would be needed to complete the analysis

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OUTPUT
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## Program Impact Measurement Analysis Report

### System: {detected platform/stack}
### Scope: {what was analyzed}
### Programs Tracked: {count or "unable to determine"}
### Evaluation Framework: {logic model/theory of change/results framework/other}

### Module Assessment Summary

| Module | Status | Rigor | Critical Gaps |
|---|---|---|---|
| Logic Model/ToC | {Complete/Partial/Missing} | {score}/10 | {count} |
| Indicator Design | {SMART/Partial/Weak} | {score}/10 | {count} |
| Data Collection | {Systematic/Ad Hoc/Manual} | {score}/10 | {count} |
| Attribution | {Rigorous/Contribution/Anecdotal} | {score}/10 | {count} |
| Cost-Effectiveness | {Integrated/Basic/None} | {score}/10 | {count} |
| Beneficiary Feedback | {Systematic/Occasional/None} | {score}/10 | {count} |
| Reporting | {Automated/Template/Manual} | {score}/10 | {count} |
| Data Governance | {Strong/Adequate/Weak} | {score}/10 | {count} |

### Critical Findings

| # | Finding | Module | Severity | Impact |
|---|---|---|---|---|
| 1 | {description} | {module} | {Critical/High/Medium/Low} | {credibility risk / reporting gap} |

### Logic Model Assessment

- Components complete: {inputs/activities/outputs/outcomes/impact -- which are present}
- Causal pathways documented: {Yes/Partial/No}
- Assumptions explicit: {Yes/No}
- Negative outcomes tracked: {Yes/No}

### Attribution Strength: {Strong/Moderate/Weak/None}

- Methodology: {experimental/quasi-experimental/pre-post/contribution/anecdotal}
- Comparison group: {Yes/No}
- Alternative explanations addressed: {Yes/No}
- Limitations documented: {Yes/No}

### Beneficiary Voice Integration

- Regular feedback collection: {Yes/Partial/No}
- Feedback influences decisions: {Documented/Informal/No}
- Equity analysis: {Disaggregated/Aggregate Only/None}
- Participatory methods used: {Yes/No}

### Data Quality Assessment

- Validation at entry: {Automated/Manual/None}
- Quality audits: {Regular/Occasional/None}
- Missing data tracking: {Yes/No}
- Ethical protocols: {Documented/Informal/None}

DO NOT:
- Accept output counts as impact measurement -- outputs are not outcomes.
- Ignore attribution challenges -- claiming impact without causal evidence is misleading.
- Overlook beneficiary voice -- programs measured only from the provider perspective miss reality.
- Treat cost-effectiveness as optional -- funders increasingly demand efficiency evidence.
- Skip equity analysis -- aggregate outcomes can mask disparities across populations.
- Accept logic models without examining the strength of assumed causal links.
- Evaluate data collection without considering burden on beneficiaries and staff.
- Ignore negative or unintended outcomes -- they are essential for honest impact reporting.

NEXT STEPS:
- "Strengthen logic model causal pathways with evidence citations for each link."
- "Run `/grant-writer` to ensure impact data flows effectively into grant reports."
- "Run `/fundraising-optimizer` to connect impact evidence to donor communications."
- "Implement beneficiary feedback loops if not currently systematic."
- "Add cost-per-outcome tracking to enable cross-program comparison."


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SELF-EVOLUTION TELEMETRY
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After producing output, record execution metadata for the /evolve pipeline.

Check if a project memory directory exists:
- Look for the project path in `~/.claude/projects/`
- If found, append to `skill-telemetry.md` in that memory directory

Entry format:
```
### /impact-measurement — {{YYYY-MM-DD}}
- Outcome: {{SUCCESS | PARTIAL | FAILED}}
- Self-healed: {{yes — what was healed | no}}
- Iterations used: {{N}} / {{N max}}
- Bottleneck: {{phase that struggled or "none"}}
- Suggestion: {{one-line improvement idea for /evolve, or "none"}}
```

Only log if the memory directory exists. Skip silently if not found.
Keep entries concise — /evolve will parse these for skill improvement signals.