name: nonprofit-rbm-logic-model description: >- Build donor-ready Results-Based Management (RBM) logic models for nonprofit and NGO programs: Theory of Change, 5-level results chain (inputs→impact), SMART outcome indicators, SDG alignment, and practical monitoring plans. Use for grant proposals, program design, M&E frameworks, and donor reporting (USAID/UN/EU).

Nonprofit RBM Logic Model

Objective

Produce a decision-ready, donor-aligned RBM package that links activities to outcomes and impact with measurable indicators and realistic monitoring.

Execution Workflow

1. Collect minimum context before drafting:

   • Problem statement and intervention summary
   • Target population (including inclusion priorities)
   • Geography and implementation scope
   • Time horizon (for example, 12-month outcomes, 3-5 year impact)
   • Donor/reporting constraints (USAID, UN, EU, or custom template)
   • Baseline/data availability constraints

2. Ask up to five high-leverage clarifying questions if key inputs are missing.

   • If details remain unknown, proceed with explicit assumptions.

3. Build the results chain with strict level separation:

   • Inputs
   • Activities
   • Outputs
   • Outcomes (short/medium term)
   • Impact (long term)

4. Keep causal logic testable:

   • Do not label activities as outcomes
   • Do not label deliverables as impact
   • Use time-bound, observable outcome statements

5. Define outcome indicators (3-5 per outcome):

   • Indicator name and definition/formula
   • Baseline and target
   • Disaggregation (sex/age/location, when relevant)
   • Data source and collection frequency

6. Map outcomes and impact to SDGs only when evidence-based linkage exists.

7. Build a practical monitoring plan:

   • Baseline/endline schedule
   • Routine monitoring cadence
   • Follow-up windows (for example 3/6/12 months)
   • Data quality checks and accountable owner

8. Return output in the required structure.

9. When structured JSON is available, run deterministic quality gate:

   • scripts/rbm_gate.py --input <rbm.json>
   • Include gate score/verdict in the final response.

Required Output Structure

1. Theory of Change (if/then logic + assumptions)
2. Executive Summary (2-3 sentences)
3. Logic Model (Inputs → Activities → Outputs → Outcomes → Impact)
4. Outcome Indicators (grouped by outcome)
5. SDG Alignment (goal + target references)
6. Monitoring & Data Collection Plan (method, cadence, owner)
7. Assumptions, Risks, and Mitigations
8. Gate Status (Pass / Conditional Pass / Fail, score X/100 when applicable)

Quality Standards

• Prefer numeric, time-bound targets over qualitative claims.
• Distinguish outputs vs outcomes with discipline.
• Keep impact long-term unless user explicitly asks otherwise.
• Surface uncertainty and assumptions explicitly.
• Flag missing baseline data and propose a collection method.
• Include deterministic gate evidence when machine-readable output is provided.

Reference File

Read references/rbm-framework.md when you need:

• Indicator templates
• Sector-specific indicator ideas
• SDG mapping shortcuts
• Worked examples