# Nonprofit Rbm Logic Model

> 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).

- Skill: `dvcrn/nonprofit-rbm-logic-model` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add dvcrn/nonprofit-rbm-logic-model`
- Raw SKILL.md: https://api.skillmd.com/api/skills/dvcrn/nonprofit-rbm-logic-model/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: dvcrn (https://skillmd.com/u/dvcrn)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/dvcrn/nonprofit-rbm-logic-model

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# 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.

## 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

## 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.

## Reference File

Read `references/rbm-framework.md` when you need:
- Indicator templates
- Sector-specific indicator ideas
- SDG mapping shortcuts
- Worked examples