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Show HN: OpenClaw skill for nonprofit RBM logic models (ToC, indicators, M&E)
- _dquq 8mo agoFor years I’ve worked on Results-Based Management (RBM) in nonprofit/development programs, where early-stage logic model design is still very manual and repetitive. I started with GPT-assisted drafting, and now I’m piloting the next step with OpenClaw autonomous agents: a focused skill for nonprofit RBM logic model development. What it generates: -5-level results chain: Inputs -> Activities -> Outputs -> Outcomes -> Impact -Theory of Change (if/then pathway + assumptions + risks) -SMART outcome indicators -SDG alignment -Monitoring and data collection plan Who it’s for: -nonprofit program managers -MEAL/M&E specialists -grant writers and NGO consultants This is early-stage and intentionally human-in-the-loop. Goal: faster structured drafting, but expert validation remains the hard gate for quality decisions. Main open issues I’m actively thinking about: confidentiality, governance, accountability, and validation quality. Repo: https://github.com/vassiliylakhonin/Nonprofit-RBM-Skill-For-Claw-Hub https://github.com/vassiliylakhonin/Nonprofit-RBM-Skill-For-... Skill on ClawHub:https://clawhub.ai/vassiliylakhonin/nonprofit-rbm-logic-model https://clawhub.ai/vassiliylakhonin/nonprofit-rbm-logic-mode... Install: clawhub install nonprofit-rbm-logic-model More technical experiments: https://github.com/vassiliylakhonin https://github.com/vassiliylakhonin If you work with real RBM/logframe workflows, I’d really value tough feedback and edge cases.