_dquq

This is how I analyze geopolitics with OpenClaw.

I developed OpenClaw Think-Tank Intelligence, a skill that transforms complex global crises into concise, insightful think-tank memos. These memos include stakeholder maps, scenarios, policy options, tradeoffs, risks, confidence levels, and assumptions. You’ll be amazed by the quality of analytics. https://clawhub.ai/vassiliylakhonin/global-think-tank-analys...

yes, interesting to discover

Built a small OpenClaw skill for turning messy geopolitical situations into something closer to a real analyst memo.

The use case I had in mind was exactly the kind of fast-moving crisis where everyone has opinions, but very few people structure the problem well, for example the Iran war and its second-order effects on escalation, oil, regional actors, sanctions, shipping routes, and policy response options.

This skill is designed to help break that kind of situation into:

stakeholder mapping scenarios policy options and trade-offs implementation risks confidence levels and assumptions The goal is not “predict the future,” but force a more disciplined way to think through what might happen and what decisions actually follow from that.

Repo: https://github.com/vassiliylakhonin/global-think-tank-analys...

Example prompt: Use $global-think-tank-analyst to assess escalation scenarios, regional spillover risks, and policy response options related to the Iran war.

What I’d love feedback on: - API design for async + HITL workflows - where donor-specific logic should live vs generic strategy prompts - ingestion/RAG ergonomics for real proposal teams - whether this is useful as a standalone API vs embedded library

A few caveats / current limitations (so expectations are clear):

- It’s an MVP and currently optimized for drafting workflow structure, not final donor submission formatting. - Donor coverage is mixed: some donors have specialized strategy behavior, others use shared generic logic with catalog aliases. - RAG ingestion is intentionally simple right now (PDF ingest + namespace isolation); deeper citation traceability is on the roadmap. - Multi-tenant auth/permissions is not implemented yet (API key is service-level).

For 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-...

Skill on ClawHub:https://clawhub.ai/vassiliylakhonin/nonprofit-rbm-logic-mode... Install: clawhub install nonprofit-rbm-logic-model More technical experiments: https://github.com/vassiliylakhonin If you work with real RBM/logframe workflows, I’d really value tough feedback and edge cases.

I’m testing a personal profile site optimized for AI/search parsing (llms.txt, schema, sitemap, case studies, measurable outcomes). For those experimenting with this: what has actually moved the needle for discoverability and recruiter conversion?

Site: https://vassiliylakhonin.github.io/ Repo: https://github.com/vassiliylakhonin/vassiliylakhonin.github....