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Hey HN,

I'm David, CEO of Expanso. Today we're launching Expanso Skills — a catalog of 200+ production-ready data processing pipeline recipes for AI agents.

The problem: Every team rebuilds the same data processing primitives from scratch. PII scrubbing, log aggregation, GDPR routing, schema enforcement, dead letter queues. Each time, slightly broken. Each team, from zero.

What we built: Reusable, composable pipeline recipes that run on Expanso Edge (our open-source distributed compute layer). Think npm packages, but for data processing.

A few examples: - `remove-pii` — Strips sensitive fields before data reaches your AI agent - `parse-logs` — 1,000 raw log lines → 1 structured JSON digest (99.9% reduction) - `cross-border-gdpr` — Routes data based on jurisdiction automatically - `dead-letter-queue` — Capture and retry failed pipeline messages - `fan-out-kafka` / `fan-out-s3` — Distribute processed data to multiple destinations - `enforce-schema` — Validate and coerce incoming data before it hits downstream services

Install and run in under 2 minutes: expanso skill install remove-pii expanso skill run remove-pii --input ./customer-data.csv

Why this matters for AI agents: Most agent frameworks assume agents can query whatever they want. That's a security and compliance disaster. Skills enforce least-privilege at the infrastructure level — agents never see raw data, only filtered, purpose-fit outputs.

Product Hunt: https://www.producthunt.com/products/expanso-skills?utm_sour...

Happy to answer anything — architecture, specific skill implementations, edge deployment. Ask away.

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I’ve spent years doing results-based management (RBM) for nonprofits and development programs—mostly designing activities, logic models, and M&E frameworks.

I started by using a custom GPT to improve consistency in early analysis/drafting: https://lnkd.in/dbmBAXMB

Now I’m piloting the next step with OpenClaw, including a focused skill for RBM logic model development: https://lnkd.in/dYuhhN8r

This is still early, but the goal is practical: build specialized agents for development consulting workflows, with human experts making final judgments (not full automation).

Main questions I’m working through: -confidentiality and data handling -security boundaries -governance/accountability of AI-assisted recommendations -how to preserve methodological rigor while speeding up workflow

Long-term, I want to digitize core consulting tasks end-to-end: -problem definition -theory of change -indicators and assumptions -risk logic -adaptation planning

If you work in development programming, M&E, or RBM system design, I’d value feedback and real-world test cases. More technical notes/experiments: https://lnkd.in/d_RwMaFp

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