Results for “export”

Discover OPC UA servers and create client connections in MATLAB using opcuaserverinfo, opcua, connect, setSecurityModel, and certificate trust functions. Use when discovering OPC UA servers on the network, connecting to OPC UA servers, authenticating with username/password or certificates, configuring security modes, handling certificate trust errors, fixing hostname mismatch warnings, troubleshooting connection failures or empty discovery results, or inspecting an OPC UA certificate (.der or .pem) for compliance issues. Trigger on: opcuaserverinfo, OPC UA discovery, find OPC UA servers, LDS setup, opcua, opc.ua.Client, connect OPC UA, OPC UA client, OPC UA security, OPC UA certificate trust, OPC UA certificate inspection, setSecurityModel, opc.ua.trustServerCertificate, opc.ua.exportClientCertificate, Industrial Communication Toolbox, OPC UA server connection, OPC UA server discovery.

Provides frameworks for international market expansion, including market selection, entry modes, localization, and go-to-market strategy.

Use when the user asks to run a full empirical / causal analysis in Python — by default in the style of an applied economics paper (AER / QJE / JPE / ReStud / AEJ) with DID / RD / IV / SCM / DML / matching, written-out estimating equation + identifying assumption, Table 1 / Table 2 / event-study figure / robustness gauntlet — OR in epidemiology / public health style (target-trial emulation, IPTW + g-formula + TMLE triplet, Mendelian randomization, KM/AFT survival, E-value sensitivity, STROBE/TRIPOD reporting) — OR in ML causal inference style (DML, S/T/X/R/DR meta-learners, causal forest, Dragonnet/TARNet/CEVAE, BCF, CATE distribution, policy learning, conformal causal, fairness audit, causal discovery) — OR in distributional / gap-decomposition style (Oaxaca–Blinder `sp.oaxaca`, Kitagawa `sp.kitagawa_decompose`, DiNardo–Fortin–Lemieux `sp.dfl_decompose`, Gelbach `sp.gelbach`, Fairlie `sp.fairlie`, RIF / FFL `sp.rif_decomposition`, all reachable through the `sp.decompose` dispatcher). Also covers exporting mu

Translates stakeholder feature requests into validated outcome statements before any work is committed. Use this skill — proactively and without waiting to be asked — whenever a stakeholder, exec, or customer arrives with a pre-packaged solution: "we need a dashboard", "add a Slack notification", "build an export feature", "create a report", "let's add a filter", "can we just add X". Also triggers for: "how do I push back on this request", "what outcome does this feature solve", "outcome vs output", "outcomes not features", "what are we really trying to achieve", "we're being a feature factory", "I need to reframe this as a problem", "the stakeholder is pushing a specific solution", "discovery before delivery", "assumption testing", "translate this request into an outcome", "ship outcomes not features". Runs the 'One Framework. Four Questions.' protocol (Liatti + Cagan + Torres): Behavior Change → Assumption Test → Cheapest Test → Success Metric. Produces an Outcome Brief with embedded AI prompts ready to pas