Support operations leader who builds with AI. I run a globally distributed technical support team and put AI into production support workflows. This is where the tools from that work, and a few from outside it, go public.
Everything here is open source. Anything marked Live runs in your browser with nothing to install.
The measuring is the part that usually gets skipped.
Your AI vendor reports 85% deflection. This audits what that number survives. It re-judges every AI-closed conversation from the transcript alone, then recomputes the rate crediting only closures that hold up. On a 40-conversation labeled set, 80% claimed came out to 55% audited.
Agent skills are prompt-matched, so a model upgrade can stop one from firing without an error anywhere. Static checks plus live dispatch tests of 23 frozen prompts, run under a tool denylist so skills that reach Salesforce and Gmail can't touch either. 21 of 23 correct on the live run.
Does a second agent pay for itself? Generalist, parallel specialists, and generalist plus QA reviewer, measured on accuracy, cost per ticket, and latency. Specialists took routing from 83% to 97% and left severity flat; the QA layer lifted severity from 70% to 80% at $0.60 per net correction.
The eval machinery from these projects, pulled out into something reusable. Blind classification, confusion matrices, ordinal miss-direction splits, and confusable-pattern trap audits. It's the scoring layer under deflection-audit and the skill harness.
An LLM triage classifier with a real eval harness. Severity and routing for IoT support tickets, scored against a 60-ticket labeled set with under- versus over-triage analysis. It caught all 12 critical tickets.
Production knowledge from running a support org, encoded into something another team can use.
When a support copilot gives a wrong answer, the root cause is often the article, not the model. Paste a help-center article and get an AI-readiness grade across structure, answer clarity, self-containment, machine readability, and language quality, with a concrete fix for each finding.
Read-only Salesforce case operations as MCP tools an agent can call. SOQL, case lookups, queue volumes, and SLA milestone risk. Auth stays in the sf CLI keychain, read-only is enforced by construction, and it runs against a live org daily.
Job discovery and resume tailoring built on Claude Code. It polls 50+ company job boards, and a calibration audit re-derives every score from the rubric that produced it, flagging decisions made under rules that have since changed.
Same habits, different problems.
MQD Runway: project your year-end Delta MQDs and price out what closes the gap to each Medallion tier. Every program rule is editable data, because loyalty programs change their math and hardcoded calculators go stale.
A native-plant planner for the Georgia Piedmont that solves for unbroken bloom. It keeps something flowering from March through November and treats heavy clay as a real constraint rather than a footnote.
Evidence grading for research peptides. Per-claim provenance and a fixed rubric, published as one standalone record per compound.