Auto-Research → OrchestKit · Adoption Explorer

What ArieGoldkin/claude-dev-kit's etk:auto-research does, and what OrchestKit can lift from it. Auto-research is a router + Karpathy loop layer: one plain-English goal → intent classifier → one of 7 specialized skills → modify·measure·keep/discard → report. OrchestKit already owns richer specialists — but has no front door and no first-class metric loop. Explore the 10 learnings, then read the synthesis.

source: etk v2.7.6 · engineering-toolkit 8 intent categories 7 route targets inspiration: Karpathy autoresearch

🧭 Router simulator // learning #1, live

Type a goal the way a human would. The classifier (a tiny keyword model, exactly etk's approach) picks an intent and routes to the real ork skill that would handle it — and flags where ork has a gap.

Awaiting a goal… try a chip above.
◂ Select a learning to inspect its mechanics, ork's current state, and the recommended adoption.

📚 The 10 learnings // click to inspect

🎯 Synthesis — top 3, devil's-advocate scored

Composite = mean of impact, leverage, and feasibility minus risk (0–10). Sequenced by dependency: the engine underpins both the router's optimize route and the self-improvement loop.

Reality check baked in: auto-research explicitly credits ork's ci-sentinel, ScheduleWakeup cadence, and the no-paid-background-LLM rule — so this is mutual borrowing, not one-way. ork's specialists (37 agents, 112 skills) are deeper than etk's 7 route targets; the gap is the unifying layer, not the parts. Don't build the router before the engine — its strongest route (optimize) routes into the loop that doesn't exist yet.