Cognitive Testing Skill

Goal

Test cognitive/agentic loops (Brain Mode active-inference, Loki swarm orchestration) by their MECHANISMS and INVARIANTS rather than by output-equality. You cannot predict what an autonomous loop decides, but you can BOUND it: assert the invariants it must never violate, the convergence it should exhibit, the memory it must retrieve faithfully, and the guards that must fire under stress.

Steps

1. Invariant-bounded state verification

   • Assert mutually-exclusive states never co-exist (e.g. a workspace cannot be both DEV_READY and INFRA_MISSING).
   • Assert budget/cost invariants always hold (spend never exceeds the cap).
   • Assert illegal state transitions never occur (no jump that skips a required gate).

2. Convergence testing

   • For an active-inference / error-minimizing loop, assert prediction error is non-increasing / converges over N iterations.
   • Test the toy demo HONESTLY: GABBE's production brain.py uses epsilon-greedy gene selection plus a monotonic success_rate, NOT literal free-energy math. Convergence claims therefore apply to the CONCEPTUAL / toy loop only, never to the production engine.

3. Episodic-memory integrity

   • Under rapid context switching, assert the resume pointer retrieves the EXACT last state — no fabricated or hallucinated history, no drift.