convergence-checklisted

# convergence-check Goal: - Evaluate whether a hypothesis newly entered the current top-k set and update the convergence counter deterministically. Inputs: - `hypothesis_id` - `previous_top_k_ids` - `current_top_k_ids` - current convergence count - caller-owned `state/EVOLUTION_STATE.json` Outputs: - `ConvergenceCheckResult` - updated convergence count - when consumed by the evolution loop, updated `state/EVOLUTION_STATE.json` Context Loading: - Open `skills/shared-references/schema-index.md`. - Read `packages/agent_contracts/pipeline_control.py` and confirm the exact `EvolutionStateContract` shape before writing `state/EVOLUTION_STATE.json`. - Treat the top-k sets as caller-supplied frontier inputs. This skill only evaluates the rule and updates the counter. Execution Contract: - This skill is deterministic and must not call an LLM. - Use `from tools import evaluate_convergence` as the stable invocation surface. - The exported helper is implemented in `packages/agent_mechanics/convergence_check.py`. - The helper signature is `evaluate_convergence(hypothesis_id, previous_top_k_ids, current_top_k_ids, current_convergence_count) -> ConvergenceCheckResult`. Execution Steps: 1. Open `skills/shared-references/schema-index.md`, then read `packages/agent_contracts/pipeline_control.py` before writing `state/EVOLUTION_STATE.json`. 2. Read the candidate `hypothesis_id`, the previous and current top-k sets, and the current convergence count. 3. Call `tools.evaluate_convergen