Integrate founding explorations and bootstrap operational radar

Assistant: codex
Assistant-Model: gpt-6-astra
Assistant-Session: 01a09c77-b1a9-7eb3-b501-6cb24b458c97

task-router/METHOD.md

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+# Capability Advisor — operational method
+
+The normative method is [TaskModelEvaluationMethod](../TaskModelEvaluationMethod.md).
+Use policy IDs `benchmark-selection-v0.1` and `model-ranking-v0.1`.
+
+1. Record task, deliverable, acceptance criteria, risk class, constraints, tools,
+   budget and requester-accessible deployments. Score the six clarity components 0–1.
+2. Map required capabilities and hard gates. Select five distinct benchmark versions
+   with task-specific weights summing to one; document relevance, redundancy,
+   weaknesses, provenance and dates. The atlas is only a discovery aid.
+3. Preserve model, effort, harness, provider and deployment identity for results.
+   Treat missing results as missing. Label normalized benchmark scores as proxies.
+4. Rank eligible profiles by comparative task fitness. Frontier maximizes fitness;
+   Topnotch includes fitness >= .90 of Frontier. Efficient minimizes expected total
+   task cost within Topnotch, using dated prices and explicit workload assumptions.
+5. Evaluate Sufficient against the risk threshold using calibrated success evidence.
+   If missing, report `no_evidenced_sufficient_model`; Budget is then undetermined.
+   Do not derive retries by dividing by an uncalibrated benchmark score.
+6. Explain confidence dimensions and evidence gaps. Estimate cost/runtime p50/p90
+   only where evidence supports quantiles; otherwise label a scenario range.
+7. Compare optional decomposition with a monolithic baseline including coordination,
+   verification and failure costs. Record actual outcomes for later calibration.
+
+Use [TEMPLATE](TEMPLATE.md). This is a manual workflow in v0.1, not an API promise.