docs(milestone): complete v0.1 foundation

---ci---
phase: 0
milestone: v0.1
status: complete
---/ci---

scenarios/cost_rates.yaml

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+# Praxis v0.1 cost rates — per-unit pricing for the cost logging (D-012, REQ-NFR-COST-01).
+# v0.1 logs actual per-session cost; no enforced ceiling (pilot).
+# Per G-005: these are pilot-config rates (Ollama tier + cloud), NOT at-scale
+# per-learner unit economics — the $3/learner target requires self-hosted
+# gemma4:e4b + Piper (post-pilot).
+
+# LLM role-play (gemma4:cloud) — Ollama tier (Pro plan amortized, pilot estimate).
+gemma4_cloud_per_1k_tokens_cents: 0.5
+
+# Debrief + classifier (deepseek-v4-flash:cloud) — Ollama tier.
+deepseek_v4_flash_per_1k_tokens_cents: 1.0
+
+# ASR (Deepgram Nova-3 streaming) — $0.0043/min → 0.43 cents/min.
+deepgram_per_audio_minute_cents: 0.43
+
+# TTS (Cartesia Sonic cloud) — per-char pricing (pilot estimate).
+cartesia_per_1k_chars_cents: 3.0
+
+# TTS (Piper self-hosted) — open-weights, $0 marginal cost.
+piper_per_1k_chars_cents: 0.0

scenarios/customer_service_refund_ca_v01.yaml

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+# Praxis v0.1 scenario — Customer Service refund role-play (D-010, D-018).
+# One branch point: accept_resolution vs escalate (D-010).
+# failure_mode present (D-009 — not provoked in v0.1).
+# Debrief via deepseek-v4-flash:cloud no_think (D-020).
+
+id: cs_refund_ca_v01
+path: customer_service
+market: CA
+language: en-CA
+title: "Angry customer requesting refund on a damaged product"
+difficulty: 1
+failure_mode: escalates_unresolved   # D-009: present, not provoked in v0.1
+
+persona:
+  voice_id: "cartesia:a3536a36-1d18-4efb-a95a-7c44b7b5e384"   # D-006: same voice as mentor
+  character: "Customer (Jordan)"
+
+setup:
+  system_prompt: |
+    You are Jordan, a customer who received a damaged product.
+    You are frustrated but not abusive. You want a refund.
+    Stay in character. Do not break role.
+    Keep responses concise for voice (1-3 sentences).
+    Do not give legal, financial, or medical advice.
+    Do not impersonate a real employee of any actual company.
+  opening_line: "Hi, I received my order yesterday and the item is cracked. I want my money back."
+
+success_criteria:
+  - "Acknowledged the customer's frustration empathetically"
+  - "Offered a concrete resolution (refund or replacement)"
+  - "Confirmed next steps"
+
+common_mistakes:
+  - "Jumping to policy before acknowledging emotion"
+  - "Using jargon ('RMA', 'SLA')"
+  - "Getting defensive about the company"
+
+branches:
+  - id: accept_resolution
+    trigger:
+      learner_signals: ["empathy", "concrete_resolution", "next_steps"]
+    outcome: success
+    debrief_focus: "What you did well — you acknowledged the customer's frustration and offered a concrete resolution."
+
+  - id: escalate
+    trigger:
+      learner_signals: ["defensive", "policy_first", "no_acknowledgement"]
+    outcome: failure
+    failure_mode: escalates_unresolved
+    debrief_focus: "The customer escalated because they felt unheard. You led with policy before acknowledging their frustration."
+
+debrief:
+  model: deepseek-v4-flash:cloud
+  mode: no_think   # D-020: latency
+  prompt_template: debrief/default