Add Flask web UI, Docker Compose, core engine + tests

- physcom core: CLI, 5-pass pipeline, SQLite repo, 37 tests
- physcom_web: Flask app with HTMX for entity/domain/pipeline/results CRUD
- Docker Compose: web + cli services sharing a named volume for the DB
- Clean up local settings to use wildcard permissions

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

src/physcom/llm/base.py

@@ -0,0 +1,25 @@
+"""Abstract LLM interface — provider-agnostic."""
+
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+
+
+class LLMProvider(ABC):
+    """Abstract LLM interface for physics estimation and plausibility review."""
+
+    @abstractmethod
+    def estimate_physics(
+        self, combination_description: str, metrics: list[str]
+    ) -> dict[str, float]:
+        """Given a natural-language description of a combination,
+        estimate raw metric values. Returns {metric_name: estimated_value}."""
+        ...
+
+    @abstractmethod
+    def review_plausibility(
+        self, combination_description: str, scores: dict[str, float]
+    ) -> str:
+        """Given a combination and its scores, return a natural-language
+        plausibility and novelty assessment."""
+        ...

src/physcom/llm/prompts.py

@@ -0,0 +1,37 @@
+"""Prompt templates for LLM-assisted passes."""
+
+PHYSICS_ESTIMATION_PROMPT = """\
+You are a physics estimation assistant. Given the following transportation concept, \
+estimate the requested metrics using order-of-magnitude physics reasoning.
+
+## Concept
+{description}
+
+## Metrics to estimate
+{metrics}
+
+## Instructions
+- Use real-world physics to estimate each metric.
+- If the concept is implausible, still provide your best estimate.
+- Return ONLY valid JSON mapping metric names to numeric values.
+- Example: {{"speed": 45.0, "cost_efficiency": 0.15, "safety": 0.7}}
+"""
+
+PLAUSIBILITY_REVIEW_PROMPT = """\
+You are reviewing a novel transportation concept for social and practical viability.
+
+## Concept
+{description}
+
+## Metric Scores
+{scores}
+
+## Instructions
+Review this concept for:
+1. Social viability — would people actually use this?
+2. Practical barriers — what engineering or regulatory obstacles exist?
+3. Novelty — does anything similar already exist?
+4. Overall plausibility — is this a genuinely interesting innovation or nonsense?
+
+Provide a concise 2-4 sentence assessment.
+"""

src/physcom/llm/providers/mock.py

@@ -0,0 +1,28 @@
+"""Mock LLM provider for testing."""
+
+from __future__ import annotations
+
+from physcom.llm.base import LLMProvider
+
+
+class MockLLMProvider(LLMProvider):
+    """Returns deterministic stub responses for testing."""
+
+    def __init__(self, default_estimates: dict[str, float] | None = None) -> None:
+        self._defaults = default_estimates or {}
+
+    def estimate_physics(
+        self, combination_description: str, metrics: list[str]
+    ) -> dict[str, float]:
+        result = {}
+        for metric in metrics:
+            result[metric] = self._defaults.get(metric, 0.5)
+        return result
+
+    def review_plausibility(
+        self, combination_description: str, scores: dict[str, float]
+    ) -> str:
+        avg = sum(scores.values()) / max(len(scores), 1)
+        if avg > 0.5:
+            return "This concept appears plausible and worth further investigation."
+        return "This concept has significant feasibility challenges."