Harden remote-ML routing + add address test suite

Wires up the regressions surfaced by a real PS 188 / Lower East Side
run on the AMD MI300X stack. Five fixes + one new probe:

1. Lift the `RIPRAP_HEAVY_SPECIALISTS` gate when remote ML is
reachable. The heavy specialists' GPU work (TerraMind LULC +
Buildings, Prithvi-live, register joins) runs on the droplet, so
the local wall-clock cost drops from ~60 s to ~5 s — gate the
default to ON so the public demo never silently disables them.

2. TTM specialists never crash with the
`ModuleNotFoundError("Could not import module 'PreTrainedModel'")`
that surfaced on local-fallback paths. Force-import
`PreTrainedModel` + `TinyTimeMixerForPrediction` before
`get_model()` so the transformers lazy-registry resolves the
class strings under FSM worker threads.

3. When remote ML is configured but returns non-ok, surface the
remote error rather than fall through to a brittle local model
load (`ttm_forecast`, `ttm_battery_surge`, `prithvi_live`). Local
fallback is reserved for transport-level `RemoteUnreachable`,
which is what a degraded droplet actually looks like.

4. Capstone meta-card reads the correct Mellea field names. The FSM
emits `requirements_passed` / `n_attempts` / `rerolls`; the card
adapter was reading `passed` / `attempts`, so the meta card
always rendered "0/0 0 rerolls" regardless of the real state.
Accepts both shapes for forward-compat.

5. New `scripts/probe_addresses.py` end-to-end test suite — drives
`/api/agent/stream` against a curated NYC address set
(442 E Houston / 80 Pioneer / 100 Gold / Hollis / Coney Island),
asserts every Stone fires (or fails to fire with a deterministic
reason) per intent, the briefing prose contains the four
sections (Policy context optional when no RAG hits land), Mellea
passes within attempt budget, and no specialist crashes with the
PreTrainedModel ModuleNotFoundError category. Writes
`outputs/probe_addresses.csv` + optional JSON dump. Exit 0 only
when 5/5 pass.

Local run with `RIPRAP_LLM_PRIMARY=vllm`,
`RIPRAP_ML_BACKEND=remote`, `RIPRAP_MELLEA_MAX_ATTEMPTS=3`:
5/5 pass, 5.8–13.1 s end-to-end, Mellea 4/4 grounding checks
passed on every address.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

app/flood_layers/prithvi_live.py

@@ -353,11 +353,16 @@ def fetch(lat: float, lon: float, timeout_s: float = 60.0) -> dict[str, Any]:
 
         # v0.4.5 — try the MI300X inference service first if configured.
         # On RemoteUnreachable (service down / not configured / 5xx) fall
-        # through to the local terratorch path. The 4-band slice the
-        # service expects is the same shape the local path uses.
+        # through to the local terratorch path. When remote is configured
+        # but returns non-ok we surface that signal directly: the local
+        # path on this machine has been brittle (v2 datamodule
+        # `test_transform=None` race), so a configured remote is more
+        # reliable than the fallback.
+        remote_attempted = False
         try:
             from app import inference as _inf
             if _inf.remote_enabled():
+                remote_attempted = True
                 remote = _inf.prithvi_pluvial(
                     img, scene_id=item.id,
                     scene_datetime=str(item.datetime),
@@ -381,10 +386,19 @@ def fetch(lat: float, lon: float, timeout_s: float = 60.0) -> dict[str, Any]:
                         "compute": f"remote · {remote.get('device', 'gpu')}",
                         "elapsed_s": round(time.time() - t0, 2),
                     }
+                return {"ok": False,
+                        "skipped": f"remote prithvi-pluvial non-ok: "
+                                   f"{remote.get('error') or 'unknown'}",
+                        "elapsed_s": round(time.time() - t0, 2)}
         except _inf.RemoteUnreachable as e:
             log.info("prithvi_live: remote unreachable (%s); falling back to local", e)
-        except Exception:
-            log.exception("prithvi_live: remote call failed; falling back to local")
+        except Exception as e:
+            log.exception("prithvi_live: remote call failed")
+            if remote_attempted:
+                return {"ok": False,
+                        "skipped": f"remote prithvi-pluvial error: "
+                                   f"{type(e).__name__}: {e}",
+                        "elapsed_s": round(time.time() - t0, 2)}
 
         # Local fallback — the path that's been live since v0.4.4.
         model, run_model = _ensure_model()

app/fsm.py

@@ -1035,9 +1035,26 @@ import os as _os  # noqa: E402
 # Default OFF on local-Ollama so the demo briefing returns in well under
 # 90 s. Enable explicitly with RIPRAP_HEAVY_SPECIALISTS=1 (e.g. on the
 # AMD-vLLM path, where the reconciler's ~5 s leaves room for the joins).
+#
+# Remote ML lift: when RIPRAP_ML_BACKEND=remote (or auto with a base URL
+# set) the heavy specialists' GPU work runs on the droplet, so the local
+# wall-clock cost drops from ~60 s to ~5 s. Default ON in that case so
+# the public demo never silently disables them.
+def _remote_ml_configured() -> bool:
+    backend = _os.environ.get("RIPRAP_ML_BACKEND", "auto").lower()
+    if backend == "local":
+        return False
+    return bool(_os.environ.get("RIPRAP_ML_BASE_URL", "").strip())
+
+
+_HEAVY_DEFAULT = (
+    "1" if (
+        _os.environ.get("RIPRAP_LLM_PRIMARY", "ollama").lower() != "ollama"
+        or _remote_ml_configured()
+    ) else "0"
+)
 _HEAVY_SPECIALISTS_ENABLED = _os.environ.get(
-    "RIPRAP_HEAVY_SPECIALISTS",
-    "0" if _os.environ.get("RIPRAP_LLM_PRIMARY", "ollama").lower() == "ollama" else "1",
+    "RIPRAP_HEAVY_SPECIALISTS", _HEAVY_DEFAULT,
 ).lower() in ("1", "true", "yes")
 
 

app/live/ttm_battery_surge.py

@@ -92,6 +92,10 @@ def _ensure_model():
         if _MODEL is not None:
             return _MODEL
         from huggingface_hub import snapshot_download
+        # Force-import dispatched class names so the transformers lazy
+        # registry can resolve `PreTrainedModel` / `TinyTimeMixerForPrediction`
+        # under FSM worker threads. Same pattern as ttm_forecast._load_model.
+        from transformers import PreTrainedModel  # noqa: F401
         from tsfm_public import TinyTimeMixerForPrediction
         log.info("ttm_battery_surge: downloading %s", REPO)
         local_dir = snapshot_download(REPO)
@@ -247,12 +251,17 @@ def fetch(timeout_s: float = 60.0) -> dict[str, Any]:
         # v0.4.5 — try the MI300X service first. The remote handles its
         # own model loading; if it's reachable we never need local
         # tsfm_public, which lets the HF Space drop the granite-tsfm
-        # bake from the image.
+        # bake from the image. When the remote is configured but returns
+        # non-ok we surface the remote error rather than try a local
+        # load — the local code path can ModuleNotFoundError on transient
+        # transformers-registry races and that's a worse user signal.
         forecast = None
         compute = "local"
+        remote_attempted = False
         try:
             from app import inference as _inf
             if _inf.remote_enabled():
+                remote_attempted = True
                 remote = _inf.ttm_forecast(
                     "fine_tune_battery", residuals.tolist(),
                     context_length=CONTEXT_LENGTH,
@@ -264,8 +273,20 @@ def fetch(timeout_s: float = 60.0) -> dict[str, Any]:
                     import numpy as np
                     forecast = np.asarray(remote["forecast"], dtype="float32")
                     compute = f"remote · {remote.get('device', 'gpu')}"
+                else:
+                    return {"available": False,
+                            "reason": f"remote ttm-forecast non-ok: "
+                                      f"{remote.get('error') or 'unknown'}",
+                            "elapsed_s": round(time.time() - t0, 2)}
         except _inf.RemoteUnreachable as e:
             log.info("ttm_battery_surge: remote unreachable (%s); local", e)
+        except Exception as e:
+            log.exception("ttm_battery_surge: remote call failed")
+            if remote_attempted:
+                return {"available": False,
+                        "reason": f"remote ttm-forecast error: "
+                                  f"{type(e).__name__}: {e}",
+                        "elapsed_s": round(time.time() - t0, 2)}
 
         if forecast is None:
             if not _DEPS_OK:

app/live/ttm_forecast.py

@@ -92,6 +92,15 @@ def _load_model(context_length: int = CONTEXT_LENGTH,
         return None
     try:
         import torch  # noqa: F401
+        # Force-import the registered class names BEFORE get_model so that
+        # transformers' lazy registry can resolve them by string. Without
+        # this, AutoModel-style dispatch raises
+        #   ModuleNotFoundError("Could not import module 'PreTrainedModel'")
+        # under the FSM worker thread (the lazy import path races with
+        # other model loads). See web/main.py startup for the same
+        # pre-import on the main thread.
+        from transformers import PreTrainedModel  # noqa: F401
+        from tsfm_public import TinyTimeMixerForPrediction  # noqa: F401
         from tsfm_public.toolkit.get_model import get_model
         m = get_model(
             "ibm-granite/granite-timeseries-ttm-r2",
@@ -191,14 +200,23 @@ def _run_ttm(history: np.ndarray,
     remote service stays a thin "given a series, give me a forecast"
     contract.
     """
+    global _MODEL_LOAD_ERROR
     mu = float(history.mean())
     sigma = float(history.std() + 1e-6)
     normed = (history - mu) / sigma
 
-    # Try remote first
+    # Try remote first. When remote is configured we bias HARD toward it:
+    # if the remote returns non-ok we surface that error rather than
+    # silently falling through to a local model load (which on cpu-basic
+    # surfaces would 502 with a cryptic transformers-internal
+    # ModuleNotFoundError). Local fallback is only used when the remote
+    # is unreachable (transport-level), which is what a degraded droplet
+    # actually looks like.
+    remote_attempted = False
     try:
         from app import inference as _inf
         if _inf.remote_enabled():
+            remote_attempted = True
             remote = _inf.ttm_forecast(
                 "zero_shot_battery", normed.tolist(),
                 context_length=context_length,
@@ -208,21 +226,40 @@ def _run_ttm(history: np.ndarray,
             if remote.get("ok"):
                 pred = np.asarray(remote["forecast"], dtype=np.float32)
                 return pred * sigma + mu
+            _MODEL_LOAD_ERROR = (
+                f"remote ttm-forecast returned non-ok: {remote.get('error') or remote}"
+            )
+            log.warning("TTM zero-shot: remote returned non-ok: %s", remote)
+            return None
     except _inf.RemoteUnreachable as e:
         log.info("TTM zero-shot: remote unreachable (%s); local fallback", e)
-    except Exception:
-        log.exception("TTM zero-shot remote call failed; local fallback")
+    except Exception as e:
+        log.exception("TTM zero-shot remote call failed: %r", e)
+        if remote_attempted:
+            _MODEL_LOAD_ERROR = f"remote ttm-forecast errored: {type(e).__name__}: {e}"
+            return None
 
-    # Local fallback
-    model = _load_model(context_length, prediction_length)
+    # Local fallback (only reached when remote isn't configured or is
+    # unreachable at the transport level).
+    try:
+        model = _load_model(context_length, prediction_length)
+    except Exception as e:
+        _MODEL_LOAD_ERROR = f"{type(e).__name__}: {e}"
+        log.exception("TTM model load raised: %r", e)
+        return None
     if model is None:
         return None
-    import torch
+    try:
+        import torch
+    except ImportError:
+        _MODEL_LOAD_ERROR = "torch not available on this deployment"
+        return None
     x = torch.from_numpy(normed.astype(np.float32))[None, :, None]
     try:
         with torch.no_grad():
             out = model(past_values=x)
     except Exception as e:
+        _MODEL_LOAD_ERROR = f"{type(e).__name__}: {e}"
         log.exception("TTM inference failed: %r", e)
         return None
     pred = out.prediction_outputs[0, :, 0].cpu().numpy()

scripts/probe_addresses.py

@@ -0,0 +1,417 @@
+"""Riprap end-to-end address test suite.
+
+Drives `/api/agent/stream` against a curated set of NYC addresses and
+asserts that every Stone fires (or fails to fire with a deterministic
+reason), the briefing prose contains all four sections, Mellea
+grounding passes within attempt budget, and no specialist crashes with
+an internal-API error (PreTrainedModel ModuleNotFoundError, etc).
+
+Designed to be runnable both locally (M3 → laptop) and against the
+deployed HF Space. The remote ML stack on the AMD MI300X is the same in
+both cases when the env is configured, so an address that passes here
+is the same address the hackathon judges will see.
+
+Usage:
+    .venv/bin/python scripts/probe_addresses.py
+    .venv/bin/python scripts/probe_addresses.py --base http://127.0.0.1:7860
+    .venv/bin/python scripts/probe_addresses.py \\
+        --base https://lablab-ai-amd-developer-hackathon-riprap-nyc.hf.space \\
+        --addresses "PS 188, Lower East Side"
+    .venv/bin/python scripts/probe_addresses.py --json outputs/probe_addresses.json
+
+Exit code 0 if every address passes every assertion; 1 otherwise. CSV
+goes to outputs/probe_addresses.csv; JSON dump (full payloads, useful
+for the UI dev loop) optionally to --json.
+"""
+from __future__ import annotations
+
+import argparse
+import csv
+import json
+import sys
+import time
+from collections import defaultdict
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any
+from urllib.parse import quote
+
+import httpx
+
+
+# Curated probe set. Each entry exercises a different surface of the
+# system; together they cover every Stone's specialists at least once.
+DEFAULT_ADDRESSES: list[dict[str, Any]] = [
+    # Anchor each entry on a fully-qualified street address so the
+    # geocoder doesn't drift to a same-named landmark in another borough
+    # (e.g. there are several "PS 188" schools city-wide).
+    {
+        "query": "442 East Houston Street, Manhattan",  # PS 188 LES
+        "intent": "single_address",
+        "expect_sandy": True,        # in the empirical 2012 extent
+        "expect_311_ge": 1,
+        "borough": "Manhattan",
+    },
+    {
+        "query": "80 Pioneer Street, Brooklyn",
+        "intent": "single_address",
+        "expect_sandy": True,        # Red Hook — canonical Sandy turf
+        "expect_311_ge": 1,
+        "borough": "Brooklyn",
+    },
+    {
+        "query": "100 Gold Street, Manhattan",
+        "intent": "single_address",
+        # Outside Sandy 2012; this is the negative-control address.
+        "expect_sandy": False,
+        "borough": "Manhattan",
+    },
+    {
+        "query": "Hollis, Queens",
+        "intent": "neighborhood",
+        "borough": "Queens",
+    },
+    {
+        "query": "Coney Island, Brooklyn",
+        "intent": "neighborhood",
+        # neighborhood intent doesn't surface a per-address sandy field
+        # in the final state — the briefing prose names the Sandy
+        # exposure narratively from RAG + DEP layers.
+        "borough": "Brooklyn",
+    },
+]
+
+
+@dataclass
+class StoneSummary:
+    fired: int = 0
+    errored: int = 0
+    silent: int = 0
+    total_seen: int = 0
+
+
+@dataclass
+class RunResult:
+    query: str
+    elapsed_s: float = 0.0
+    intent: str | None = None
+    paragraph: str = ""
+    n_steps: int = 0
+    steps: list[dict[str, Any]] = field(default_factory=list)
+    final: dict[str, Any] = field(default_factory=dict)
+    attempts: list[dict[str, Any]] = field(default_factory=list)
+    stones: dict[str, StoneSummary] = field(default_factory=lambda: defaultdict(StoneSummary))
+    errors: list[str] = field(default_factory=list)
+    error_steps: list[str] = field(default_factory=list)
+
+
+# Mapping mirrors web/sveltekit/src/lib/client/cardAdapter.ts:stoneForStep.
+# Kept here so the probe doesn't need to read the bundled JS.
+def _stone_for_step(step: str) -> str | None:
+    n = (step or "").lower()
+    if n in {"sandy_inundation", "dep_stormwater", "ida_hwm_2021",
+             "prithvi_eo_v2", "microtopo_lidar"}:
+        return "cornerstone"
+    if n in {"mta_entrance_exposure", "nycha_development_exposure",
+             "doe_school_exposure", "doh_hospital_exposure",
+             "terramind_synthesis", "terramind_buildings", "eo_chip_fetch"}:
+        return "keystone"
+    if n in {"floodnet", "nyc311", "nws_obs", "noaa_tides",
+             "prithvi_eo_live", "terramind_lulc"}:
+        return "touchstone"
+    if n in {"nws_alerts", "ttm_forecast", "ttm_311_forecast",
+             "floodnet_forecast", "ttm_battery_surge"}:
+        return "lodestone"
+    if n.startswith("reconcile") or n.startswith("mellea") or \
+            n in {"rag_granite_embedding", "gliner_extract"}:
+        return "capstone"
+    return None
+
+
+def stream_one(query: str, base: str, timeout_s: float) -> RunResult:
+    """Drive one SSE run, accumulate every event into a RunResult."""
+    url = f"{base}/api/agent/stream?q={quote(query)}"
+    res = RunResult(query=query)
+    t0 = time.time()
+    paragraph = ""
+
+    with httpx.stream("GET", url, timeout=timeout_s) as r:
+        r.raise_for_status()
+        ev = None
+        buf: list[str] = []
+        for line in r.iter_lines():
+            if line.startswith("event:"):
+                ev = line.split(":", 1)[1].strip()
+            elif line.startswith("data:"):
+                buf.append(line[5:].lstrip())
+            elif line == "":
+                if not (ev and buf):
+                    ev = None
+                    buf = []
+                    continue
+                data = "\n".join(buf)
+                buf = []
+                try:
+                    payload = json.loads(data)
+                except json.JSONDecodeError:
+                    payload = {"_raw": data}
+                if ev == "plan":
+                    res.intent = payload.get("intent")
+                elif ev == "step":
+                    res.n_steps += 1
+                    res.steps.append(payload)
+                    stone = _stone_for_step(payload.get("step", ""))
+                    if stone:
+                        s = res.stones[stone]
+                        s.total_seen += 1
+                        if not payload.get("ok"):
+                            s.errored += 1
+                            res.error_steps.append(payload.get("step", ""))
+                        elif payload.get("result") is None and payload.get("err") is None:
+                            s.silent += 1
+                        else:
+                            s.fired += 1
+                elif ev == "token":
+                    paragraph += payload.get("delta") or ""
+                elif ev == "mellea_attempt":
+                    res.attempts.append(payload)
+                elif ev == "final":
+                    res.final = payload
+                    if isinstance(payload.get("paragraph"), str):
+                        paragraph = payload["paragraph"]
+                elif ev == "error":
+                    res.errors.append(str(payload.get("err") or payload))
+                ev = None
+    res.elapsed_s = round(time.time() - t0, 2)
+    res.paragraph = paragraph
+    return res
+
+
+# ---- Assertions ----------------------------------------------------------
+
+# Flag step-result errors that look like the local-fallback ModuleNotFoundError
+# we just hardened against. If any address surfaces this string, the
+# guard-rail regressed.
+_ERROR_REGRESSIONS = (
+    "ModuleNotFoundError",
+    "Could not import module 'PreTrainedModel'",
+)
+
+# Briefing section headings the system prompt teaches Granite to emit.
+# Granite's exact rendering varies per attempt — sometimes
+# `**Status.**` on its own line, sometimes inline. We treat each section
+# as present if its label appears at all (case-insensitive).
+#
+# The system prompt says "Omit any section whose supporting facts are
+# absent from the documents" — so on a query with no RAG hits the
+# Policy-context section is correctly skipped. We require Status +
+# Empirical evidence + Modeled scenarios always; Policy context is
+# best-effort.
+_REQUIRED_HEADINGS = (
+    "Status",
+    "Empirical evidence",
+    "Modeled scenarios",
+)
+_OPTIONAL_HEADINGS = ("Policy context",)
+
+
+def assert_run(spec: dict[str, Any], r: RunResult) -> list[str]:
+    """Return a list of failures (empty list if the run passes)."""
+    fails: list[str] = []
+    if r.errors:
+        fails.append(f"stream errors: {r.errors}")
+
+    # Specialist regressions — the LOD-002/003/004 ModuleNotFoundError
+    # category. If any step result string contains those keywords we
+    # treat it as a hard regression of the pre-import hardening.
+    for step in r.steps:
+        err = step.get("err") or ""
+        for marker in _ERROR_REGRESSIONS:
+            if marker in str(err):
+                fails.append(
+                    f"{step.get('step')}: {marker} regressed in step error"
+                )
+
+    # Intent classification.
+    expected_intent = spec.get("intent")
+    if expected_intent and r.intent and r.intent != expected_intent:
+        fails.append(f"intent={r.intent} expected {expected_intent}")
+
+    # Briefing presence.
+    if not r.paragraph or len(r.paragraph) < 200:
+        fails.append(f"briefing too short: {len(r.paragraph)} chars")
+    else:
+        para_lower = r.paragraph.lower()
+        for heading in _REQUIRED_HEADINGS:
+            if heading.lower() not in para_lower:
+                fails.append(f"briefing missing heading {heading!r}")
+
+    # Mellea grounding.
+    final = r.final or {}
+    m = final.get("mellea") or {}
+    passed = m.get("requirements_passed") or []
+    total = m.get("requirements_total") or 0
+    if total:
+        if len(passed) < total:
+            failed_names = ",".join(m.get("requirements_failed") or []) or "?"
+            fails.append(
+                f"mellea: only {len(passed)}/{total} grounding checks passed "
+                f"(failed: {failed_names})"
+            )
+    elif r.attempts:
+        last = r.attempts[-1]
+        if last.get("failed"):
+            fails.append(f"mellea: last attempt failed {last['failed']}")
+
+    # Stones — the per-stone requirement is intent-dependent. The
+    # single_address FSM fires every stone's specialists (Cornerstone /
+    # Keystone / Touchstone / Lodestone). The neighborhood and
+    # development_check intents have a smaller fixed surface that does
+    # not exercise the address-level register / live-now stones — they
+    # rely on RAG + a smaller set of specialists. So we only enforce
+    # the full Stone roster for single_address; for the others we just
+    # check Capstone fires (RAG / GLiNER / reconcile are universal).
+    intent = (r.intent or expected_intent or "single_address").lower()
+    if intent == "single_address":
+        for stone in ("cornerstone", "touchstone", "lodestone"):
+            s = r.stones.get(stone)
+            if not s or s.fired == 0:
+                fails.append(
+                    f"{stone}: 0 specialists fired "
+                    f"(saw {s.total_seen if s else 0})"
+                )
+        s = r.stones.get("keystone")
+        if not s or s.total_seen == 0:
+            fails.append("keystone: no specialists attempted")
+    s = r.stones.get("capstone")
+    if not s or s.fired == 0:
+        fails.append(
+            f"capstone: 0 fired — reconcile/rag/gliner step events missing "
+            f"(saw {s.total_seen if s else 0})"
+        )
+
+    # Spec-driven asserts (only meaningful for single_address — the
+    # neighborhood / development_check intents have no per-address
+    # sandy / 311 fields in the final state).
+    if intent == "single_address":
+        sandy_state = (final.get("sandy") is True)
+        if "expect_sandy" in spec:
+            want = spec["expect_sandy"]
+            if sandy_state is not want:
+                fails.append(f"sandy={sandy_state} expected {want}")
+        n311 = (final.get("nyc311") or {}).get("n") or 0
+        if "expect_311_ge" in spec and n311 < spec["expect_311_ge"]:
+            fails.append(f"nyc311={n311} expected >= {spec['expect_311_ge']}")
+
+    return fails
+
+
+# ---- Entry point ---------------------------------------------------------
+
+def main() -> int:
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--base", default="http://127.0.0.1:7860",
+                    help="Riprap server base URL")
+    ap.add_argument("--addresses", default="",
+                    help="Pipe-separated subset of queries to run "
+                         "(addresses themselves contain commas, so pipe is "
+                         "the separator); default runs the full curated set")
+    ap.add_argument("--timeout", type=float, default=600.0)
+    ap.add_argument("--out", default="outputs/probe_addresses.csv")
+    ap.add_argument("--json", default="",
+                    help="Optional path to dump full per-address JSON payload")
+    args = ap.parse_args()
+
+    if args.addresses:
+        wanted = {a.strip() for a in args.addresses.split("|") if a.strip()}
+        specs = [s for s in DEFAULT_ADDRESSES if s["query"] in wanted]
+        if not specs:
+            specs = [{"query": q} for q in wanted]
+    else:
+        specs = list(DEFAULT_ADDRESSES)
+
+    Path(args.out).parent.mkdir(parents=True, exist_ok=True)
+
+    summary_rows: list[dict[str, Any]] = []
+    full: list[dict[str, Any]] = []
+    all_pass = True
+
+    print(f"Probing {len(specs)} addresses against {args.base}")
+    print()
+
+    for i, spec in enumerate(specs, 1):
+        q = spec["query"]
+        print(f"[{i}/{len(specs)}] {q!r:50s}", end="  ", flush=True)
+        try:
+            r = stream_one(q, args.base, args.timeout)
+        except Exception as e:
+            print(f"STREAM ERROR: {type(e).__name__}: {e}")
+            summary_rows.append({"query": q, "ok": False,
+                                  "fails": f"stream raised: {e}"})
+            all_pass = False
+            continue
+        fails = assert_run(spec, r)
+        ok = not fails
+        all_pass &= ok
+        m = (r.final or {}).get("mellea") or {}
+        passed = m.get("requirements_passed") or []
+        rerolls = m.get("rerolls") if m.get("rerolls") is not None else \
+            (max(0, (m.get("n_attempts") or 1) - 1))
+        verdict = "PASS" if ok else "FAIL"
+        print(f"{verdict}  {r.elapsed_s:6.1f}s  "
+              f"steps={r.n_steps} prose={len(r.paragraph)}c "
+              f"mellea={len(passed)}/{m.get('requirements_total') or '?'} "
+              f"rerolls={rerolls}")
+        for f in fails:
+            print(f"        - {f}")
+
+        summary_rows.append({
+            "query": q, "ok": ok, "elapsed_s": r.elapsed_s, "intent": r.intent,
+            "n_steps": r.n_steps,
+            "para_chars": len(r.paragraph),
+            "mellea_passed": len(passed),
+            "mellea_total": m.get("requirements_total") or 0,
+            "rerolls": rerolls,
+            "stones_fired": ",".join(
+                f"{k}={v.fired}" for k, v in sorted(r.stones.items())),
+            "stones_errored": ",".join(
+                f"{k}={v.errored}" for k, v in sorted(r.stones.items())
+                if v.errored),
+            "errored_steps": ",".join(r.error_steps),
+            "fails": " | ".join(fails),
+        })
+        full.append({
+            "spec": spec,
+            "elapsed_s": r.elapsed_s,
+            "intent": r.intent,
+            "paragraph": r.paragraph,
+            "stones": {k: vars(v) for k, v in r.stones.items()},
+            "mellea": m,
+            "attempts": r.attempts,
+            "errors": r.errors,
+            "error_steps": r.error_steps,
+            "fails": fails,
+        })
+
+    out_path = Path(args.out)
+    if summary_rows:
+        with out_path.open("w", newline="") as f:
+            w = csv.DictWriter(f, fieldnames=list(summary_rows[0].keys()))
+            w.writeheader()
+            w.writerows(summary_rows)
+        print(f"\nWrote {out_path}")
+    if args.json:
+        json_path = Path(args.json)
+        json_path.parent.mkdir(parents=True, exist_ok=True)
+        json_path.write_text(json.dumps(full, indent=2, default=str))
+        print(f"Wrote {json_path}")
+
+    print()
+    print("=" * 70)
+    print(f"  {sum(1 for r in summary_rows if r.get('ok'))}/{len(summary_rows)} addresses passed")
+    print("=" * 70)
+    return 0 if all_pass else 1
+
+
+if __name__ == "__main__":
+    sys.exit(main())

web/sveltekit/build/200.html

@@ -6,16 +6,16 @@
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="description" content="Riprap — citation-grounded NYC flood-exposure briefings." />
     <title>Riprap — flood-exposure briefing</title>
-    <link href="/_app/immutable/entry/start.DhVcDgd2.js" rel="modulepreload">
-		<link href="/_app/immutable/chunks/DXzi87nJ.js" rel="modulepreload">
+    <link href="/_app/immutable/entry/start.DcfT94lp.js" rel="modulepreload">
+		<link href="/_app/immutable/chunks/D9Zf6nF7.js" rel="modulepreload">
 		<link href="/_app/immutable/chunks/R1l3q-hJ.js" rel="modulepreload">
-		<link href="/_app/immutable/entry/app.DbHmO7Py.js" rel="modulepreload">
+		<link href="/_app/immutable/entry/app.qmZS3WSP.js" rel="modulepreload">
 		<link href="/_app/immutable/chunks/DZRTzx66.js" rel="modulepreload">
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 		<link href="/_app/immutable/chunks/C6-4B-da.js" rel="modulepreload">
-		<link href="/_app/immutable/nodes/0.BkCp5U8o.js" rel="modulepreload">
+		<link href="/_app/immutable/nodes/0.IL53jLg5.js" rel="modulepreload">
 		<link href="/_app/immutable/chunks/Bu_bEyoS.js" rel="modulepreload">
-		<link href="/_app/immutable/chunks/BHheE8H_.js" rel="modulepreload">
+		<link href="/_app/immutable/chunks/DP_9AkkW.js" rel="modulepreload">
 		<link href="/_app/immutable/chunks/a4L4UsYq.js" rel="modulepreload">
 		<link href="/_app/immutable/chunks/B_9mIQpm.js" rel="modulepreload">
 		
@@ -25,15 +25,15 @@
     <div style="display: contents">
 			<script>
 				{
-					__sveltekit_7ykx85 = {
+					__sveltekit_186n2ot = {
 						base: ""
 					};
 
 					const element = document.currentScript.parentElement;
 
 					Promise.all([
-						import("/_app/immutable/entry/start.DhVcDgd2.js"),
-						import("/_app/immutable/entry/app.DbHmO7Py.js")
+						import("/_app/immutable/entry/start.DcfT94lp.js"),
+						import("/_app/immutable/entry/app.qmZS3WSP.js")
 					]).then(([kit, app]) => {
 						kit.start(app, element);
 					});

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web/sveltekit/build/_app/immutable/chunks/{BHheE8H_.js → DP_9AkkW.js}

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web/sveltekit/build/_app/immutable/entry/{app.DbHmO7Py.js → app.qmZS3WSP.js}

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web/sveltekit/build/_app/immutable/entry/start.DcfT94lp.js

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web/sveltekit/build/_app/immutable/entry/start.DhVcDgd2.js

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web/sveltekit/build/_app/immutable/nodes/{1.CQ4nY8y7.js → 1.4zkFLEZX.js}

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web/sveltekit/build/_app/immutable/nodes/{2.4m0D4KZ3.js → 2.C4OcOHhN.js}

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n=je(),u=c(n);ce(u,l=>E(t,l),()=>d(t)),M(2),o(n),m(e,n),P()}var _e=w(`<section class="land-section svelte-1anw2jf"><div class="land-section-head svelte-1anw2jf"><span class="section-label">What you'll get back</span> <span class="land-section-meta svelte-1anw2jf">A grounded paragraph with citations, not a chatbot answer.</span></div> <div class="land-preview-grid svelte-1anw2jf"><div class="land-preview-pane land-preview-pane-excerpt svelte-1anw2jf"><div class="land-preview-eyebrow svelte-1anw2jf">Briefing excerpt</div> <p class="land-preview-body svelte-1anw2jf">The lot sits inside the FEMA <span class="land-preview-cite svelte-1anw2jf">1% AE flood zone <sup class="svelte-1anw2jf">[c3]</sup></span>,
         with Sandy high-water marks recorded <span class="land-preview-cite svelte-1anw2jf">4.7 ft above grade <sup class="svelte-1anw2jf">[c1]</sup></span>.
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This is a historical fact, not a model prediction.",docId:"sandy",citeId:"sandy",mapLayer:"sandy"}}function pn(n){const e=I(n.dep);if(!e)return null;const t=[];for(const[r,s]of Object.entries(e)){const d=I(s);if(!d)continue;const o=b(d.depth_class)??0;o<=0||t.push([r.replace("dep_",""),T(d.depth_label)??"—",`class ${o}`])}return t.length?{id:"fsm-dep",stone:"cornerstone",tier:"modeled",variant:"tabular",source:"NYC DEP",agency:"NYC Department of Environmental Protection · Stormwater Flood Maps",vintage:"2021",title:"Stormwater flood scenarios at this address",columns:["scenario","depth label","class"],rows:t,sub:`${t.length} scenario${t.length===1?"":"s"} place this lot in modeled flooding`,docId:"dep_stormwater",citeId:"dep",mapLayer:"stormwater"}:null}function mn(n){const e=I(n.ida_hwm);if(!e)return null;const t=b(e.n_within_radius);if(!t||t<=0)return null;const r=[];return r.push(["count",`${t}`,`${b(e.radius_m)??800} m radius`]),b(e.max_height_above_gnd_ft)!=null&&r.push(["max above gnd",`${e.max_height_above_gnd_ft} ft`,"—"]),b(e.nearest_dist_m)!=null&&r.push(["nearest",T(e.nearest_site)??"HWM",`${e.nearest_dist_m} m`]),{id:"fsm-ida-hwm",stone:"cornerstone",tier:"empirical",variant:"tabular",source:"USGS",agency:"USGS STN Hurricane Ida 2021 high-water marks (Event 312)",vintage:"2021-09",title:"Hurricane Ida 2021 high-water marks nearby",columns:["field","value","context"],rows:r,docId:"ida_hwm",citeId:"ida_hwm",mapLayer:"hwm"}}function fn(n){const e=I(n.prithvi_water);if(!e)return null;const t=b(e.nearest_distance_m);return t==null?null:{id:"fsm-prithvi-water",stone:"cornerstone",tier:"modeled",variant:"raster",source:"Prithvi-EO 2.0",agency:"IBM/NASA Prithvi-EO 2.0 · baked Hurricane Ida 2021 polygons",vintage:"2021-09-02",title:"Hurricane Ida 2021 — satellite-attributable inundation",rasterKind:"prithvi",headline:e.inside_water_polygon?"Inside polygon":`${t} m away`,subhead:"pre/post HLS Sentinel-2 segmentation",sub:`${b(e.n_polygons_within_500m)??0} distinct polygons within 500 m`,docId:"prithvi_water",citeId:"prithvi_water",mapLayer:"prithvi"}}function _n(n){const e=I(n.microtopo);if(!e)return null;const t=b(e.point_elev_m);if(t==null)return null;const r=[{value:`${t.toFixed(1)} m`,label:"elevation"}];return b(e.hand_m)!=null&&r.push({value:`${e.hand_m.toFixed(1)} m`,label:"HAND"}),b(e.twi)!=null&&r.push({value:`${e.twi.toFixed(1)}`,label:"TWI"}),b(e.rel_elev_pct_200m)!=null&&r.push({value:`${e.rel_elev_pct_200m}%`,label:"pct lower 200m"}),{id:"fsm-microtopo",stone:"cornerstone",tier:"proxy",variant:"scalars",source:"USGS 3DEP",agency:"USGS 3DEP DEM (LiDAR-derived) + whitebox-workflows hydrology",vintage:"2018",title:"Microtopography at this point",scalars:r,sub:"Lower percentile = topographic low point; runoff routes here.",docId:"microtopo",citeId:"microtopo"}}function hn(n){const e=[],t=I(n.mta_entrances);if(t!=null&&t.available&&Array.isArray(t.entrances))for(const o of t.entrances.slice(0,4))e.push({reg:"MTA",tier:"empirical",label:T(o.station_name)??T(o.entrance_id)??"entrance",detail:`${b(o.distance_m)??"—"} m · ${T(o.daytime_routes)??""}`.trim(),sourceId:T(o.station_id)??"MTA",note:null});else t&&t.available===!1&&e.push({reg:"MTA",tier:"empirical",label:null,detail:null,sourceId:null,note:"no subway entrances within 1.0 mi (silent)"});const r=I(n.nycha_developments);if(r!=null&&r.available&&Array.isArray(r.developments))for(const o of r.developments.slice(0,3))e.push({reg:"NYCHA",tier:"empirical",label:T(o.development)??"development",detail:`${b(o.distance_m)??"—"} m · ${T(o.borough)??""}`.trim(),sourceId:T(o.tds_num)??null,note:null});else r&&r.available===!1&&e.push({reg:"NYCHA",tier:"empirical",label:null,detail:null,sourceId:null,note:"no NYCHA developments within 1.0 mi (silent)"});const s=I(n.doe_schools);if(s!=null&&s.available&&Array.isArray(s.schools))for(const o of s.schools.slice(0,3))e.push({reg:"DOE",tier:"empirical",label:T(o.loc_name)??"school",detail:`${b(o.distance_m)??"—"} m · ${T(o.borough)??""}`.trim(),sourceId:T(o.loc_code)??null,note:null});else s&&s.available===!1&&e.push({reg:"DOE",tier:"empirical",label:null,detail:null,sourceId:null,note:"no schools within 1.0 mi (silent)"});const d=I(n.doh_hospitals);if(d!=null&&d.available&&Array.isArray(d.hospitals))for(const o of d.hospitals.slice(0,3))e.push({reg:"DOH",tier:"empirical",label:T(o.facility_name)??"hospital",detail:`${b(o.distance_m)??"—"} m · ${T(o.borough)??""}`.trim(),sourceId:T(o.fac_id)??null,note:null});else d&&d.available===!1&&e.push({reg:"DOH",tier:"empirical",label:null,detail:null,sourceId:null,note:"no acute-care hospital within 1.0 mi (silent)"});return e.length?{id:"fsm-registers",stone:"keystone",tier:"empirical",variant:"register",source:"NYC OpenData",agency:"NYC OpenData · multi-agency join",vintage:X,title:"Nearby exposed assets",registers:e,sub:`${e.filter(o=>o.label).length} of ${e.length} registers fired · joined within 1.0 mi`,docId:"registers",citeId:"registers",mapLayer:"registers"}:null}function vn(n){const e=I(n.terramind_buildings);return e!=null&&e.ok?{id:"fsm-tm-buildings",stone:"keystone",tier:"modeled",variant:"raster-pred",source:"TerraMind-NYC",agency:"msradam/TerraMind-NYC-Adapters · Buildings LoRA",vintage:"2026",title:"NYC building footprints — TerraMind LoRA",rasterKind:"buildings",headline:`${b(e.pct_buildings)??0}%`,subhead:"building-footprint coverage in chip",sub:`${b(e.n_building_components)??0} distinct components · test mIoU 0.5511`,illustrative:!0,docId:"tm_buildings",citeId:"tm_buildings",mapLayer:"buildings"}:null}function gn(n){const e=I(n.floodnet);if(!e||(b(e.n_sensors)??0)<=0)return null;const t=b(e.n_flood_events_3y)??0;return{id:"fsm-floodnet",stone:"touchstone",tier:"empirical",variant:"spark",source:"FloodNet",agency:"FloodNet NYC ultrasonic depth sensor network",vintage:"2026",title:"FloodNet sensors near this address",headline:`${t} events`,subhead:`${b(e.n_sensors)??0} sensors · last 3 y`,spark:Array.from({length:24},(r,s)=>Math.max(0,Math.round(t/24*1.4*Math.exp(-Math.pow((s-14)/4,2))+t/24))),sparkSub:"Above-curb depth events ≥ 2 cm. Synthetic monthly distribution; raw deployment-id history is in the audit panel.",docId:"floodnet",citeId:"floodnet",mapLayer:"floodnet"}}function yn(n){var p;const e=I(n.nyc311);if(!e)return null;const t=b(e.n)??0;if(t<=0)return null;const r=I(e.by_year),s=I(e.by_descriptor),d=r?Object.values(r).map(f=>b(f)??0):Array.from({length:12},()=>Math.round(t/12)),o=s?(p=Object.entries(s).sort((f,l)=>(b(l[1])??0)-(b(f[1])??0))[0])==null?void 0:p[0]:null;return{id:"fsm-311",stone:"touchstone",tier:"proxy",variant:"histogram",source:"NYC 311",agency:"NYC 311 service requests (Socrata erm2-nwe9)",vintage:X,title:"Recent 311 flood complaints",headline:`${t} calls`,subhead:o?`top descriptor: ${o}`:"all flood-related descriptors",histogram:d,sparkSub:`Within ${b(e.radius_m)??200} m · ${b(e.years)??5} y window. 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a(Ge)},get linkedKey(){return a(ee)}});var m=N(_,2);Gt(m,{get active(){return a(ae)},get featureCounts(){return a(E)},onToggle:$=>v(ae,{...a(ae),[$]:!a(ae)[$]},!0)}),y(i),F(u,i)};W(Ct,u=>{a(J)&&u(It)})}y(Le);var Ze=N(Le,2),Mt=w(Ze);Bt(Mt,{get citations(){return a(se)}}),y(Ze),y(Ve),y(Me);var Je=N(Me,2),Xe=w(Je),qt=w(Xe);Ht(qt,{get data(){return a(ue)},density:$e,provenanceMode:Te,get showGrammar(){return a(de)},get linkedKey(){return a(ee)},onLink:mt,onCite:ft}),y(Xe),y(Je),y(We),y(Ie),Z(u=>Y(St,u),[()=>a(r)()]),F(n,Ie),Ot()}export{fr as component,mr as universal};

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-						import("../_app/immutable/entry/app.DbHmO7Py.js")
+						import("../_app/immutable/entry/start.DcfT94lp.js"),
+						import("../_app/immutable/entry/app.qmZS3WSP.js")
 					]).then(([kit, app]) => {
 						kit.start(app, element, {
 							node_ids: [0, 5],

web/sveltekit/src/lib/client/cardAdapter.ts

@@ -572,16 +572,27 @@ function buildNwsAlerts(state: Final): Card | null {
 
 function buildCapstoneMeta(final: FinalResult, wallSeconds?: number): Card {
   // v0.4.5 §2 — wire the four metrics to the reconciler's actual state.
-  // The Mellea attempts field is one-indexed by the reconciler (initial
-  // attempt + N rerolls). A clean run with one reroll arrives as
-  // attempts=2, which we surface as "1 reroll" — the human-meaningful
-  // count. attempts=1 renders as "0 rerolls" (initial pass only).
-  const m = final.mellea;
-  const passed = m?.passed?.length ?? 0;
-  const failed = m?.failed?.length ?? 0;
-  const totalChecks = passed + failed > 0 ? passed + failed : 4;
-  const attempts = m?.attempts ?? 0;
-  const rerolls = Math.max(0, attempts - 1);
+  // The FSM emits `mellea` as `{ rerolls, n_attempts, requirements_passed,
+  // requirements_failed, requirements_total }` (the mellea_validator
+  // shape). Earlier UI types used `{ passed, failed, attempts }`; we
+  // accept both so cards keep rendering across backend versions.
+  const m = (final.mellea ?? {}) as Record<string, unknown>;
+  const passedArr = Array.isArray(m.requirements_passed)
+    ? (m.requirements_passed as unknown[])
+    : Array.isArray(m.passed) ? (m.passed as unknown[]) : [];
+  const failedArr = Array.isArray(m.requirements_failed)
+    ? (m.requirements_failed as unknown[])
+    : Array.isArray(m.failed) ? (m.failed as unknown[]) : [];
+  const passed = passedArr.length;
+  const failed = failedArr.length;
+  const totalChecks = (typeof m.requirements_total === 'number'
+    ? (m.requirements_total as number)
+    : (passed + failed)) || 4;
+  const attempts = (typeof m.n_attempts === 'number'
+    ? (m.n_attempts as number)
+    : (typeof m.attempts === 'number' ? (m.attempts as number) : 0));
+  const rerollsField = typeof m.rerolls === 'number' ? (m.rerolls as number) : null;
+  const rerolls = rerollsField ?? Math.max(0, attempts - 1);
   const cites = final.citations?.length ?? 0;
   return {
     id: 'fsm-capstone-meta',