P0 falsification harness (H1-H4) + Forgejo CI wiring

BUILD_LOG.md

@@ -36,8 +36,10 @@ VERIFIED (real execution, not claims)
 OUTSTANDING (requires USER action — external)
 =====================================================================
 - DNS: add A records for api.mangoopsdesign.com and git.mangoopsdesign.com -> 177.7.40.244.
-  Once they resolve publicly, Caddy auto-issues Let's Encrypt certs and the edge goes live
-  on real TLS. Until then, Caddy logs ACME NXDOMAIN errors (harmless; retries).
+  DONE 2026-08-07. Caddy auto-issued Let's Encrypt certs; edge verified live over real TLS
+  (api./health -> ok; git./ -> 200 Forgejo; POST /events over public TLS -> recorded).
+  Note: the box's own local resolver cached the old NXDOMAIN briefly; external clients
+  (public DNS) reach the edge correctly. No action needed.
 - Secrets: .env uses dev passwords. Before any real tenant, set strong PG/vep_app secrets
   and consider moving Forgejo to postgres at P6.
 - P0 falsification (gst_p0_falsification.md) still not run — per the spine, P0 should have

compose.yml

@@ -36,7 +36,7 @@ services:
   forgejo:
     image: codeberg.org/forgejo/forgejo:10.0.3
     environment:
-      FORGEJO__database__DB_TYPE: sqlite
+      FORGEJO__database__DB_TYPE: sqlite3
       FORGEJO__server__SSH_PORT: 22
       FORGEJO__server__ROOT_URL: https://git.mangoopsdesign.com
       FORGEJO__server__APP_DATA_PATH: /data

scripts/p0/run.py

@@ -0,0 +1,161 @@
+# VEP — Phase 0 Falsification Harness (GST reference tenant)
+#
+# PURPOSE
+# Run the four load-bearing GST hypotheses (H1-H4) against real data and emit a
+# KILL / KEEP / REVISE verdict per the gst_p0_falsification.md worksheet.
+# This is the gate that, per the spine, should have preceded the P1 build. Since P1
+# exists, this harness lets the gate run now: supply data -> get verdicts -> prune or keep.
+#
+# DESIGN RULES (spine-consistent)
+#  - No fabricated numbers. If data is missing, the verdict is INSUFFICIENT, never a guess.
+#  - Each hypothesis names its falsify-condition and the data column(s) it needs.
+#  - Verdicts are explainable: every KILL cites the observed number that killed it.
+#
+# USAGE
+#   python scripts/p0/run.py --data-dir scripts/p0/sample_data
+# Data files expected (see templates in sample_data/README):
+#   signups.csv        : email, intent_bucket, converted_to_foundation(bool), date
+#   cohort_jan2026.csv : attendee, bought_ageless(bool), upgraded_tier2(bool)
+#   subscribers.csv    : sub_id, tier, practice_signal(0-1), continued(bool), purchase_recency_days
+#   capped_clients.csv : client_id, revenue_stream, converted_to_scalable(bool)
+#
+# With no data the harness reports INSUFFICIENT for every hypothesis (honest default).
+
+from __future__ import annotations
+import argparse, csv, json, os, sys
+from dataclasses import dataclass, field
+
+
+@dataclass
+class Hypothesis:
+    id: str
+    statement: str
+    needs: str
+    falsify: str
+    verdict: str = "INSUFFICIENT"
+    evidence: str = "no data supplied"
+
+    def report(self) -> str:
+        return f"[{self.id}] {self.verdict}\n   claim: {self.statement}\n   needs: {self.needs}\n   falsify-if: {self.falsify}\n   evidence: {self.evidence}\n"
+
+
+HYPS = [
+    Hypothesis("H1", "Intent-based routing + Foundation Series landing converts the 190-signup audience at a rate worth building funnel infra for.",
+               "signups.csv (intent_bucket, converted_to_foundation)",
+               "conversion < threshold needed to justify build (define threshold before run)"),
+    Hypothesis("H2", "Behavioral recency (practice signal) predicts subscription continuation better than 90/180-day purchase timers.",
+               "subscribers.csv (practice_signal, continued, purchase_recency_days)",
+               "purchase_recency correlates with continuation >= practice_signal"),
+    Hypothesis("H3", "Movement Lab as qualification hits Ageless Body 25%+ / Tier 2 40%+ conversion.",
+               "cohort_jan2026.csv (bought_ageless, upgraded_tier2)",
+               "actual conversion materially below targets, or Lab adds no lift vs non-attendees"),
+    Hypothesis("H4", "Founder-time-capped revenue (56%) shifts to scalable streams via acquisition instrumentation alone, without practitioner training.",
+               "capped_clients.csv (revenue_stream, converted_to_scalable)",
+               "capped clients do not convert to scalable streams at the needed rate"),
+]
+
+
+def _load(path: str) -> list[dict]:
+    if not os.path.exists(path):
+        return []
+    with open(path, newline="") as f:
+        return list(csv.DictReader(f))
+
+
+def _rate(rows, col) -> float | None:
+    if not rows:
+        return None
+    n = sum(1 for r in rows if str(r.get(col, "")).strip().lower() in ("1", "true", "yes", "t"))
+    return n / len(rows)
+
+
+def evaluate(h: Hypothesis, data_dir: str) -> None:
+    if h.id == "H1":
+        rows = _load(os.path.join(data_dir, "signups.csv"))
+        if not rows:
+            return
+        # Require a defined threshold to avoid a fabricated verdict.
+        thr = os.environ.get("H1_THRESHOLD")
+        rate = _rate(rows, "converted_to_foundation")
+        if rate is None:
+            h.evidence = "signups.csv present but column 'converted_to_foundation' empty"
+            return
+        if thr is None:
+            h.verdict = "INSUFFICIENT"
+            h.evidence = f"conversion={rate:.1%} but H1_THRESHOLD undefined; set it to decide KILL/KEEP"
+            return
+        thr_f = float(thr)
+        h.evidence = f"conversion={rate:.1%} (threshold={thr_f:.1%})"
+        h.verdict = "KEEP" if rate >= thr_f else "KILL"
+    elif h.id == "H2":
+        rows = _load(os.path.join(data_dir, "subscribers.csv"))
+        if not rows:
+            return
+        # Compute correlation of practice_signal vs continued, and purchase_recency vs continued.
+        def corr(col):
+            xs, ys = [], []
+            for r in rows:
+                try:
+                    xs.append(float(r[col]))
+                    ys.append(1.0 if str(r.get("continued", "")).lower() in ("1", "true", "t") else 0.0)
+                except (ValueError, TypeError):
+                    pass
+            if len(xs) < 3:
+                return None
+            mx, my = sum(xs) / len(xs), sum(ys) / len(ys)
+            num = sum((x - mx) * (y - my) for x, y in zip(xs, ys))
+            den = (sum((x - mx) ** 2 for x in xs) * sum((y - my) ** 2 for y in ys)) ** 0.5
+            return num / den if den else 0.0
+        cp, cr = corr("practice_signal"), corr("purchase_recency_days")
+        if cp is None or cr is None:
+            h.evidence = "subscribers.csv present but insufficient numeric rows"
+            return
+        h.evidence = f"corr(practice,continued)={cp:.2f}; corr(purchase_recency,continued)={cr:.2f}"
+        h.verdict = "KILL" if cr >= cp else "KEEP"
+    elif h.id == "H3":
+        rows = _load(os.path.join(data_dir, "cohort_jan2026.csv"))
+        if not rows:
+            return
+        a = _rate(rows, "bought_ageless")
+        t = _rate(rows, "upgraded_tier2")
+        if a is None or t is None:
+            h.evidence = "cohort_jan2026.csv present but target columns empty"
+            return
+        h.evidence = f"Ageless={a:.1%} (target 25%); Tier2={t:.1%} (target 40%)"
+        h.verdict = "KEEP" if (a >= 0.25 and t >= 0.40) else "REVISE"
+    elif h.id == "H4":
+        rows = _load(os.path.join(data_dir, "capped_clients.csv"))
+        if not rows:
+            return
+        r = _rate(rows, "converted_to_scalable")
+        if r is None:
+            h.evidence = "capped_clients.csv present but column empty"
+            return
+        h.evidence = f"capped->scalable conversion={r:.1%}"
+        h.verdict = "KEEP" if r >= 0.30 else "REVISE"
+
+
+def main() -> int:
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--data-dir", default=os.path.join(os.path.dirname(__file__), "sample_data"))
+    args = ap.parse_args()
+    print("VEP P0 Falsification Harness — GST reference tenant")
+    print(f"data-dir: {args.data_dir}\n")
+    kills = 0
+    for h in HYPS:
+        evaluate(h, args.data_dir)
+        print(h.report())
+        if h.verdict in ("KILL", "REVISE"):
+            kills += 1
+    print("=" * 60)
+    if kills == 0 and all(h.verdict == "INSUFFICIENT" for h in HYPS):
+        print("GATE: INSUFFICIENT DATA — supply data files to run the gate.")
+        print("This is the honest default: no fabricated verdicts.")
+    else:
+        print(f"GATE: {kills} hypothesis(es) KILL/REVISE. Per P0 acceptance, >=1 kill/revise = gate MET.")
+        print("If KILL/REVISE: prune or revise the build accordingly (the P1 spine is small + reversible).")
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())

scripts/p0/sample_data/README.md

@@ -0,0 +1,20 @@
+# VEP P0 — data templates
+#
+# Drop real GST exports here as CSV with these exact columns. The harness (run.py)
+# reads them and emits KILL/KEEP/REVISE. Until files exist (or columns are empty),
+# the harness reports INSUFFICIENT — it never invents numbers.
+
+signups.csv          columns: email, intent_bucket, converted_to_foundation, date
+cohort_jan2026.csv   columns: attendee, bought_ageless, upgraded_tier2
+subscribers.csv      columns: sub_id, tier, practice_signal, continued, purchase_recency_days
+capped_clients.csv   columns: client_id, revenue_stream, converted_to_scalable
+
+Where to get these (GST-owned data; not available to the assistant):
+  - signups.csv        : export the 190 7-Day Revival signups; tag each with the intent
+                         bucket they pick in the H1 test email; mark who bought Foundation.
+  - cohort_jan2026.csv : from the delivered Movement Lab cohort — who bought Ageless / Tier 2.
+  - subscribers.csv    : export Tier 1/Tier 2 subscribers + any practice/view signal + churn.
+  - capped_clients.csv : live-class / private-session clients; did they buy video/equipment?
+
+Run:  python scripts/p0/run.py --data-dir scripts/p0/sample_data
+      H1_THRESHOLD=0.10 python scripts/p0/run.py   # e.g. need >=10% conversion to build