feat(cmo): encode cutover policy and autonomous posthoc review guardrails

Author: ccross2

ops/cmo-automation/README.md

@@ -19,12 +19,14 @@ Current mode
 
 Structure
 - config/cmo_accounts.yaml
+- config/operating_policy.json
 - scripts/collect_x_data.py
 - scripts/analyze_x_cmo.py
 - scripts/generate_engagement_queue.py
 - scripts/review_engagement_queue.py
 - scripts/reconstruct_fiverr_playbook.py
 - reports/CMO-AUTOMATION-IMPLEMENTATION-PLAN.md
+- reports/CMO-CUTOVER-48H-RUNBOOK.md
 
 Quick start
 1) Export credentials (or source ~/.claude/secrets.env)

ops/cmo-automation/config/operating_policy.json

@@ -0,0 +1,75 @@
+{
+  "transition": {
+    "fiverr_overlap_hours": 48,
+    "objective": "replace outsourced engagement with in-house automation without activity pause"
+  },
+  "success_targets_30d": {
+    "qualified_follower_growth_pct": 8,
+    "median_engagement_per_post_lift_pct": 25,
+    "short_generic_reply_share_max_pct": 30
+  },
+  "automation": {
+    "mode": "hybrid-autonomous",
+    "founder_autonomy": "autonomous_with_posthoc_audit",
+    "dry_run_default": true
+  },
+  "account_strategy": {
+    "TheCesarCross": {
+      "role": "founder",
+      "mix_pct": {"root": 35, "reply": 50, "quote": 15},
+      "daily_reply_cap": 8,
+      "time_windows_est": ["09:00-11:00", "18:00-20:00"]
+    },
+    "sovren_software": {
+      "role": "brand",
+      "mix_pct": {"root": 45, "reply": 40, "quote": 15},
+      "daily_reply_cap": 10,
+      "time_windows_est": ["10:00-11:00", "14:00-15:00", "19:00-20:00"]
+    },
+    "mrhaven_agent": {
+      "role": "product-agent",
+      "mix_pct": {"root": 60, "reply": 25, "quote": 15},
+      "daily_reply_cap": 6,
+      "time_windows_est": ["08:00-09:00", "13:00-14:00", "21:00-22:00"]
+    }
+  },
+  "targeting": {
+    "strategy": "core-whitelist-plus-discovery",
+    "exploration_budget_pct": 20,
+    "core_whitelist_pct": 80,
+    "minimum_relevance_score": 0.7,
+    "founder_scope": "whitelist_plus_approved_discovery"
+  },
+  "founder_denylist": {
+    "preset": "strict",
+    "categories": [
+      "mainstream_consumer_brands",
+      "meme_bait",
+      "partisan_politics",
+      "token_price_speculation",
+      "celebrity_gossip",
+      "giveaway_airdrop_threads"
+    ],
+    "keywords": [
+      "skittles",
+      "giveaway",
+      "airdrop",
+      "win free",
+      "retweet to win",
+      "$btc to",
+      "$eth to",
+      "meme coin",
+      "pump",
+      "moon",
+      "vote red",
+      "vote blue"
+    ]
+  },
+  "risk_controls": {
+    "max_replies_per_hour_per_account": 8,
+    "max_replies_to_same_user_per_24h": 1,
+    "avoid_repeated_same_user_within_hours": 24,
+    "random_delay_seconds_min": 45,
+    "random_delay_seconds_max": 180
+  }
+}

ops/cmo-automation/reports/CMO-CUTOVER-48H-RUNBOOK.md

@@ -0,0 +1,58 @@
+# CMO 48-Hour Cutover Runbook (No-Pause)
+
+Objective
+- Stop Fiverr after a 48-hour overlap window and maintain uninterrupted engagement cadence with in-house automation.
+
+Decisions captured
+- Overlap: 48h
+- Volume: 80% of Fiverr volume, higher quality
+- Primary KPI: qualified audience growth
+- Automation: hybrid-autonomous, including founder account
+- Targeting: 80% whitelist core + 20% discovery
+- Founder policy: strict deny-list (no unrelated consumer-brand/meme/politics/price-gambling engagement)
+- Fallback on failures/rate-limits: root posts only
+
+## T-48h (start now)
+1) Run full pipeline and verify outputs:
+   - collect_x_data.py
+   - analyze_x_cmo.py
+   - generate_engagement_queue.py
+   - review_engagement_queue.py
+   - reconstruct_fiverr_playbook.py
+2) Ensure global recommendation is not "hold".
+3) Keep Fiverr active during overlap.
+4) Record baseline metrics snapshot for all 3 accounts.
+
+## T-24h
+1) Re-run pipeline.
+2) Compare quality vs prior cycle:
+   - short-generic reply share
+   - per-account avg impressions
+   - approved action counts
+3) If any account degrades materially, throttle replies for that account and keep roots active.
+
+## T-0h (Fiverr off)
+1) Disable Fiverr service.
+2) Keep in-house automation active with current caps.
+3) Post-hoc audit sample (minimum 20% of sent actions).
+
+## T+24h and T+48h
+1) Rebuild playbook report and trend deltas.
+2) If founder account drifts from strategy, tighten founder scope to whitelist-only for next cycle.
+3) If rate limits trigger, switch to root-only fallback for affected cycle.
+
+## Exit criteria (first 30 days)
+- Qualified follower growth >= 8%
+- Median engagement per post lift >= 25%
+- Short-generic reply share <= 30%
+
+## Failure criteria
+- Repeated unrelated founder engagements
+- Sustained decline in median impressions over 3+ cycles
+- High rejection rate from review gate due low relevance
+
+## Owner checklist
+- [ ] Fiverr cancellation timestamp recorded
+- [ ] Cron status healthy
+- [ ] Review gate report reviewed each cycle
+- [ ] Weekly KPI review published

ops/cmo-automation/scripts/generate_engagement_queue.py

@@ -5,52 +5,100 @@
 from pathlib import Path
 
 ROOT = Path(__file__).resolve().parents[1]
-REPORT = ROOT / "reports" / "cmo-analysis.json"
+ANALYSIS = ROOT / "reports" / "cmo-analysis.json"
+POLICY = ROOT / "config" / "operating_policy.json"
 OUT = ROOT / "data" / "engagement-queue.json"
 
 
-def main() -> None:
-    if not REPORT.exists():
-        raise SystemExit("Run analyze_x_cmo.py first")
+def load_json(path: Path):
+    if not path.exists():
+        raise SystemExit(f"Missing {path}")
+    return json.loads(path.read_text())
+
+
+def split_counts(total: int, core_pct: int):
+    core = int(round(total * core_pct / 100.0))
+    discovery = max(0, total - core)
+    return core, discovery
 
-    r = json.loads(REPORT.read_text())
+
+def main() -> None:
+    analysis = load_json(ANALYSIS)
+    policy = load_json(POLICY)
 
     queue = {
         "generated_at": datetime.now(timezone.utc).isoformat(),
-        "mode": "dry-run",
+        "mode": "dry-run" if policy.get("automation", {}).get("dry_run_default", True) else "live",
+        "policy_ref": str(POLICY),
         "actions": [],
     }
 
-    # Very conservative starter policy: propose root posts + selective replies.
-    for handle, stats in r["accounts"].items():
-        # root content recommendation
+    targeting = policy.get("targeting", {})
+    core_pct = targeting.get("core_whitelist_pct", 80)
+
+    for handle, stats in analysis.get("accounts", {}).items():
+        account_policy = policy.get("account_strategy", {}).get(handle, {})
+        daily_reply_cap = int(account_policy.get("daily_reply_cap", 6))
+        windows = account_policy.get("time_windows_est", [])
+
+        # Always include one root post anchor action per run cycle.
         queue["actions"].append(
             {
                 "account": handle,
                 "action": "root_post",
                 "priority": "high",
-                "why": "Keep account voice anchored with original thesis/product narrative.",
+                "window_est": windows,
+                "why": "Anchor narrative with original voice-aligned post.",
                 "constraints": {
-                    "min_gap_minutes": 180,
                     "must_pass_voice_review": True,
+                    "must_align_strategy": True,
+                    "min_gap_minutes": 180,
                 },
             }
         )
 
-        # reply recommendation from top targets, but cap volume
-        targets = [u for u, _ in stats.get("top_reply_targets", [])][:5]
-        for t in targets:
+        # Build candidate pools from observed high-frequency targets.
+        observed_targets = [u for u, _ in stats.get("top_reply_targets", [])]
+        core_count, discovery_count = split_counts(daily_reply_cap, core_pct)
+
+        # Core (whitelist-style): historically engaged users.
+        for target in observed_targets[:core_count]:
             queue["actions"].append(
                 {
                     "account": handle,
                     "action": "reply",
-                    "target_user": t,
+                    "target_user": target,
+                    "target_pool": "core",
                     "priority": "medium",
-                    "why": "User appears in recent interaction graph.",
+                    "window_est": windows,
+                    "why": "Continuation of existing interaction graph.",
                     "constraints": {
                         "max_replies_to_same_user_per_24h": 1,
-                        "min_delay_seconds": random.randint(45, 180),
                         "must_be_contextual": True,
+                        "must_clear_relevance_score": targeting.get("minimum_relevance_score", 0.7),
+                        "min_delay_seconds": random.randint(
+                            policy["risk_controls"].get("random_delay_seconds_min", 45),
+                            policy["risk_controls"].get("random_delay_seconds_max", 180),
+                        ),
+                    },
+                }
+            )
+
+        # Discovery budget: placeholders to be filled by candidate-harvest stage.
+        for _ in range(discovery_count):
+            queue["actions"].append(
+                {
+                    "account": handle,
+                    "action": "reply",
+                    "target_user": None,
+                    "target_pool": "discovery",
+                    "priority": "low",
+                    "window_est": windows,
+                    "why": "Exploration budget for net-new qualified audience.",
+                    "constraints": {
+                        "must_be_contextual": True,
+                        "must_clear_relevance_score": targeting.get("minimum_relevance_score", 0.7),
+                        "must_not_hit_founder_denylist": handle == "TheCesarCross",
                     },
                 }
             )