Initial Arka plugin registry: Official plugins mirror + Arka-signed index

11 plugins from github.com/librefang/librefang-registry plugins/.
index.json / index.json.sig are signed with Arka's Ed25519 key
(not upstream stats.librefang.ai). Private key is not in this repo.

plugins/user-profile/hooks/ingest.py

@@ -0,0 +1,134 @@
+#!/usr/bin/env python3
+"""User-profile ingest hook.
+
+Reads the persisted user profile for the given agent and, when enough
+interaction data has been collected (>= 5 interactions), returns a
+compact profile summary as injected memory so the agent can personalise
+its responses.
+
+This hook is READ-ONLY -- it never modifies the profile store.
+
+Receives via stdin:
+    {"type": "ingest", "agent_id": "...", "message": "user message text"}
+
+Prints to stdout:
+    {"type": "ingest_result", "memories": [{"content": "..."}]}
+"""
+import json
+import os
+import sys
+
+# ---------------------------------------------------------------------------
+# Constants
+# ---------------------------------------------------------------------------
+
+STORE_DIR = os.path.join(
+    os.path.expanduser("~"), ".librefang", "plugins", "user-profile"
+)
+
+MIN_INTERACTIONS = 5
+MAX_SUMMARY_LEN = 200
+TOP_EXPERTISE = 5
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+
+def empty_result():
+    """Return an empty ingest result."""
+    return json.dumps({"type": "ingest_result", "memories": []})
+
+
+def load_profile(agent_id):
+    """Load the profile JSON for an agent. Returns None on any error."""
+    path = os.path.join(STORE_DIR, f"{agent_id}.json")
+    try:
+        with open(path, "r", encoding="utf-8") as f:
+            data = json.load(f)
+        if isinstance(data, dict) and isinstance(data.get("interaction_count"), int):
+            return data
+    except (OSError, json.JSONDecodeError, ValueError):
+        pass
+    return None
+
+
+def message_length_bucket(avg_len):
+    """Classify average message length into a human-readable bucket."""
+    if avg_len < 50:
+        return "brief"
+    elif avg_len <= 200:
+        return "moderate"
+    else:
+        return "detailed"
+
+
+def build_summary(profile):
+    """Build a compact profile summary string (max MAX_SUMMARY_LEN chars)."""
+    parts = []
+
+    # Top expertise areas
+    expertise = profile.get("expertise_areas", {})
+    if expertise:
+        sorted_areas = sorted(expertise.items(), key=lambda x: x[1], reverse=True)
+        top = [area for area, _count in sorted_areas[:TOP_EXPERTISE]]
+        parts.append("expertise=" + ",".join(top))
+
+    # Communication style
+    avg_len = profile.get("avg_message_length", 0)
+    parts.append("style=" + message_length_bucket(avg_len))
+
+    # Technical level
+    tech_level = profile.get("technical_level", "")
+    if tech_level:
+        parts.append("level=" + tech_level)
+
+    # Question ratio
+    q_ratio = profile.get("question_ratio", 0.0)
+    if q_ratio > 0.5:
+        parts.append("asks-many-questions")
+
+    summary = "; ".join(parts)
+    if len(summary) > MAX_SUMMARY_LEN:
+        summary = summary[:MAX_SUMMARY_LEN - 3] + "..."
+    return summary
+
+
+# ---------------------------------------------------------------------------
+# Main
+# ---------------------------------------------------------------------------
+
+
+def main():
+    try:
+        request = json.loads(sys.stdin.read())
+    except (json.JSONDecodeError, ValueError):
+        print(empty_result())
+        return
+
+    agent_id = request.get("agent_id", "")
+    if not agent_id:
+        print(empty_result())
+        return
+
+    profile = load_profile(agent_id)
+    if profile is None:
+        print(empty_result())
+        return
+
+    interaction_count = profile.get("interaction_count", 0)
+    if interaction_count < MIN_INTERACTIONS:
+        print(empty_result())
+        return
+
+    summary = build_summary(profile)
+    if not summary:
+        print(empty_result())
+        return
+
+    memory = {"content": f"[user-profile] User context: {summary}"}
+    print(json.dumps({"type": "ingest_result", "memories": [memory]}))
+
+
+if __name__ == "__main__":
+    main()