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/topic-memory/hooks/ingest.py

@@ -0,0 +1,139 @@
+#!/usr/bin/env python3
+"""Topic-memory ingest hook.
+
+Reads the topic store for the given agent and returns the top matching
+topic summaries based on Jaccard similarity between the incoming message
+keywords and each stored topic cluster.
+
+This hook is READ-ONLY -- it never modifies the topic 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 re
+import sys
+
+# ---------------------------------------------------------------------------
+# Stopwords & constants
+# ---------------------------------------------------------------------------
+
+STOPWORDS = frozenset({
+    "a", "an", "the", "and", "or", "but", "in", "on", "at", "to", "for",
+    "of", "with", "by", "from", "is", "are", "was", "were", "be", "been",
+    "being", "have", "has", "had", "do", "does", "did", "will", "would",
+    "could", "should", "may", "might", "shall", "can", "need", "must",
+    "it", "its", "i", "me", "my", "you", "your", "he", "she", "we",
+    "they", "them", "their", "this", "that", "these", "those", "what",
+    "which", "who", "how", "when", "where", "why", "if", "then", "so",
+    "not", "no", "just", "also", "very", "too", "about", "up", "out",
+    "all", "some", "any", "each", "every", "into", "over", "after",
+})
+
+MIN_WORD_LEN = 3
+MAX_RESULTS = 3
+MIN_SIMILARITY = 0.15
+
+STORE_DIR = os.path.join(os.path.expanduser("~"), ".librefang", "plugins", "topic-memory")
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+
+def extract_keywords(text):
+    """Extract lowercase keywords from text, filtering stopwords and short tokens."""
+    words = re.findall(r"[a-zA-Z][a-zA-Z0-9\-]*[a-zA-Z0-9]|[a-zA-Z]", text)
+    seen = set()
+    keywords = set()
+    for w in words:
+        lower = w.lower()
+        if lower not in STOPWORDS and len(lower) >= MIN_WORD_LEN and lower not in seen:
+            seen.add(lower)
+            keywords.add(lower)
+    return keywords
+
+
+def jaccard_similarity(set_a, set_b):
+    """Compute Jaccard similarity between two sets."""
+    if not set_a or not set_b:
+        return 0.0
+    intersection = len(set_a & set_b)
+    union = len(set_a | set_b)
+    if union == 0:
+        return 0.0
+    return intersection / union
+
+
+def load_store(agent_id):
+    """Load the topic store JSON for an agent. Returns empty structure 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("topics"), list):
+            return data
+    except (OSError, json.JSONDecodeError, ValueError):
+        pass
+    return {"topics": []}
+
+
+def empty_result():
+    """Return an empty ingest result."""
+    return json.dumps({"type": "ingest_result", "memories": []})
+
+
+# ---------------------------------------------------------------------------
+# Main
+# ---------------------------------------------------------------------------
+
+
+def main():
+    try:
+        request = json.loads(sys.stdin.read())
+    except (json.JSONDecodeError, ValueError):
+        print(empty_result())
+        return
+
+    message = request.get("message", "")
+    agent_id = request.get("agent_id", "")
+
+    if not message.strip() or not agent_id:
+        print(empty_result())
+        return
+
+    msg_keywords = extract_keywords(message)
+    if not msg_keywords:
+        print(empty_result())
+        return
+
+    store = load_store(agent_id)
+    topics = store.get("topics", [])
+
+    # Score each topic cluster against the current message keywords
+    scored = []
+    for topic in topics:
+        topic_kw = set(topic.get("keywords", []))
+        sim = jaccard_similarity(msg_keywords, topic_kw)
+        if sim >= MIN_SIMILARITY:
+            scored.append((sim, topic))
+
+    # Sort descending by similarity, take top N
+    scored.sort(key=lambda x: x[0], reverse=True)
+    top = scored[:MAX_RESULTS]
+
+    memories = []
+    for _sim, topic in top:
+        summary = topic.get("summary", "")
+        if summary:
+            memories.append({"content": f"[topic-memory] Related context: {summary}"})
+
+    print(json.dumps({"type": "ingest_result", "memories": memories}))
+
+
+if __name__ == "__main__":
+    main()