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/keyword-memory/README.md

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+# keyword-memory
+
+Extracts keywords and named entities from user messages and returns them as contextual memories. Gives agents awareness of conversation topics without requiring external NLP libraries.
+
+## Extraction Techniques
+
+- **Plain keywords**: Splits words, filters English stopwords (~50 words), removes short tokens
+- **Capitalized phrases**: Detects multi-word proper nouns and mid-sentence capitalized words
+- **Emails and URLs**: Regex pattern matching
+- **Numbers with units**: e.g. 500ms, 10GB, 3.5GHz
+- **Dates**: YYYY-MM-DD, MM/DD/YYYY, DD.MM.YYYY formats
+- **Technical terms**: camelCase, snake_case, dotted identifiers (e.g. `os.path`)
+
+Results are deduplicated and capped at 10 keywords.
+
+## Hooks
+
+| Hook | Script | Description |
+|------|--------|-------------|
+| ingest | `hooks/ingest.py` | Extracts keywords from the user message and returns them as a memory fragment |
+
+## Example Output
+
+```json
+{"type": "ingest_result", "memories": [{"content": "[keyword-memory] Key topics: GPT-4, machine_learning, data pipeline, https://example.com"}]}
+```
+
+If no meaningful keywords are found, returns an empty memories list.
+
+## Usage
+
+Installed automatically when enabled in agent configuration. No external dependencies required (stdlib only).

plugins/keyword-memory/hooks/README.md

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+# keyword-memory hooks
+
+Python hook scripts for the keyword-memory plugin. Each script reads a JSON request from stdin and writes a JSON response to stdout.
+
+## Scripts
+
+| Script | Hook | Description |
+|--------|------|-------------|
+| `ingest.py` | ingest | Receives `{"message": "..."}`, extracts keywords and named entities, returns them as memory fragments |
+
+## Protocol
+
+- **Input**: JSON object on stdin (fields vary by hook type)
+- **Output**: JSON object on stdout (`ingest_result` with memories)

plugins/keyword-memory/hooks/ingest.py

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+#!/usr/bin/env python3
+"""Keyword memory ingest hook.
+
+Extracts keywords and named entities from user messages and returns
+them as contextual memories so agents have topic awareness.
+
+Receives via stdin:
+    {"type": "ingest", "agent_id": "...", "message": "user message text"}
+
+Prints to stdout:
+    {"type": "ingest_result", "memories": [{"content": "..."}]}
+"""
+import json
+import re
+import sys
+
+# Compact English stopword set (~50 common words)
+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",
+})
+
+# Minimum word length for plain keyword extraction
+MIN_WORD_LEN = 3
+
+# Maximum keywords to return
+MAX_KEYWORDS = 10
+
+# Pattern: email addresses
+RE_EMAIL = re.compile(r"[a-zA-Z0-9._%+\-]+@[a-zA-Z0-9.\-]+\.[a-zA-Z]{2,}")
+
+# Pattern: URLs (http/https/ftp) — excludes trailing punctuation
+RE_URL = re.compile(r"https?://[^\s,)>]+(?<=[a-zA-Z0-9/])|ftp://[^\s,)>]+(?<=[a-zA-Z0-9/])")
+
+# Pattern: numbers with units (e.g. 500ms, 10GB, 3.5GHz, 200k)
+RE_NUMBER_UNIT = re.compile(r"\b\d+(?:\.\d+)?(?:ms|s|min|hr|h|kb|mb|gb|tb|ghz|mhz|hz|k|m|px|em|rem|%)\b", re.IGNORECASE)
+
+# Pattern: dates (YYYY-MM-DD, MM/DD/YYYY, DD.MM.YYYY)
+RE_DATE = re.compile(
+    r"\b\d{4}-\d{2}-\d{2}\b"
+    r"|\b\d{1,2}/\d{1,2}/\d{2,4}\b"
+    r"|\b\d{1,2}\.\d{1,2}\.\d{2,4}\b"
+)
+
+# Pattern: camelCase or PascalCase identifiers
+RE_CAMEL = re.compile(r"\b[a-z]+(?:[A-Z][a-z0-9]+)+\b|\b(?:[A-Z][a-z0-9]+){2,}\b")
+
+# Pattern: snake_case identifiers (at least one underscore)
+RE_SNAKE = re.compile(r"\b[a-zA-Z][a-zA-Z0-9]*(?:_[a-zA-Z0-9]+)+\b")
+
+# Pattern: dotted technical terms (e.g. api.endpoint, os.path)
+RE_DOTTED = re.compile(r"\b[a-zA-Z][a-zA-Z0-9]*(?:\.[a-zA-Z][a-zA-Z0-9]*)+\b")
+
+
+def extract_patterns(text):
+    """Extract structured patterns: emails, URLs, numbers+units, dates, tech terms."""
+    found = []
+    for pattern in (RE_EMAIL, RE_URL, RE_NUMBER_UNIT, RE_DATE, RE_CAMEL, RE_SNAKE, RE_DOTTED):
+        found.extend(pattern.findall(text))
+    return found
+
+
+def extract_capitalized_phrases(text):
+    """Detect consecutive capitalized words (likely proper nouns / named entities).
+
+    Skips single capitalized words at sentence boundaries by requiring
+    either multi-word phrases or mid-sentence capitalized words.
+    """
+    phrases = []
+    # Find sequences of 2+ capitalized words
+    for match in re.finditer(r"\b([A-Z][a-z]+(?:\s+[A-Z][a-z]+)+)\b", text):
+        phrases.append(match.group(0))
+
+    # Find single capitalized words that are NOT at sentence start
+    # (preceded by a lowercase letter, comma, or mid-sentence punctuation)
+    for match in re.finditer(r"(?<=[a-z,;]\s)([A-Z][a-zA-Z0-9]+)\b", text):
+        word = match.group(1)
+        if word.lower() not in STOPWORDS and len(word) >= MIN_WORD_LEN:
+            phrases.append(word)
+
+    return phrases
+
+
+def extract_plain_keywords(text):
+    """Split text into words and filter out stopwords and short tokens."""
+    # Remove URLs and emails first so they don't pollute word splitting
+    cleaned = RE_URL.sub(" ", text)
+    cleaned = RE_EMAIL.sub(" ", cleaned)
+    # Split on non-alphanumeric (keep hyphens inside words)
+    words = re.findall(r"[a-zA-Z][a-zA-Z0-9\-]*[a-zA-Z0-9]|[a-zA-Z]", cleaned)
+    keywords = []
+    for w in words:
+        lower = w.lower()
+        if lower not in STOPWORDS and len(lower) >= MIN_WORD_LEN:
+            keywords.append(lower)
+    return keywords
+
+
+def deduplicate_keywords(items):
+    """Deduplicate while preserving insertion order. Case-insensitive for plain words."""
+    seen = set()
+    result = []
+    for item in items:
+        key = item.lower()
+        if key not in seen:
+            seen.add(key)
+            result.append(item)
+    return result
+
+
+def main():
+    request = json.loads(sys.stdin.read())
+    message = request.get("message", "")
+
+    if not message.strip():
+        print(json.dumps({"type": "ingest_result", "memories": []}))
+        return
+
+    # Collect keywords from all extraction methods (patterns first for priority)
+    all_keywords = []
+    all_keywords.extend(extract_patterns(message))
+    all_keywords.extend(extract_capitalized_phrases(message))
+    all_keywords.extend(extract_plain_keywords(message))
+
+    # Deduplicate and cap at MAX_KEYWORDS
+    keywords = deduplicate_keywords(all_keywords)[:MAX_KEYWORDS]
+
+    if not keywords:
+        print(json.dumps({"type": "ingest_result", "memories": []}))
+        return
+
+    topic_str = ", ".join(keywords)
+    memories = [
+        {"content": f"[keyword-memory] Key topics: {topic_str}"}
+    ]
+
+    print(json.dumps({"type": "ingest_result", "memories": memories}))
+
+
+if __name__ == "__main__":
+    main()

plugins/keyword-memory/plugin.toml

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+name = "keyword-memory"
+version = "0.1.0"
+description = "Extracts keywords and named entities from user messages and returns them as contextual memories"
+author = "librefang"
+
+[hooks]
+ingest = "hooks/ingest.py"
+
+
+[i18n.zh]
+name = "关键词记忆"
+description = "从用户消息中抽取关键词与命名实体，作为上下文记忆返回。"
+
+[i18n.zh-TW]
+name = "關鍵字記憶"
+description = "從使用者訊息中抽取關鍵字與命名實體，作為上下文記憶返回。"
+
+[i18n.ja]
+name = "キーワードメモリ"
+description = "ユーザーメッセージからキーワードと固有表現を抽出し、文脈メモリとして返す。"
+
+[i18n.ko]
+name = "키워드 메모리"
+description = "사용자 메시지에서 키워드와 개체명을 추출하여 컨텍스트 메모리로 반환."
+
+[i18n.de]
+name = "Keyword-Gedächtnis"
+description = "Extrahiert Schlüsselwörter und Named-Entities aus Nutzernachrichten und liefert sie als Kontext-Gedächtnis zurück."
+
+[i18n.es]
+name = "Memoria por palabras clave"
+description = "Extrae palabras clave y entidades de los mensajes del usuario y las devuelve como memoria contextual."
+
+[i18n.fr]
+name = "Mémoire par mots-clés"
+description = "Extrait mots-clés et entités nommées des messages utilisateur et les restitue comme mémoire contextuelle."
+
+[integrity]
+"hooks/ingest.py" = "6e9de6524c394e6879e4ab694dfdfbffb2bd423c3df2ef374dd59bf006836330"