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/auto-summarizer/README.md

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+# auto-summarizer
+
+Maintains a running conversation summary to help agents handle long conversations without losing context. Uses extractive summarization (no ML or external dependencies) to identify the most important parts of a conversation.
+
+## How it works
+
+After each conversation turn, the plugin scans all messages and extracts:
+
+- **Topic opener** -- the first user message that started the conversation
+- **Questions** -- any messages containing questions (detected via `?`)
+- **Decisions** -- messages with conclusion/decision language ("let's", "decided", "the plan is", etc.)
+- **Recent context** -- the last 2 exchanges to preserve immediate context
+
+These are combined into a compact summary (max 500 characters) and persisted to disk. On the next ingest, the summary is returned as a memory fragment so the agent retains awareness of the full conversation.
+
+Summarization only activates when the conversation exceeds 6 messages -- shorter conversations are passed through as-is.
+
+## Hooks
+
+| Hook | Script | Description |
+|------|--------|-------------|
+| ingest | `hooks/ingest.py` | Returns the stored conversation summary as a memory fragment |
+| after_turn | `hooks/after_turn.py` | Builds and persists an extractive summary of the conversation |
+
+## Storage
+
+Summaries are stored at `~/.librefang/plugins/auto-summarizer/{agent_id}.summary`.
+
+## Usage
+
+Installed automatically when enabled in agent configuration.

plugins/auto-summarizer/hooks/after_turn.py

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+#!/usr/bin/env python3
+"""Auto-summarizer after_turn hook.
+
+Generates a compact extractive summary of the conversation after each turn.
+Keeps agents aware of conversation context even in long exchanges.
+
+Receives via stdin:
+    {"type": "after_turn", "agent_id": "...", "messages": [...]}
+
+Prints to stdout:
+    {"type": "ok"}
+"""
+import json
+import os
+import sys
+
+# Only summarize when conversation exceeds this many messages
+MIN_MESSAGES_FOR_SUMMARY = 6
+
+# Maximum length of the generated summary
+MAX_SUMMARY_CHARS = 500
+
+# Keywords that signal decisions or conclusions
+DECISION_KEYWORDS = (
+    "let's", "i'll", "we should", "decided", "agreed",
+    "the plan is", "we'll", "going to", "conclusion",
+    "in summary", "to summarize", "final answer",
+)
+
+
+def get_storage_dir():
+    """Return the plugin storage directory, creating it if needed."""
+    home = os.path.expanduser("~")
+    path = os.path.join(home, ".librefang", "plugins", "auto-summarizer")
+    os.makedirs(path, exist_ok=True)
+    return path
+
+
+def get_content(msg):
+    """Extract text content from a message object."""
+    if isinstance(msg, dict):
+        return msg.get("content", "") or ""
+    return str(msg)
+
+
+def get_role(msg):
+    """Extract the role from a message object."""
+    if isinstance(msg, dict):
+        return msg.get("role", "unknown")
+    return "unknown"
+
+
+def contains_question(text):
+    """Check if text contains a question."""
+    return "?" in text
+
+
+def contains_decision(text):
+    """Check if text contains decision/conclusion language."""
+    lower = text.lower()
+    return any(kw in lower for kw in DECISION_KEYWORDS)
+
+
+def truncate(text, max_len):
+    """Truncate text to max_len, adding ellipsis if needed."""
+    if len(text) <= max_len:
+        return text
+    return text[:max_len - 3] + "..."
+
+
+def build_summary(messages):
+    """Build an extractive summary from the conversation messages.
+
+    Strategy:
+    - First user message (topic opener)
+    - Messages containing questions
+    - Messages containing decisions/conclusions
+    - Last 2 exchanges (most recent context)
+
+    Deduplicates and truncates to MAX_SUMMARY_CHARS.
+    """
+    if len(messages) <= MIN_MESSAGES_FOR_SUMMARY:
+        return ""
+
+    selected = []
+    seen_indices = set()
+
+    # 1. First user message (topic opener)
+    for i, msg in enumerate(messages):
+        if get_role(msg) == "user":
+            content = get_content(msg).strip()
+            if content:
+                selected.append(f"Topic: {truncate(content, 120)}")
+                seen_indices.add(i)
+            break
+
+    # 2. Messages containing questions
+    for i, msg in enumerate(messages):
+        if i in seen_indices:
+            continue
+        content = get_content(msg).strip()
+        if content and contains_question(content):
+            role = get_role(msg)
+            prefix = "Q" if role == "user" else "Agent-Q"
+            selected.append(f"{prefix}: {truncate(content, 100)}")
+            seen_indices.add(i)
+
+    # 3. Messages containing decisions/conclusions
+    for i, msg in enumerate(messages):
+        if i in seen_indices:
+            continue
+        content = get_content(msg).strip()
+        if content and contains_decision(content):
+            selected.append(f"Decision: {truncate(content, 100)}")
+            seen_indices.add(i)
+
+    # 4. Last 2 exchanges (up to 4 messages: user+assistant pairs)
+    tail_start = max(0, len(messages) - 4)
+    for i in range(tail_start, len(messages)):
+        if i in seen_indices:
+            continue
+        content = get_content(messages[i]).strip()
+        if content:
+            role = get_role(messages[i])
+            label = "User" if role == "user" else "Agent"
+            selected.append(f"Recent({label}): {truncate(content, 100)}")
+            seen_indices.add(i)
+
+    if not selected:
+        return ""
+
+    summary = " | ".join(selected)
+    return truncate(summary, MAX_SUMMARY_CHARS)
+
+
+def main():
+    request = json.loads(sys.stdin.read())
+    agent_id = request.get("agent_id", "unknown")
+    messages = request.get("messages", [])
+
+    summary = build_summary(messages)
+
+    if summary:
+        storage_dir = get_storage_dir()
+        summary_path = os.path.join(storage_dir, f"{agent_id}.summary")
+        with open(summary_path, "w", encoding="utf-8") as f:
+            f.write(summary)
+
+    print(json.dumps({"type": "ok"}), flush=True)
+
+
+if __name__ == "__main__":
+    main()

plugins/auto-summarizer/hooks/ingest.py

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+#!/usr/bin/env python3
+"""Auto-summarizer ingest hook.
+
+Returns the stored conversation summary as a memory fragment so agents
+maintain awareness of prior conversation context.
+
+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
+
+
+def get_summary_path(agent_id):
+    """Return the path to the summary file for the given agent."""
+    home = os.path.expanduser("~")
+    return os.path.join(
+        home, ".librefang", "plugins", "auto-summarizer", f"{agent_id}.summary"
+    )
+
+
+def main():
+    request = json.loads(sys.stdin.read())
+    agent_id = request.get("agent_id", "unknown")
+
+    memories = []
+    summary_path = get_summary_path(agent_id)
+
+    if os.path.isfile(summary_path):
+        try:
+            with open(summary_path, "r", encoding="utf-8") as f:
+                summary = f.read().strip()
+            if summary:
+                memories.append(
+                    {"content": f"[summary] Conversation so far: {summary}"}
+                )
+        except (OSError, IOError):
+            # If we cannot read the file, return no memories silently.
+            pass
+
+    print(json.dumps({"type": "ingest_result", "memories": memories}))
+
+
+if __name__ == "__main__":
+    main()

plugins/auto-summarizer/plugin.toml

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+name = "auto-summarizer"
+version = "0.1.0"
+description = "Maintains a running conversation summary to help agents handle long conversations without losing context"
+author = "librefang"
+
+[hooks]
+ingest = "hooks/ingest.py"
+after_turn = "hooks/after_turn.py"
+
+
+[i18n.zh]
+name = "自动摘要"
+description = "持续维护会话摘要，帮助 Agent 在长对话中不丢失上下文。"
+
+[i18n.zh-TW]
+name = "自動摘要"
+description = "持續維護會話摘要，幫助 Agent 在長對話中不遺失上下文。"
+
+[i18n.ja]
+name = "自動要約"
+description = "会話の要約を逐次更新し、長い会話でもコンテキストを失わないよう支援。"
+
+[i18n.ko]
+name = "자동 요약"
+description = "진행 중인 대화 요약을 유지하여 긴 대화에서 컨텍스트 손실을 방지."
+
+[i18n.de]
+name = "Auto-Zusammenfassung"
+description = "Pflegt eine laufende Konversations-Zusammenfassung, damit Agenten bei langen Gesprächen den Kontext behalten."
+
+[i18n.es]
+name = "Auto-resumen"
+description = "Mantiene un resumen continuo de la conversación para que los agentes no pierdan contexto en diálogos largos."
+
+[i18n.fr]
+name = "Auto-résumé"
+description = "Maintient un résumé continu de la conversation pour que les agents ne perdent pas de contexte dans les longs échanges."
+
+[integrity]
+"hooks/after_turn.py" = "fe24d5f8dd85348f855f7e2c27e607e57c4fad549d50d814101be14fa5ab34f1"
+"hooks/ingest.py" = "0581dd415457bffc3160580acf3036a2fe9e211e86b49bbc51cc30118dc67e0e"

plugins/auto-summarizer/requirements.txt

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+# No external dependencies — uses only Python stdlib