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/sentiment-tracker/README.md

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+# sentiment-tracker
+
+Analyzes user message sentiment using keyword-based scoring and injects emotional context so agents can respond with appropriate tone. No external ML libraries required (stdlib only).
+
+## Scoring Method
+
+- **Positive words** (~30): great, love, excellent, awesome, helpful, appreciate, etc. (+1 each)
+- **Negative words** (~30): bad, terrible, frustrated, broken, bug, error, crash, etc. (-1 each)
+- **Intensifiers**: very, extremely, really, absolutely, totally (multiply next sentiment word by 1.5x)
+- **Negators**: not, no, never, don't, doesn't, isn't, can't, won't (flip next word's polarity)
+
+The raw score is normalized by message length and clamped to [-1.0, 1.0].
+
+## Classification
+
+| Score Range | Label | Action |
+|-------------|-------|--------|
+| > 0.3 | positive | Inject positive context memory |
+| < -0.3 | negative | Inject frustration-aware memory |
+| -0.3 to 0.3 | neutral | No memory injected (avoid context clutter) |
+
+## Hooks
+
+| Hook | Script | Description |
+|------|--------|-------------|
+| ingest | `hooks/ingest.py` | Analyzes message sentiment and returns emotional context as a memory fragment |
+
+## Example Output
+
+Negative sentiment:
+```json
+{"type": "ingest_result", "memories": [{"content": "[sentiment] User appears frustrated (score: -0.6). Consider acknowledging the issue."}]}
+```
+
+Positive sentiment:
+```json
+{"type": "ingest_result", "memories": [{"content": "[sentiment] User seems satisfied (score: 0.7). Positive interaction."}]}
+```
+
+Neutral sentiment returns an empty memories list.
+
+## Usage
+
+Installed automatically when enabled in agent configuration. No external dependencies required (stdlib only).

plugins/sentiment-tracker/hooks/README.md

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+# sentiment-tracker hooks
+
+Python hook scripts for the sentiment-tracker 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": "..."}`, analyzes sentiment, returns emotional context as a memory fragment |
+
+## Protocol
+
+- **Input**: JSON object on stdin (fields vary by hook type)
+- **Output**: JSON object on stdout (`ingest_result` with memories, empty for neutral sentiment)

plugins/sentiment-tracker/hooks/ingest.py

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+#!/usr/bin/env python3
+"""Sentiment tracker ingest hook.
+
+Analyzes user message sentiment using keyword-based scoring and injects
+emotional context so agents can respond with appropriate tone.
+
+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
+
+# Positive sentiment words with base score of +1
+POSITIVE_WORDS = frozenset({
+    "great", "love", "excellent", "happy", "thanks", "awesome", "perfect",
+    "wonderful", "good", "nice", "amazing", "fantastic", "helpful", "pleased",
+    "appreciate", "brilliant", "outstanding", "superb", "delightful", "glad",
+    "impressive", "beautiful", "enjoy", "excited", "grateful", "incredible",
+    "marvelous", "terrific", "thank", "cool",
+})
+
+# Negative sentiment words with base score of -1
+NEGATIVE_WORDS = frozenset({
+    "bad", "terrible", "hate", "angry", "frustrated", "disappointed", "broken",
+    "wrong", "awful", "horrible", "annoying", "useless", "fail", "worst",
+    "problem", "issue", "bug", "error", "crash", "slow", "stuck", "confused",
+    "difficult", "painful", "ugly", "ridiculous", "poor", "sucks", "garbage",
+    "missing",
+})
+
+# Intensifiers multiply the next sentiment word's score by this factor
+INTENSIFIERS = frozenset({
+    "very", "extremely", "really", "absolutely", "totally", "incredibly",
+    "completely", "utterly", "highly", "so",
+})
+INTENSIFIER_MULTIPLIER = 1.5
+
+# Negators flip the polarity of the next sentiment word
+NEGATORS = frozenset({
+    "not", "no", "never", "don't", "doesn't", "isn't", "can't", "won't",
+    "didn't", "wasn't", "weren't", "couldn't", "shouldn't", "wouldn't",
+    "hardly", "barely", "neither",
+})
+
+# Sentiment thresholds
+POSITIVE_THRESHOLD = 0.3
+NEGATIVE_THRESHOLD = -0.3
+
+
+def tokenize(text):
+    """Split text into lowercase tokens, preserving contractions."""
+    return re.findall(r"[a-zA-Z][a-zA-Z']*", text.lower())
+
+
+def compute_sentiment(tokens):
+    """Compute sentiment score from -1.0 to 1.0.
+
+    Walks through tokens tracking negator and intensifier state,
+    then applies them to sentiment-bearing words.
+    """
+    if not tokens:
+        return 0.0
+
+    raw_score = 0.0
+    negate_next = False
+    intensify_next = False
+
+    for token in tokens:
+        if token in NEGATORS:
+            negate_next = True
+            continue
+
+        if token in INTENSIFIERS:
+            intensify_next = True
+            continue
+
+        score = 0.0
+        if token in POSITIVE_WORDS:
+            score = 1.0
+        elif token in NEGATIVE_WORDS:
+            score = -1.0
+
+        if score != 0.0:
+            if intensify_next:
+                score *= INTENSIFIER_MULTIPLIER
+                intensify_next = False
+
+            if negate_next:
+                score *= -1.0
+                negate_next = False
+
+            raw_score += score
+        else:
+            # Reset modifiers if the next word is not a sentiment word
+            # (modifiers only apply to the immediately following sentiment word)
+            negate_next = False
+            intensify_next = False
+
+    # Normalize to -1.0 .. 1.0 using tanh-like scaling
+    # This keeps small scores proportional while bounding large ones
+    word_count = len(tokens)
+    if word_count == 0:
+        return 0.0
+
+    # Scale by number of tokens to normalize for message length
+    normalized = raw_score / max(word_count ** 0.5, 1.0)
+
+    # Clamp to [-1.0, 1.0]
+    return max(-1.0, min(1.0, normalized))
+
+
+def classify(score):
+    """Classify sentiment score into a label."""
+    if score > POSITIVE_THRESHOLD:
+        return "positive"
+    elif score < NEGATIVE_THRESHOLD:
+        return "negative"
+    else:
+        return "neutral"
+
+
+def main():
+    request = json.loads(sys.stdin.read())
+    message = request.get("message", "")
+
+    if not message.strip():
+        print(json.dumps({"type": "ingest_result", "memories": []}))
+        return
+
+    tokens = tokenize(message)
+    score = compute_sentiment(tokens)
+    label = classify(score)
+
+    # Only inject memory for clearly non-neutral sentiment
+    if label == "neutral":
+        print(json.dumps({"type": "ingest_result", "memories": []}))
+        return
+
+    score_str = f"{score:.1f}"
+
+    if label == "negative":
+        content = f"[sentiment] User appears frustrated (score: {score_str}). Consider acknowledging the issue."
+    else:
+        content = f"[sentiment] User seems satisfied (score: {score_str}). Positive interaction."
+
+    memories = [{"content": content}]
+    print(json.dumps({"type": "ingest_result", "memories": memories}))
+
+
+if __name__ == "__main__":
+    main()

plugins/sentiment-tracker/plugin.toml

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+name = "sentiment-tracker"
+version = "0.1.0"
+description = "Analyzes user message sentiment and injects emotional context so agents can respond with appropriate tone"
+author = "librefang"
+
+[hooks]
+ingest = "hooks/ingest.py"
+
+
+[i18n.zh]
+name = "情绪追踪"
+description = "分析用户消息的情绪，并把情感上下文注入 Agent，让回复更贴合情绪。"
+
+[i18n.zh-TW]
+name = "情緒追蹤"
+description = "分析使用者訊息的情緒，並將情感上下文注入 Agent，讓回覆更貼合情緒。"
+
+[i18n.ja]
+name = "感情トラッカー"
+description = "ユーザーメッセージの感情を分析し、感情コンテキストを注入して適切なトーンで応答できるようにする。"
+
+[i18n.ko]
+name = "감정 추적기"
+description = "사용자 메시지의 감정을 분석하여 Agent 컨텍스트에 감정 정보를 주입하고 적절한 어조로 응답하게 함."
+
+[i18n.de]
+name = "Sentiment-Tracker"
+description = "Analysiert die Stimmung von Nutzernachrichten und fügt emotionalen Kontext ein, damit Antworten den passenden Ton treffen."
+
+[i18n.es]
+name = "Rastreador de sentimiento"
+description = "Analiza el sentimiento de los mensajes del usuario e inyecta contexto emocional para que el agente responda con el tono adecuado."
+
+[i18n.fr]
+name = "Suivi de sentiment"
+description = "Analyse le sentiment des messages utilisateur et injecte un contexte émotionnel pour que l'agent réponde avec le bon ton."
+
+[integrity]
+"hooks/ingest.py" = "1ce4a89e6b3de86d236d54d60b96b6fffb8306616666aef4802ba690ca8ae225"