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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.
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:
{"type": "ingest_result", "memories": [{"content": "[sentiment] User appears frustrated (score: -0.6). Consider acknowledging the issue."}]}
Positive sentiment:
{"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).