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8dec8f6038
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.
45 lines
1.6 KiB
Markdown
45 lines
1.6 KiB
Markdown
# sentiment-tracker
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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).
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## Scoring Method
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- **Positive words** (~30): great, love, excellent, awesome, helpful, appreciate, etc. (+1 each)
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- **Negative words** (~30): bad, terrible, frustrated, broken, bug, error, crash, etc. (-1 each)
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- **Intensifiers**: very, extremely, really, absolutely, totally (multiply next sentiment word by 1.5x)
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- **Negators**: not, no, never, don't, doesn't, isn't, can't, won't (flip next word's polarity)
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The raw score is normalized by message length and clamped to [-1.0, 1.0].
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## Classification
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| Score Range | Label | Action |
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|-------------|-------|--------|
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| > 0.3 | positive | Inject positive context memory |
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| < -0.3 | negative | Inject frustration-aware memory |
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| -0.3 to 0.3 | neutral | No memory injected (avoid context clutter) |
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## Hooks
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| Hook | Script | Description |
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|------|--------|-------------|
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| ingest | `hooks/ingest.py` | Analyzes message sentiment and returns emotional context as a memory fragment |
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## Example Output
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Negative sentiment:
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```json
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{"type": "ingest_result", "memories": [{"content": "[sentiment] User appears frustrated (score: -0.6). Consider acknowledging the issue."}]}
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```
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Positive sentiment:
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```json
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{"type": "ingest_result", "memories": [{"content": "[sentiment] User seems satisfied (score: 0.7). Positive interaction."}]}
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```
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Neutral sentiment returns an empty memories list.
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## Usage
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Installed automatically when enabled in agent configuration. No external dependencies required (stdlib only).
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