learn-timeout
General-purpose timeout estimation skill. Trains dual models (duration regression + risk classification) from corpus data and observation feedback. Returns calibrated timeout predictions with confidence intervals for any task type.
Quellinformationen
- Repository
- grahama1970/agent-stack-public
- Letzte Quellaktivität
- 24. September 2026 um 15:51
- Erkannte Sprache von SKILL.md
- Englisch
- Sterne
- 0
- Forks
- 0
Installationsoptionen
Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.
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Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
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SKILL.md
Quellanweisungen · Schreibgeschützte Vorschau- name
- learn-timeout
- triggers
- ["learn-timeout","timeout prediction","estimate timeout","predict timeout","timeout model"]
- description
- General-purpose timeout estimation skill. Trains dual models (duration regression + risk classification) from corpus data and observation feedback. Returns calibrated timeout predictions with confidence intervals for any task type.
- provides
- ["learn-timeout"]
- composes
- ["task-monitor","agentic-evals"]
- disciplines
- ["ml-training","observability-operations"]
# Learn Timeout
General-purpose timeout estimation that replaces fragmented Ridge/Logistic models with a unified GradientBoosting-based predictor.
## Commands
```bash
./run.sh collect # Gather training data from all sources
./run.sh train # Train both models
./run.sh predict '{"task_type":"pdf_extraction","page_count":400}'
./run.sh observe --task-id X --actual-seconds Y
./run.sh status # Model health dashboard
./run.sh benchmark # Classifier-lab backbone comparison
```
## Prediction Output
```json
{
"estimated_seconds": 4200,
"confidence_interval": [2800, 6300],
"risk_probability": 0.35,
"risk_label": "medium",
"recommended_timeout_seconds": 6300,
"features_used": ["page_count", "table_pages", "domain"],
"model_version": "2026-02-13_v1",
"duration_model_available": true,
"risk_model_available": true
}
```
## Task Types
| task_type | Key Features |
|-----------|-------------|
| `pdf_extraction` | page_count, tables, figures, file_size, domain |
| `llm_api_call` | prompt_tokens, model, provider, image_count |
| `subprocess` | command_type, input_size, complexity_hints |
| `remediation` | issue_count, issue_severity, skill_name |
## Training Data Sources
- Corpus `profile.json` + `timings.jsonl` (S00 features + actual durations)
- Supervisor run logs (`extract_timeout` events)
- Aggregate reports (extraction timing events)
- Observation feedback loop (`data/observations.jsonl`)
Auf GitHub ansehen