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.
معلومات المصدر
- المستودع
- grahama1970/agent-stack-public
- آخر نشاط في المصدر
- ٢٤ سبتمبر ٢٠٢٦ في ١٥:٥١
- لغة SKILL.md المكتشفة
- الإنجليزية
- النجوم
- ٠
- التفرعات
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خيارات التثبيت
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
مراجعة ملفات المصدر
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
مستكشف الملفات
14 ملفاتعرض SKILL.md
SKILL.md
تعليمات المصدر · معاينة للقراءة فقط- 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`)
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