القائمة
Predictive maintenance scheduling skill using telematics data and historical patterns to maximize fleet uptime
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/a5c-ai/babysitter --skill predictive-maintenance-schedulerيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
Reference for querying the Atlas knowledge graph through its MCP tools — the SECONDARY enrichment/comparison layer that adds best-practice context to systems you have ALREADY scanned from your real sources (`az`, repos, dirs). Use when you need to look up nodes, edges, kinds, clusters, stats, or wiki pages in Atlas to compare against your real inventory. (atlas graph, query atlas, atlas mcp, search the graph, graph neighbors, atlas record, atlas kinds, enrichment layer)
Atlas turns your STATED NEED into a real systems atlas by SCANNING your actual sources (Azure via `az`, git repos, local dirs) and process/data mining them, THEN enriching against the Atlas knowledge graph. Use this skill when asked to inventory/map your real systems, scan your cloud + repos + directories, mine the real processes or data they contain, or collect their real constraints/gotchas. (atlas, scan my systems, inventory our azure account, map my repos, real systems atlas, process mining, data mining, collect nuances, system discovery)
This skill should be used when the user asks to "find skills in the wild", "assimilate popular workflows", "discover SKILL.md files in repos", "research external skills", "find workflow patterns", "survey the skill landscape", "what skills exist out there", or wants to investigate public repositories for extractable processes, babysitter plugins, and reusable procedural insights. Searches GitHub for SKILL.md files, classifies repos by archetype, and maintains structured research under docs/reference-repos/.
عرض SKILL.md
استنادا إلى تصنيف SOC المهني
| name | predictive-maintenance-scheduler |
| description | Predictive maintenance scheduling skill using telematics data and historical patterns to maximize fleet uptime |
| allowed-tools | ["Read","Write","Glob","Grep","Bash","WebFetch"] |
| metadata | {"specialization":"logistics","domain":"business","category":"fleet-management","priority":"lower"} |
| graph | {"domains":["domain:logistics"],"skillAreas":["skill-area:procurement-management","skill-area:statistical-analysis","skill-area:data-analytics"],"roles":["role:supply-chain-analyst","role:operations-analyst","role:procurement-manager"]} |
The Predictive Maintenance Scheduler uses telematics data and historical patterns to predict equipment failures and schedule maintenance proactively. It maximizes fleet uptime, reduces unplanned breakdowns, and optimizes maintenance costs through data-driven scheduling and parts inventory management.
skill: predictive-maintenance-scheduler
inputs:
vehicle:
vehicle_id: "VH001"
make: "Freightliner"
model: "Cascadia"
year: 2022
odometer_miles: 125000
engine_hours: 4500
telematics_data:
engine_temperature_avg: 195
oil_pressure_psi: 42
brake_wear_percent: 65
tire_tread_depth_mm: [8, 7, 9, 8]
fault_codes: ["P0171"]
fuel_efficiency_mpg: 6.8
maintenance_history:
- service_type: "oil_change"
date: "2025-11-15"
odometer: 115000
- service_type: "brake_inspection"
date: "2025-10-01"
odometer: 108000
operational_schedule:
daily_miles: 350
days_per_week: 5
outputs:
maintenance_predictions: