القائمة
Statistical safety stock calculation skill with service level targeting and variability analysis
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 safety-stock-calculatorيبقى 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 | safety-stock-calculator |
| description | Statistical safety stock calculation skill with service level targeting and variability analysis |
| allowed-tools | ["Read","Write","Glob","Grep","Bash"] |
| metadata | {"specialization":"supply-chain","domain":"business","category":"inventory","priority":"medium"} |
| graph | {"domains":["domain:supply-chain"],"specializations":["specialization:supply-chain-optimization"],"skillAreas":["skill-area:procurement-management","skill-area:statistical-analysis","skill-area:quantitative-modeling"],"workflows":["workflow:vendor-onboarding","workflow:vendor-evaluation"],"roles":["role:supply-chain-analyst","role:operations-analyst","role:procurement-manager"]} |
The Safety Stock Calculator provides statistical methods for determining optimal safety stock levels. It analyzes demand and lead time variability, converts service level requirements, and calculates appropriate buffer stocks to meet customer service targets while optimizing working capital.
safety_stock_request:
items: array
- sku_id: string
demand_history: array
lead_time: object
average: float
standard_deviation: float
review_period: integer
unit_cost: float
service_level_targets:
target_type: string # fill_rate, cycle_service_level
target_value: float # e.g., 0.95 for 95%
calculation_method: string # standard, periodic_review, simulation
simulation_iterations: integer # For Monte Carlo method
safety_stock_output:
calculations: array
- sku_id: string
demand_stats:
mean: float
std_dev: float
cov: float
lead_time_stats:
mean: float
std_dev: float
safety_stock_units: integer
safety_stock_days: float
service_level_achieved: float
investment_value: float
summary:
total_safety_stock_investment: float
average_days_coverage: float
service_level_distribution: object
recommendations: array
Input: Demand history, lead time, 95% service level target
Process: Calculate demand and LT variability, apply formula
Output: Safety stock in units and days of supply
Input: Complex demand patterns, variable lead times
Process: Simulate 10,000 demand-supply scenarios
Output: Simulation-based safety stock with confidence interval
Input: ABC/XYZ segmented portfolio
Process: Apply differentiated service levels by segment
Output: Tiered safety stock policy with investment optimization