用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt;运行前请先检查来源。
npx skills add https://github.com/a5c-ai/babysitter --skill supplier-scorecard-engine命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地?可下载 SkillsMP 当前能够提供的文件。
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/.
正在显示 SKILL.md
基于 SOC 职业分类
| name | supplier-scorecard-engine |
| description | Automated supplier performance scorecard generation with KPI tracking and trend analysis |
| allowed-tools | ["Read","Write","Glob","Grep","Bash"] |
| metadata | {"specialization":"supply-chain","domain":"business","category":"supplier-management","priority":"high"} |
| graph | {"domains":["domain:supply-chain"],"specializations":["specialization:supply-chain-optimization"],"skillAreas":["skill-area:procurement-management","skill-area:vendor-management-ops","skill-area:data-analytics"],"workflows":["workflow:vendor-onboarding","workflow:vendor-evaluation"],"roles":["role:supply-chain-analyst","role:procurement-manager","role:data-analyst"]} |
The Supplier Scorecard Engine automates the generation and maintenance of supplier performance scorecards. It aggregates performance data across multiple KPI categories, calculates weighted scores, tracks trends, and generates actionable insights for supplier management.
scorecard_request:
supplier_id: string
evaluation_period:
start_date: date
end_date: date
performance_data:
delivery:
orders_received: integer
on_time: integer
in_full: integer
quality:
units_received: integer
defects: integer
returns: integer
cost:
contracted_spend: float
actual_spend: float
savings_target: float
responsiveness:
issues_raised: integer
issues_resolved: integer
avg_resolution_time: float
sustainability:
certifications: array
esg_score: float
weighting_profile: object
benchmark_data: object
scorecard_output:
supplier_id: string
period: object
category_scores:
delivery:
otif_percent: float
score: float
trend: string
quality:
ppm: float
score: float
trend: string
cost:
variance_percent: float
score: float
trend: string
responsiveness:
resolution_rate: float
score: float
trend: string
sustainability:
score: float
trend: string
composite_score: float
rating: string # A, B, C, D, F
benchmark_comparison: object
action_items: array
trend_analysis: object
Input: Previous month's delivery, quality, cost data
Process: Calculate KPIs, apply weights, generate score
Output: Comprehensive supplier scorecard with rating
Input: 12 months of scorecard history
Process: Analyze performance trajectory by category
Output: Trend report with improvement/decline identification
Input: Supplier scorecard, peer group data
Process: Compare against category averages and best-in-class
Output: Relative performance positioning