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 media-mix-modeling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 | media-mix-modeling |
| description | Advanced econometric modeling for marketing effectiveness and budget optimization |
| allowed-tools | ["Read","Write","Glob","Grep","Bash","WebFetch"] |
| metadata | {"specialization":"marketing","domain":"business","category":"Marketing Analytics","skill-id":"SK-019"} |
| graph | {"domains":["domain:marketing"],"skillAreas":["skill-area:brand-strategy","skill-area:brand-positioning","skill-area:content-marketing"],"workflows":["workflow:brand-campaign-launch"],"roles":["role:marketing-manager","role:marketing-strategist","role:brand-manager"]} |
The Media Mix Modeling Skill provides advanced econometric modeling capabilities for measuring marketing effectiveness and optimizing budget allocation. This skill enables marketing mix model development, channel contribution analysis, saturation curve modeling, and scenario planning using statistical techniques and machine learning approaches including Google Lightweight MMM and custom Python/R implementations.
This skill integrates with the following marketing processes:
skill: media-mix-modeling
action: build-model
parameters:
model_type: bayesian_mmm
framework: lightweight_mmm
data_configuration:
date_column: week
target_variable: revenue
media_variables:
- tv_spend
- digital_display_spend
- paid_search_spend
- paid_social_spend
- radio_spend
control_variables:
- price_index
- competitor_spend
- economic_indicator
- seasonality_index
model_settings:
adstock:
type: geometric
max_lag: 8
saturation:
type: hill
priors:
type: informative
source: prior_mmm_results
validation:
holdout_weeks: 12
cross_validation_folds: 5
skill: media-mix-modeling
action: analyze-contributions
parameters:
model_id: "mmm_2024_q4"
analysis_period:
start_date: "2024-01-01"
end_date: "2024-12-31"
outputs:
- type: contribution_breakdown
format: waterfall_chart
- type: channel_roi
format: bar_chart
- type: contribution_over_time
format: stacked_area
- type: marginal_contribution
format: line_chart
export:
format: [pdf, csv, xlsx]
destination: "reports/mmm_contributions"
skill: media-mix-modeling
action: optimize-budget
parameters:
model_id: "mmm_2024_q4"
optimization_settings:
objective: maximize_revenue
total_budget: 10000000
constraints:
- channel: tv_spend
min_percent: 0.20
max_percent: 0.40
- channel: paid_search_spend
min_percent: 0.15
max_percent: 0.30
- channel: paid_social_spend
min_percent: 0.10
max_percent: 0.25
business_rules:
- type: minimum_presence
channels: [tv, digital_display]
- type: maximum_concentration
single_channel_cap: 0.50
scenarios:
- name: "optimal_allocation"
constraints: default
- name:
skill: media-mix-modeling
action: run-scenarios
parameters:
model_id: "mmm_2024_q4"
scenarios:
- name: "Budget Cut 20%"
budget_change: -0.20
allocation: optimized
- name: "Budget Increase 30%"
budget_change: 0.30
allocation: optimized
- name: "TV Elimination"
channel_changes:
tv_spend: 0
reallocate: true
- name: "New Channel Test"
new_channels:
- name: connected_tv
estimated_roi: 2.5
test_budget: 500000
- name: "Q1 Seasonal Plan"
period: "2025-01-01 to 2025-03-31"
seasonality_adjustment: true
comparison_metrics:
- total_revenue
- incremental_revenue
-
skill: media-mix-modeling
action: analyze-saturation
parameters:
model_id: "mmm_2024_q4"
channels:
- tv_spend
- paid_search_spend
- paid_social_spend
analysis:
- type: response_curves
spend_range: [0, 2x_current]
granularity: 100_points
- type: optimal_spend
threshold: 0.95_saturation
- type: marginal_roi_curve
spend_range: [0.5x_current, 1.5x_current]
visualization:
charts:
- response_curves_overlay
- marginal_roi_comparison
- saturation_heatmap